|  A Comprehensive Review of Emerging Approaches in Machine Learning for De
Novo PROTAC Design
---|---
|  Yossra Gharbi and Rocío Mercado
|  Targeted protein degradation (TPD) is a rapidly growing field in modern
drug discovery that aims to regulate the intracellular levels of proteins by
harnessing the cell’s innate degradation pathways to selectively target and
degrade disease-related proteins. This strategy creates new opportunities for
therapeutic intervention in cases where occupancy-based inhibitors have not
been successful. Proteolysis-targeting chimeras (PROTACs) are at the heart of
TPD strategies, which leverage the ubiquitin-proteasome system for the
selective targeting and proteasomal degradation of pathogenic proteins. This
unique mechanism can be particularly useful for dealing with proteins that
were once deemed “undruggable” using conventional small-molecule drugs.
PROTACs are hetero-bifunctional molecules consisting of two ligands, connected
by a chemical linker. As the field evolves, it becomes increasingly apparent
that the traditional methodologies for designing such complex molecules have
limitations. This has led to the use of machine learning (ML) and generative
modeling to improve and accelerate the development process. In this review, we
aim to provide a thorough exploration of the impact of ML on de novo PROTAC
design — an aspect of molecular design that has not been comprehensively
reviewed despite its significance. Initially, we delve into the distinct
characteristics of PROTAC linker design, underscoring the complexities
required to create effective bifunctional molecules capable of TPD. We then
examine how ML in the context of fragment-based drug design (FBDD), honed in
the realm of small-molecule drug discovery, is paving the way for PROTAC
linker design. Our review provides a critical evaluation of the limitations
inherent in applying this method to the complex field of PROTAC development.
Moreover, we review existing ML works applied to PROTAC design, highlighting
pioneering efforts and, importantly, the limitations these studies face. By
offering insights into the current state of PROTAC development and the
integral role of ML in PROTAC design, we aim to provide valuable perspectives
for biologists, chemists, and ML practitioners alike in their pursuit of
better design strategies for this new modality.

##

††footnotetext: Department of Computer Science and Engineering, Section for
Data Science and AI, Chalmers University of Technology, Chalmersplatsen 4, 412
96 Gothenburg, Sweden. E-mail<EMAIL_ADDRESS><EMAIL_ADDRESS>
##  1 Introduction

Targeted protein degradation (TPD) is a novel therapeutic approach with
attractive potential to eliminate disease-causing proteins from within
cells.1, 2, 3, 4 Traditional drug development strategies have focused on
inhibiting the activity of such proteins, but TPD goes a step further by
removing or reducing protein levels from the cell. This is particularly useful
for targeting proteins that are difficult to inhibit with small molecules or
biologics, often due to the absence of well-defined binding sites; these are
frequently referred to as “undruggable” targets, and they can be challenging
to target due to their structure, location, and/or function. 5, 6 Proteolysis-
targeting chimeras (PROTACs) are hetero-bifunctional molecules engineered to
bind simultaneously to an E3 ligase, a key enzyme involved in the process of
tagging proteins for degradation7, and the protein of interest (POI) that is
targeted for degradation (Figure 1a). 8, 9, 10 A PROTAC molecule brings the E3
ligase and the POI into close proximity, facilitating the formation of a
ternary complex consisting of the E3 ligase system, the PROTAC, and the POI to
induce POI ubiquitination and its subsequent degradation by the proteasome
(Figure 1b). 11

PROTACs were first reported in 2001, when the first fully synthesized PROTAC,
named Protac-1, was developed by Crews, Deshaies, and co-workers.12 This
provided an in vitro proof-of-concept, which proved the feasibility of
designing molecules with the potential to selectively target and degrade
cellular proteins by hijacking the ubiquitin proteasome system (UPS). Protac-1
was specifically designed to target the methionyl aminopeptidase 2 (MetAP-2)
protein, which plays a role in angiogenesis and various other pathologies,
including cancer. Protac-1 was designed to target MetAP-2, as ovalicin and
fumagillin do, but with the added mechanism of promoting its degradation. The
binding of Protac-1 to MetAP-2 led to the tethering of MetAP-2 to beta-
transducin repeat-containing protein ($\beta$-TrCP), functioning as an E3
ubiquitin ligase responsible for ubiquitination of MetAP-2. The effectiveness
of Protac-1 in facilitating the ubiquitination of MetAP-2 was demonstrated
using extracts from unfertilized Xenopus laevis eggs, a common model organism
in biomedical research. 13 These extracts provided a controlled environment
rich in cellular machinery that mimics the conditions inside a living cell
needed for ubiquitination, protein degradation, and observing the interaction
between Protac-1 and MetAP-2. Results showed that MetAP-2 selectively binds
the angiogenesis inhibitor ovalicin moiety of Protac-1, and that Protac-1 can
mediate the ubiquitination of MetAP-2 by $\beta$-TrCP, leading to its
degradation. 12

In 2003, the same group synthesized a PROTAC using estradiol, a form of
estrogen, as part of its structure.14 This PROTAC was designed to target and
promote the destruction of the estrogen receptor alpha (ER$\alpha$), which,
when activated by estrogen, can promote the growth of some breast cancers.15
It has been shown that the estradiol-based PROTAC could effectively enforce
the ubiquitination and subsequent degradation of the $\alpha$ isoform of ER in
vitro. 14 Similarly, they created a PROTAC that incorporates
dihydrotestosterone (DHT) to target and degrade the androgen receptor (AR).
When activated by androgens like DHT, the AR can stimulate the growth of
prostate cancer cells.16 The DHT-based PROTAC has shown efficacy in promoting
the rapid ubiquitination and proteasome-dependent degradation of AR in
cellular tests. 14 These PROTACs served as proof that they are a promising
modality for selectively degrading key proteins involved in cancer, opening up
potential treatment benefits by TPD in hormone-responsive cancers.17, 18

While first-generation PROTACs were capable of degrading target proteins, they
suffered from poor cell permeability and chemical stability stemming from
their high molecular weight. 19 They generally exhibited low potency using
micromolar (µM) concentrations, which is less desirable than the nanomolar
concentrations used with more potent drugs, indicating that higher doses are
required to exhibit efficacy. 1 Notably, early PROTACs were peptide-based and
commonly used $\beta$-TrCP or Von Hippel-Lindau (VHL) as E3 ligases. One
significant drawback of peptide-based therapeutics is their high molecular
weight, which affects their ability to cross cell membranes. This poor
permeability is a critical limitation because it means that even if a PROTAC
is theoretically effective, its inability to enter cells renders them
ineffective in practice. 1 These limitations promoted the need to develop
second-generation PROTACs, motivating a transition from peptide-based to
small-molecule PROTACs. The use of small molecules expanded the range of
potentially targetable proteins by taking advantage of a more extensive array
of E3 ligases beyond $\beta$-TrCP and VHL, such as mouse double minute 2
homologue (MDM2), inhibitors of apoptosis proteins (IAPs), and cereblon
(CRBN). 19 In 2008, the Crews lab developed the first small-molecule PROTAC
that could degrade a target protein within cells, in this case, targeting AR.
20 This PROTAC was composed of nutlin-3A, a ligand for MDM2, and a non-
steroidal androgen receptor ligand (SARM) for AR, connected by a polyethylene
glycol (PEG) linker.20 The SARM-nutlin PROTAC induced the degradation of AR in
a proteasome-dependent manner with enhanced cell penetration in vitro.

In some of the latest generations of PROTACs, additional elements have been
introduced to give another dimension of control over PROTAC activity. 21, 22
These classes of controllable PROTACs aim to address off-tissue effects by
controlling PROTAC action in a spatiotemporal manner. 22 Some are designed to
be activated or deactivated by specific wavelengths of light, allowing for
controlled degradation processes in target cells, with potentially reduced
side effects and enhanced therapeutic index. These PROTACs include phospho-
dependent PROTACs that degrade targets with activated kinase-signaling clues,
and light-controllable PROTACs that use light as an external clue to trigger
target degradation. Notable light-controllable PROTACs, also commonly referred
to as PHOTACs, include photo-caged and photo-switchable PROTACs.23 Photo-caged
PROTACs are designed to be inactive in their initial form and activated by
light exposure, which removes the photo-cage group and enables the degradation
of the POI. Photo-switchable PROTACs, on the other hand, are designed to
reversibly control the degradation process via the incorporation of
photoswitchable groups such as azobenzene, which can switch between active and
inactive states under different wavelengths of light. In-cell click-formed
proteolysis-targeting chimeras (CLIPTACs) share similar ambitions to PHOTACs
and have been used to degrade two key oncology targets successfully.24 The
reader is referred to these excellent reviews for a more detailed analysis of
milestones in PROTAC development.1, 25, 19, 22

Since the first PROTAC was reported in the literature, the field of PROTACs
has experienced remarkable growth26, 3 and has led to the design of compounds
with improved drug-like properties demonstrating effectiveness both in vitro
and in vivo.27, 28, 29, 30 The first PROTACs to enter clinical trials were
ARV-11031, 32 and ARV-47133, which target AR and ER, respectively. ARV-110 was
tested in a heavily pre-treated population with metastatic castration-
resistant prostate cancer (mCRPC). Results from a phase I trial showed that
ARV-110 could reduce the levels of AR in cancer cells by at least 95%, which
is a significant reduction that hampers the cancer cell’s ability to grow and
survive. Notably, its effectiveness in ENZ-resistant models offers a potential
treatment option for patients who no longer respond to ENZ, addressing a
critical gap in prostate cancer therapy. ARV-110 advanced to phase II clinical
trials in 2020 based on initial phase I data that demonstrated the drug’s good
oral availability, safety, and tolerability in patients34. On the other hand,
ARV-471 is designed for oral administration in patients with hormone receptor-
positive (HR+) and HER2-negative metastatic breast cancer. In a phase I
clinical study involving breast cancer patients who had undergone multiple
prior treatments, ARV-471 significantly reduced the expression level of ER in
tumor tissues of patients. It was also reported that ARV-471 is well tolerated
across all tested doses (30-700 mg), and maintained a high level of ER
degradation (89%). 10

Following the lead of ARV-110, thousands of PROTACs have been developed to
degrade kinases, nuclear receptors, transcription factors, regulatory
proteins, etc. Some of the most common targets include the bromodomain and
extra-terminal domain (BET) family, AR, ER, bruton’s tyrosine kinase (BTK),
anaplastic lymphoma kinase (ALK), mitogen-activated protein kinase kinase
(MEK), the BCR-ABL fusion gene, and the epidermal growth factor receptor
(EGFR).22. Most of these are well-known targets for cancer and inflammatory
disorders. PROTACs are developing rapidly and have already been used to target
and degrade a variety of proteins associated with a wide range of diseases,
including cancer, immune disorders, neurodegenerative diseases, cardiovascular
conditions, and viral infections. 35 This rapid progress highlights the
utility of PROTACs in treating a wide range of health conditions via TPD.

PROTACs work by ingeniously harnessing the ubiquitin-proteasome system (UPS),
an important cellular pathway, which naturally degrades over 80% of cellular
proteins to regulate protein levels and turnover.36 The UPS selectively
targets misfolded and damaged proteins within the cell for degradation,
maintaining proper protein homeostasis. 37 However, if this system falters
such that old, damaged, or surplus proteins are not promptly degraded, they
can form aggregates resistant to degradation. These aggregates can interfere
with cellular functions and are the hallmark of several neurodegenerative
diseases such as Alzheimer’s, Parkinson’s, and Huntington’s diseases.38, 39,
40, 41 Moreover, a recent multi-omics study of $>$9,000 human tumors and 33
cancer types found that $>$19% of all cancer driver genes impact UPS
function.42 The UPS operates by tagging target proteins for degradation
through the attachment of ubiquitin (Ub) protein chains. These Ub tags mark
the protein for degradation by the proteasome, a protein complex responsible
for protein degradation via proteolysis.43 The process of Ub conjugation
involves an enzyme cascade, starting with E1 activating enzymes, proceeding to
E2 conjugating enzymes, and culminating with E3 ligases.44 Initially, Ub is
activated by an E1 enzyme, a reaction that requires adenosine triphosphate and
results in an E1-Ub conjugate. The activated Ub is then transferred to an E2
enzyme through a transthioesterification reaction, forming an E2-Ub complex.
The most crucial step is mediated by the E3 ubiquitin ligase, which confers
specificity to the ubiquitination process. 45

E3 ubiquitin ligases are categorized into two main types based on their
mechanism of action (MoA) for transferring Ub’s to their target proteins:
HECT-domain and RING-type E3 ligases. HECT-domain E3 ligases first form a
thioester bond with Ub. This means that Ub is temporarily attached to the E3
ligase itself. Subsequently, the E3 ligase transfers the Ub from itself
directly onto the substrate protein that is to be tagged for degradation. 46
Unlike HECT-domain ligases, RING E3 ligases do not form a direct bond with Ub.
Instead, they facilitate the transfer of Ub directly from an E2 enzyme (which
is conjugated with Ub) to the substrate protein. 47 In essence, RING-type E3
ligases act as mediators that bring the E2–Ub conjugate close to the
substrate, enabling the direct transfer of Ub. This ubiquitination cycle
repeats, leading to the transfer of multiple Ub’s and the polyubiquitination
of the substrate. Once a protein is polyubiquitinated, it is tagged for
degradation. The proteasome recognizes the tagged protein, binds to it,
unfolds it, and breaks it down into smaller peptides.48 The PROTAC is then
recycled for additional ubiquitination rounds of additional substrates. 49

The UPS’s sophisticated mechanism forms the basis for PROTAC structure design.
PROTACs are bifunctional molecules, designed to harness the UPS for TPD. Each
PROTAC comprises three key components: a ligand that binds to the POI, also
frequently referred to as the “warhead;” a ligand that recruits an E3
ubiquitin ligase; and an organic linker that connects these two ligands. This
dual engagement enables PROTACs to bring the POI and E3 ligase into proximity,
forming a ternary complex that facilitates the transfer of Ub from the E3
ligase to the target protein. 11, 50, 51 Notably, the linker connecting these
moieties is not merely a passive scaffold; it has been shown to play a vital
role in determining the overall efficacy and specificity of the PROTAC
molecule. 52, 53, 54, 55, 56 It ensures efficient ubiquitination by correctly
positioning the two ligands. This can be achieved by carefully designing the
linker length and composition to maintain the required distance, flexibility
(or rigidity), and spatial orientation between the POI and the E3 ligase. 57,
58, 10 Additionally, linker modification can affect properties like hydrogen
bond donors (HBDs) and acceptors (HBAs), lipophilicity, molecular weight,
rotatable bonds, and polar surface area, which are all critical factors in
absorption, distribution, metabolism, and excretion (ADME). 59 Improving these
properties can make PROTACs function better as drugs. A study on BET degraders
provides a notable example,60 where researchers replaced an amide bond in BET
degraders MZ1 and ARV-771 with an ester group. This change removed one HBD and
increased the lipophilicity for each molecule, leading to increased cell
membrane permeability. This was reflected in improvements in parallel
artificial membrane permeability assay (PAMPA) and ALogP values, among other
measurements. Besides being able to enter cells more easily, each molecule’s
degradation activity also improved after this otherwise “small” change to the
linker.

Fig. 1: (a) A PROTAC is a hetero-bifunctional molecule, consisting of a ligand
(blue triangle) that recruits an E3 ubiquitin ligase, a warhead (orange
circle) that binds to the POI, and a linker (blue curve) that connects the two
binding moieties. The PROTAC functions by simultaneously binding to the POI
and the E3 ligase, thus bringing them into close proximity and inducing the
formation of a ternary complex. (b) The PROTAC MoA begins with an E1
ubiquitin-activating enzyme that activates ubiquitin (Ub) in an ATP-dependent
manner. This activated Ub is then transferred to an E2 Ub-conjugating enzyme.
Subsequently, a PROTAC simultaneously binds to the POI and an E3 ubiquitin
ligase, bringing them into close proximity. This facilitates the transfer of
Ub from the E2 enzyme to the POI, catalyzed by the E3 ligase. The
polyubiquitinated POI is then recognized and degraded by the proteasome into
smaller peptides, and the PROTAC is released back into the cellular
environment where it can be reused, initiating the process again with another
instance of the same POI. (c) Visual representations of dBET6 and its
respective ternary complex: left – a 2D skeletal formula of the PROTAC
molecule dBET6; middle – a close-up of the dBET6 degrader’s three-dimensional
(3D) structure in complex with CRBN and BRD4 (PDBID:6BOY), emphasizing the
importance of the PROTAC’s spatial orientation in forming a good ternary
complex; and right – a space filling model for the same complex, involving
BRD4, CRBN, DNA damage-binding protein 1 (DDB1), and dBET6. Color key: BRD4
(green), CRBN (cyan), and DDB1 (dark blue).

.

While small-molecule drugs (SMDs) have demonstrated success in treating
various diseases,61, 62, 63 PROTACs can target and degrade proteins regardless
of their function by hijacking the cell’s natural disposal mechanism – the
ubiquitin-proteasome pathway. 64 This approach circumvents the inherent
limitations of standard SMDs, which must occupy specific binding sites on
target proteins. 3 Notably, $\sim$85% of the human proteome has been deemed
“undruggable” by SMDs;65, 64, 66, 67 these proteins typically lack well-
defined binding pockets, offering limited opportunities for ligand interaction
or binding.3, 68 For instance, 63% of the known 600 cancer-related proteins
are classified as “undruggable”, including transcription factors, scaffold
proteins, and membrane-bound proteins. 69 While SMDs are constrained to a
limited pool of proteins that can be effectively targeted, PROTACs don’t
necessarily require binding to a specific well-defined pocket on a POI to
trigger degradation; in theory, they can bind to any reachable region on a
POI’s surface that facilitates induced proximity between the POI and an E3
ligase, even in the case of low binding affinities with the POI. 1, 70 This
flexibility in target engagement expands the scope of the proteome that can be
drugged with this modality, and is a mechanism that can be particularly
pertinent in cancer treatments, where target proteins often develop resistance
to SMDs through mechanisms like genetic mutations, overexpression, or altered
signaling pathways. PROTACs offer a potential alternative capable of
overcoming these resistance barriers.71, 72, 73, 74, 75

Furthermore, a key advantage of PROTACs lies in their substoichiometric
catalytic activity, which operates on an event-driven basis. 76 This means
that PROTACs do not need to fully occupy their target proteins to be
effective, in contrast to traditional inhibitors that function in an
occupancy-driven manner. 77 In SMDs, the effectiveness of the drug is often
dependent on stoichiometrically occupying the target binding site. 78 This
means that a significant portion of a target protein must be bound by an
inhibitor molecule for the desired therapeutic effect to be observed. This
often requires relatively high concentrations of the drug to achieve
sufficient occupancy, since the effects are proportional to the extent of
binding. 78 PROTACs operate differently: they bind transiently to their
targets and, after facilitating ubiquitination, dissociate. This allows them
to cycle through multiple rounds of activity, repeatedly initiating the
degradation of additional instances of the same POI. 1 In contrast to SMDs
that act in a dose-dependent manner, this catalytic feature allows PROTACs to
achieve potent effects at possibly lower doses, offering potential advantages
in terms of efficacy, safety, negative side effects, and off-target effects.
79

One final advantageous characteristic of PROTACs worth mentioning is that they
are able to selectively target and induce the degradation of specific protein
isoforms. These are distinct forms of the same protein arising from a single
gene. The ability to selectively target them is significant because it implies
that PROTACs can be used to differentiate between closely related forms of a
protein and target only the isoform(s) associated with a disease without
affecting others that may have essential functions in normal cellular
processes. 76, 6, 69

These attractive characteristics make PROTACs a prime focus of drug design
endeavors. To maximize the potential of this innovative class of compounds,
researchers are increasingly turning to data-driven approaches for design
strategies. Machine learning (ML) has thus demonstrably advanced drug
discovery and development by enhancing target identification, small-molecule
design, predictive biomarker discovery, and the prediction of clinical trial
success.80, 81 ML methods can help researchers analyze large amounts of data
to identify potential drug targets, optimize compound properties, and predict
how patients will respond to treatments. This makes drug development more
efficient and increases the likelihood of success in vitro. 80, 81 Given the
complexity of designing PROTACs due to the large chemical space they span and
their multivalent nature, leveraging ML will likely be crucial in making the
development of this new modality more feasible. Despite numerous reviews on
PROTACs, there is a notable gap in the literature: an in-depth review that
delves into the use of ML for PROTAC design is still lacking. In this
comprehensive literature review, we explore the impact of ML on de novo PROTAC
design to date. First, we delve into the distinct characteristics of PROTAC
linker design, underscoring the features required to create effective
bifunctional molecules capable of TPD. We then examine how ML in the context
of fragment-based drug discovery (FBDD; Figure 2a), honed for small-molecule
drug discovery, is paving the way for PROTAC linker design. Our review
provides a critical assessment of the obstacles inherent in the application of
these methods to PROTAC development. This assessment seeks to shed light on
the pressing need for specialized algorithms, enhanced data quality, and the
adaptation of ML models to address the multifaceted nature of PROTAC
engineering. Moreover, we review existing ML works that have been tailored to
PROTAC design, highlighting pioneering efforts as well as the limitations
associated with these existing approaches. We also offer perspectives on
potential avenues for future ML research in this field.

##  2 Machine learning in PROTAC linker design

###  2.1 The peculiarities of PROTAC linker design

The role the linker plays in PROTAC function is both unique and complex,
offering the broadest scope in terms of where structural modifications can be
made when designing a PROTAC (Figure 2b). 52, 53, 54, 55, 56, 82 Unlike the
linker, the structures of the ligands that bind the POI and E3 ligase are
generally more restricted. These restrictions stem from the need to maintain
specific structure-activity relationships (SAR) and effective target binding,
limiting the options for modifying the warhead and E3 ligase ligand according
to their functional requirements. 82 Consequently, the linker becomes a
primary focus for design optimization in PROTACs. Modifications to the linker,
such as altering its length, tuning its flexibility or rigidity, and
incorporating different chemical groups, can influence the pharmacokinetic
(PK) and pharmacodynamic (PD) profiles of PROTACs, as well as their
degradation activity and overall efficacy. 82, 83, 84 Notably, the geometry
(conformation) of the ternary complex is heavily influenced by the nature of
the linker. 50, 85 Evidently, the linker not only dictates the spatial
arrangement necessary for successful TPD in a given system but also the
efficiency with which the PROTAC can facilitate the degradation of the POI.
86, 84 This is often quantified by metrics like the DC50, the concentration at
which half-maximal degradation is observed, and D${}_{\text{max}}$, the
maximum level of degradation achievable. This implies that PROTAC linker
design requires a multifaceted approach that balances several key properties
to ensure the successful degradation of a POI while maintaining a desirable
drug-like profile. In this section, we present example case studies that
highlight the unique aspects of linker design in PROTACs.

Linker length. The conformation of the ternary complex is heavily dependent on
the linker length within PROTACs. A long linker can lead to the formation of
no functional complex since the ubiquitination of the POI might not occur. A
short linker might result in what’s called a binary complex where the PROTAC
is only effectively linked to either the POI or the E3 ligase, but not both.
87 Similar results were observed by Qin _et al._ 88, where the potency of
PROTACs targeting BET proteins to inhibit cell growth was highly dependent on
the linker length. This was demonstrated via a series of five PROTACs with
progressively longer linkers. Notably, the authors observed an optimal linker
length; further extending the linker beyond this optimal length did not
enhance the potency. This shows that a very long linker might not provide
additional benefits, and could inadvertently introduce steric constraints
leading to decreased binding affinity. The most effective PROTAC from this
study, QCA570, was tested in xenograft mouse models of leukemia, where it
induced complete and durable tumor regression at low picomolar concentrations.

Furthermore, the length of the linker affects the range of spatial
configurations accessible to potential ternary complexes during formation,
restricting which protein interfaces are accessible for interaction. Smith _et
al._ 55 demonstrated how differences in linker lengths and attachment points
enable selective degradation of closely related kinase isoforms using PROTACs.
The study developed isoform-selective PROTACs for the p38 mitogen-activated
protein kinase (MAPK) family using the same warhead and E3 ligase but varying
the linker features (linker attachment points and lengths). Two different
linker attachment points (an amide and phenyl series) and varying linker
lengths (10, 11, 12, and 13 atoms) were used to create distinct PROTACs that
differentially recruit VHL. This selective recruitment controls the
degradation of either the p38$\alpha$ or p38$\delta$ isoforms. For instance,
PROTACs with 12- and 13-atom linkers in the amide series became highly
selective for p38$\alpha$ degradation, showing much higher degradation
efficacy compared to degraders with shorter linkers which were also less
selective. Conversely, a 10-atom linker in the phenyl series led to selective
degradation of p38$\delta$, with very minimal impact on other isoforms. This
selective degradation ability is attributed to how variations in linker
lengths and attachment points influence the formation of the ternary complex.
By fine-tuning the linkers, PROTACs can achieve selective degradation profiles
– in this particular study, shorter linkers may bring the E3 ligase into a
position that is optimal for ubiquitinating p38$\delta$ but not p38$\alpha$.

  * •

Linker length is an important factor in determining the spatial configuration
necessary for effective ternary complex formation. Adequate length ensures
optimal potency. Both too-long and too-short linkers can negatively impact the
potency of PROTACs.

  * •

Small changes in linker length can shift the degradation selectivity between
closely related protein isoforms.

Linker composition. The linker composition has an impact on the
physicochemical properties of PROTACs, such as solubility and membrane
permeability, among other factors. 59 For instance, the substitution of amide
bonds with ester bonds in BET degraders MZ1 and ARV-771 results in
improvements to their permeability and cellular activity.60 The ester-linked
versions, OMZ1 and OARV-771, demonstrated a 10- and 1.5-fold increase in PAMPA
permeability, respectively. This improved permeability contributes to their
enhanced ability to degrade target proteins, with OMZ1 achieving a 1.5- to
2-fold increase in degradation potency, while OARV-771 achieved a 5.5-fold
increase. Despite concerns about the stability of esters compared to amides,
both OMZ1 and OARV-771 maintained stability in plasma, indicating that their
increased permeability does not compromise their overall stability.
Additionally, an optimal lipophilicity range (ALogP between 3 and 5) was
established for these ester-linked PROTACs, balancing effective membrane
crossing with adequate aqueous solubility and minimal efflux. Klein _et al._
60 conclude that amide-to-ester substitution can benefit the optimization of
PROTACs, and potentially other compounds, falling beyond the Rule of 5.

The composition of the linker can also improve other physicochemical
properties of PROTACs, such as metabolic stability, and biodistribution. 82
These properties influence how the drug is adsorbed, distributed, and
eventually metabolized inside the body. However, the majority of linkers in
PROTACs have been based on a limited set of chemical motifs, with PEG and
alkyl chains being the most common. Approximately 55% of linkers utilize PEG,
while about 30% use alkyl chains of various lengths. 57 These motifs are
favored due to their versatility, ease of synthesis, and ability to modulate
the solubility and permeability of PROTAC molecules. Around 65% of published
PROTAC structures incorporate both alkyl and PEG segments within their
linkers. This combination aims to leverage the beneficial properties of both
motifs, such as the flexibility and hydrophilicity provided by PEG, and the
structural simplicity and modifiability of alkyl chains. A further 15% of
linkers involve modifications to the basic glycol units in PEG, such as adding
methylene groups. 57 Such modifications are typically done to explore
different chain lengths and thus influence the potential structural
configurations accessible to PROTACs.

  * •

Amide-to-ester substitution can benefit the optimization of PROTACs, and
potentially other compounds, falling beyond the Rule of Five.

  * •

Modifications in PROTAC linker composition, such as altering chemical groups
and combining different motifs, directly influence the physicochemical
properties of PROTACs.

Linker flexibility. The flexibility of the linker can allow a PROTAC to more
easily adapt to different spatial configurations, though too much flexibility
may also lead to less predictable interactions.54, 89 A certain degree of
rigidity can thus confer stability during ternary complex formation, leading
to more consistent degradation activity. A 2018 study on PROTACs for ANK-
binding kinase 1 (TBK1) degradation underscores the role linker flexibility
plays in PROTAC activity.89 Flexibility was imparted via long alkyl and ether
chains to achieve potent compounds due to the inherent flexibility of these
chemical units. A systematic exploration of linker length was thus conducted,
and the degradation activity of each PROTAC variant was measured, focusing on
those with sub-µM potency to identify effective linker lengths and
compositions. The study found that PROTACs with linkers shorter than 12 atoms
showed no appreciable degradation activity. In contrast, longer linkers,
despite their higher polar surface area and challenges in cellular
penetration, were generally well-tolerated and effective in degrading TBK1.
The very flexible nature of the linker allowed long linkers to orient the
ligands in a way that facilitated the association of TBK1 and VHL into
suitable ternary complexes.

Another study describes how the ability of PROTACs to induce selective protein
degradation is enhanced by the plastic nature of the binding interactions
between CRBN and BRD4 bromodomains. 54 Plasticity here means that the proteins
can adopt multiple conformations at the binding interface depending on the
linker length, composition, and linkage position. It was shown that different
linkers can promote different binding conformations between the CRBN and BRD4.
This plasticity allows the PROTAC to effectively bring the proteins into
proximity in orientations that are conducive to ubiquitination. Using X-ray
crystallography and molecular docking, the authors shed light on how different
linker configurations lead to distinct low-energy binding conformations
between CRBN and BRD4. The varying conformations accessible to PROTACs in this
study illustrate how linker-induced flexibility directly impacts biological
outcomes.

  * •

The flexibility of a PROTAC’s linker can be tuned by adjusting, for instance,
the length and chemical composition of the linker.

  * •

Flexibility can allow for conformational adaptability and access to multiple
binding orientations.

Bottom line – There are no definitive guidelines that guarantee the design of
an effective PROTAC for any given E3 ligase-POI pair. 57 This means that
developing a potent degrader generally requires trial and error, with a
reliance on empirical metrics to identify effective linker features that
establish the optimal SAR. The large combinatorial space makes linker design
ideally suited to data-driven approaches, which provide a valuable complement
to traditional, labor-intensive experimental methods. Computational models can
furthermore simulate how ternary complexes form and behave at a level of
detail generally inaccessible in most experiments. Used wisely, computational
tools can aid in tricky tasks such as linker optimization without the need to
synthesize and test numerous variants experimentally, potentially speeding up
their development and reducing the costs associated with the synthesis and
empirical testing of unpromising compounds.

One recent study which nicely illustrates these points used a combination of
crystallographic data and mathematical modeling to explore the conformational
dynamics of protein-protein interactions induced by PROTACs, to understand how
these dynamics influence ubiquitination and eventual protein degradation.90
Interestingly, the authors found that the stability of the ternary complex did
not necessarily correlate with increased protein degradation efficiency,
suggesting that excessive stability might inhibit degradation efficiency.
Notably, the spatial arrangement and kinetic properties of the ternary complex
were crucial in this context: effective PROTACs brought lysine residues on the
POI close to the active site of the E2 enzyme, facilitated by the E3 ligase
within the complex. Lysine residues are the most common sites for
ubiquitination in proteins. The authors also confirmed that the kinetics of
the ternary complex, especially its dissociation rate, also play a role in
determining the degradation efficiency. Salt bridges and the hydrophobicity of
the interactions within the ternary complex were found to contribute
positively both to the cooperativity and to the half-life of the interaction.
These findings suggest prioritizing compounds that can induce the necessary
conformational dynamics without overly stabilizing the ternary complex,
highlighting how valuable insights can be gained using computational tools.

###  2.2 PROTAC linker design goes beyond fragment linking

While PROTAC design shares similarities with traditional small-molecule drug
design, it is fundamentally distinct, notably in linker optimization (Figure
2c). For instance, the approach to optimizing PROTAC linkers differs
significantly from the concept of “fragment linking” used in fragment-based
drug design (FBDD). In essence, FBDD is a strategy used in drug discovery
where small, low-complexity molecules, i.e., fragments, are screened for
binding to a specific pocket on the target protein. First, a library of small
chemical fragments is created. These fragments are typically smaller than
traditional drug-like molecules, with a molecular weight of less than 300 Da.
The fragment library is then screened against the target protein to identify
fragments that bind to the protein. This can be done using various techniques
such as nuclear magnetic resonance (NMR) spectroscopy, X-ray crystallography,
surface plasmon resonance (SPR), and thermal shift assays. Fragments that show
binding affinity to the target protein are identified as “hits.” These hits
often bind with low affinity but serve as a starting point for further
optimization. Subsequently, the identified fragments are optimized to improve
their binding affinity and drug-like properties. This can involve growing the
fragment by adding more atoms, merging fragments that bind to adjacent sites,
or linking fragments that bind to different parts of the target protein.
Finally, the optimized fragments are developed into lead compounds, which have
improved pharmacological properties and can be further tested in biological
assays and in vivo studies. 91, 92

Fragments can be an ideal starting point for drug design, with fragment
growing and linking strategies allowing for the optimization of their potency
and physicochemical properties. Fragment linking in particular gives the
possibility for significant potency gains by ensuring that the linked molecule
maintains the interactions of the original fragments, a phenomenon known as
super-additivity.93 However, achieving this is in practice very challenging,
as a bad linker can instead lead to the disruption of fragment binding poses.

Despite its success in drug discovery, FBDD may fall short when applied to
PROTAC linker design. PROTACs are substantially larger and more complex than
the small fragments typically dealt with in FBDD. The linker in a PROTAC must
connect two distinct binding moieties, facilitating the formation of a stable
ternary complex, and does not simply focus on improving the binding affinity.
To reiterate, the linker in a PROTAC must be flexible enough to allow the
formation of a ternary complex but rigid enough to maintain the correct
spatial arrangement of the ligands. This balance is difficult to tackle using
traditional FBDD approaches, which focus on optimizing single-binding
interactions rather than complex multi-protein assemblies. The unique
challenges posed by the size, complexity, and spatial requirements of PROTACs
necessitate more advanced methodologies. While direct application of typical
fragment-linking strategies used in FBDD is not generally feasible in PROTAC
design, a modular approach can certainly be beneficial. As we show in the next
section, researchers are already taking inspiration and lessons learned from
FBDD and applying them to PROTAC design.

Fig. 2: (a) An overview of fragment-based drug design (FBDD). The initial step
involves fragment screening to identify potential fragments that can bind to
the pocket of the target protein. These fragments are then linked and
optimized to improve their binding properties. The result is a strongly-bound
ligand that fits precisely within the target protein’s pocket. (b) left – The
linker in a PROTAC isn’t just a passive bridge. It’s an important component
that enhances the interaction dynamics between the POI and the E3. right – The
linker also contributes to the PROTAC’s overall PK profile, including cell
permeability. center – Because its MoA relies on transient ternary complex
formation, the PROTAC is eventually released, meaning it is catalytic and can
go on to be reused for other processes inside the cell. (c) The large and
multivalent nature of PROTACs means they require a more complex design
approach than FBDD methods developed for small molecules. The linker must be
long and/or flexible enough to allow the warhead and E3 ligase ligand to adopt
the necessary conformations for effective ternary complex formation, but not
too flexible that the PROTAC cannot maintain the correct spatial orientation
of the warhead and E3 ligase ligand. The linker may also need to incorporate
specific chemical groups to enhance the overall potency of the PROTAC.

###  2.3 FBDD and ML pave the way for PROTAC development

The advent of ML in drug discovery has transformed the way researchers
approach the design and optimization of therapeutics. ML algorithms streamline
labor-intensive design-make-test-analyze (DMTA) cycles by automating the
identification of promising compounds and enabling researchers to avoid
synthesizing and testing ineffective compounds, thereby reducing both time and
costs.80, 81 Before the widespread use of ML in drug discovery seen today,
computational tools for linker design typically involved searching a database,
limiting proposed linkers to those already documented in the database.93 While
such approaches have been successful, ML-based solutions can give models an
advantage by allowing them to better explore the possible chemical space.
Generative models, in particular, are used to create new molecules from
scratch, designing possible structures with enhanced or desirable property
profiles based on patterns learned from data. 94, 95 However, generative
models trained for FBDD are optimized for designing linkers between small
fragments. These models might not account for the larger size and higher
complexity of PROTAC structures, potentially leading to inaccurate linker
design. Presented below is a detailed overview of generative models used in de
novo linker design within the framework of FBDD.

2D generative models in linker design. SyntaLinker96 is a method that employs
syntactic pattern recognition with molecular string representations via deep
conditional transformer neural networks.96 It uses molecular representations
based on the simplified molecular-input line-entry system (SMILES), one of the
most popular methods for representing 2D molecular graphs as strings.
SyntaLinker allows for the automatic linking of molecular fragments by
learning from patterns in drug-like compounds in ChEMBL.97 This enables the
generation of new molecular structures starting from pairs of fragments in a
conditional way; possible constraints include the shortest-linker bond
distance (SLBD), HBDs, number of rotatable bonds, and number of rings. The
constraints are incorporated as control codes. For example, “[L_4]” denotes a
linker with a bond distance of four, which acts as a guiding prefix in the
sequence. ChEMBL data was filtered using Lipinski’s “Rule of Five,” PAINS
substructures, and synthetic accessibility (SAscore) to ensure lead-like and
synthesizable molecules were used to construct the training data. The dataset
was constructed using matched-molecular pairs (MMPs) to build molecules into
fragment molecule triplets (fragment 1, linker, fragment 2). SyntaLinker
consists of multiple encoder–decoder stacks where each encoder layer has a
multi-head self-attention sub-layer and a feedforward network sub-layer. The
model was trained using the prepared dataset, with SLBD and the aforementioned
constraints to learn the implicit rules of fragment linking. The generated
molecules were evaluated using several metrics, including validity (97.2%),
uniqueness (88.1%), recovery (84.7%), and novelty (91.8%).

Link-INVENT93 is an extension to the existing de novo molecular design
platform REINVENT; it uses policy-based reinforcement learning (RL) for multi-
parameter optimization, and can be applied to both fragment linking and
scaffold hopping given a desired property profile. Via RL, the Link-INVENT
agent learns to generate linkers connecting molecular fragments while
satisfying diverse objectives, facilitating the practical application of the
model for real-world drug discovery projects. In the original study, Link-
INVENT used the drug-like compound SMILES extracted from ChEMBL for training.
Lenient criteria were applied to ensure the dataset’s effectiveness for PROTAC
applications (e.g., larger warheads). ChEMBL compounds were sliced using
reaction SMIRKS to create triplets (linker, warheads, full molecule).
Unrealistic data points were removed, and datasets were augmented via SMILES
randomization for improved generalizability. Link-INVENT is trained based on
the conditional probabilities of observing a linker given both molecular
subunits, similar to SyntaLinker. The agent is initialized with the same
parameters as the prior and is updated via RL to generate linkers that
increasingly satisfy the desired multi-parameter optimization (MPO)
objectives. The scoring function combines various components (physicochemical
properties, structural features, predictive models, and binding energy
approximations) to evaluate the desirability of generated linkers. Link-INVENT
was tested in various experiments, demonstrating its capability to generate
linkers that meet specific criteria. Notably, Link-INVENT has also been
demonstrated to be effective in PROTAC linker design, successfully optimizing
the properties of generated linkers, including effective length, the presence
of rings, and flexibility.

Due partly to the surprising effectiveness of 2D representations like SMILES,
the majority of molecular generative models used for de novo molecular design
and FBDD have made limited use of 3D structural information, including
SyntaLinker and Link-INVENT. Nevertheless, the PROTAC MoA suggests that
incorporating 3D information may come to play an important role in designing
PROTAC structures which lead to favorable ternary complexes. In the next
subsection, we cover ML models that seek to incorporate structural information
into their molecular design workflows.

3D generative models in linker design. DeLinker98 is a graph-based deep
generative model that incorporates 3D structural information for designing
molecules using a multi-modal encoder-decoder. Training data was derived from
a 250k molecule subset of ZINC, better known as ZINC-250k.99 The dataset was
further processed to create fragment-molecule pairs using standard
transformations from MMP analysis. 3D conformers were generated using RDKit,
and the lowest-energy conformation was used as the reference structure.
Molecules are represented as graphs, where atoms are nodes and bonds are
edges. The model builds new molecules iteratively, bond by bond, from a pool
of atoms that can be initialized with partial structures. The model uses a
gated graph neural network (GGNN) for encoding and a single-layer neural
network for edge prediction and labeling. This setup allows the model to use
local, global, and 3D structural information for molecule generation, encoding
multi-modal molecular information in a low-dimensional latent space. The model
is trained under the VAE framework to reconstruct known linkers from fragment
pairs and the linked molecule. The latent vector is derived from the embedding
of the linked molecule, and the model is regularized to follow a standard
normal distribution. The training objective includes a reconstruction loss and
a Kullback-Leibler (KL) regularization term, which together ensure that
generated molecules are valid and structurally sound. DeLinker can generate
novel molecular structures, including those with longer linkers of at least
five atoms. It was applied to the design of PROTACs targeting SMARCA2 and
SMARCA4 subunits, where it generated linkers maintaining the geometry observed
in a ternary complex with VHL. Imrie _et al._ 100 further improves DeLinker in
their more recent DEVELOP framework by introducing a convolutional neural
network (CNN) which operates on the 3D structure of the starting fragments.
DEVELOP not only improves the proportion of generated molecules with high 3D
similarity to the reference molecule, but also recovers 10× more of the
original molecules compared to DeLinker.

3DLinker101 is a conditional generative model for designing molecular linkers
using 3D spatial information and is capable of generating linker graphs along
with their 3D structures and anchor atoms. This is achieved through an
E(3)-equivariant graph VAE, addressing challenges such as the conditional
generation of linkers based on two input ligands and the requirement for 3D
structural awareness to avoid atom clashes. It predicts both the graph (2D)
structure of the linker and its 3D coordinates while ensuring the model’s
outputs are equivariant with respect to E(3) group symmetries (i.e., rotation,
translation, and reflection). The training data was derived from the ZINC
database,102 from which 3D conformers were generated for each molecule using
RDKit and the lowest-energy conformation chosen as the reference structure.
The final curated dataset contains $\sim$366k (fragment, linker, coordinate)
triplets and was roughly divided into 99.8%/0.1%/0.1%
training/validation/testing splits. Using this generous training split, the
model outperforms other baselines, including DeLinker and other 2D graph
generative models (coupled with ConfVAE103 for 3D structure generation), in
recovering molecular graphs and accurately predicting the 3D coordinates of
atoms. Nevertheless, it is unclear if the reported metrics are for the
training, validation, or test set. While 3DLinker demonstrates improved
performance in generating 3D molecular structures with accurate geometry,
precise connection of molecular fragments, and higher recovery rates, the
authors observed that this comes with the trade-off of lower uniqueness and
novelty in sampled molecules compared to the benchmarked approaches.

DiffLinker104 is an E(3)-equivariant 3D-conditional diffusion model for the
design of molecular linkers. This approach uniquely generates molecular
linkers for a set of input fragments represented as 3D atomic point clouds,
overcoming the limitations of previous methods by not being restricted to
linking pairs of fragments. DiffLinker automatically determines the number of
atoms in the linker and its attachment points to the input fragments. As the
previous approaches, DiffLinker was trained and evaluated on a dataset derived
from ZINC-250k, but the authors also took things a step further by
benchmarking on two additional datasets: one derived from CASF-2016, and
another derived from GEOM105. The molecules derived from GEOM can be
decomposed into three or more fragments with one or two linkers connecting
them, creating a more challenging benchmark that better approximates real-
world usage. DiffLinker demonstrates an ability to generate diverse and
synthetically accessible molecules with minimal clashes, especially when
conditioned on target protein pockets. It represents a significant advancement
in FBDD, providing a powerful tool for the generation of chemically relevant
molecules in a flexible and efficient manner. Nevertheless, the authors did
not apply their fragment-linking approach to PROTAC design.

Building upon the success of SyntaLinker, DRlinker106 is a similar approach
that incorporates RL, and, indirectly, 3D information, for the generation of
linkers with specific 2D and 3D attributes. It was trained and evaluated for
FBDD on datasets derived not only from ChEMBL, but also from CASF-2016. 107 On
tasks like optimizing bioactivity, it achieves a 91.0% and 93.9% success rate
in generating compounds with desired linker length and LogP, respectively.
Despite being based on 2D SMILES representation, DRlinker can also perform
scaffold-hopping in a way that generates molecules with high 3D similarity but
low 2D similarity to lead inhibitors. Two years later, the same team followed
up with another model for FBDD which aims to better incorporate 3D
information. GRELinker108 combines a gated-graph neural network (GGNN109) with
RL and curriculum learning (CL) to design linkers with desirable property
profiles. Its architecture is very similar to that of GraphINVENT.110 It
outperforms DRlinker in tasks such as controlling LogP, optimizing
synthesizability and bioactivity, and generating molecules with high 3D
similarity but low 2D similarity to lead compounds. It has also been evaluated
in scenarios representative of real-world use-cases, where the aim is to
optimize for molecular affinity using docking scores. The authors found that
the use of CL improved its efficiency in generating complex linkers.

Despite the successes of the aforementioned works in FBDD, and, in particular,
of DeLinker and Link-INVENT in PROTAC linker design, the methods reviewed
above all face a key limitation – they were all trained and optimized on
small-molecule binders rather than on an actual PROTAC dataset. Although
careful filtering was done to make the datasets more generalizable beyond
small-molecule binders, due to the fact that warheads can be much larger than
the typical fragments used in FBDD, we argue that the training of these models
may not fully capture the unique features and complexities of larger,
multivalent molecules like PROTACs, nor their unique chemistry. As previously
discussed, PROTACs not only have larger sizes but also exhibit different
biophysical and chemical properties compared to the small molecules typically
found in drug discovery databases (Figure 3). This training limitation can
affect the applicability of these methods for designing effective PROTAC
linkers, as the chemical space and design strategies for PROTACs diverge
significantly from those of small molecules. This underscores the necessity
for specialized tools for PROTAC linker design that can accommodate their
unique size, complexity, and 3D structural requirements. The next section
reviews ML models specifically tailored for PROTAC design.

###  2.4 Previous work in PROTAC linker design

In this section, we focus on prior ML studies that have been designed to
advance the optimization of PROTAC linkers. By highlighting how computational
models have been intentionally developed to refine PROTAC linker design, we
hope to illustrate how the application of these technologies has evolved from
traditional drug design to TPD. As ML techniques continue to evolve, they are
expected to play an increasingly central role in PROTAC development. Notable
ML-driven works such as AIMLinker111 and PROTAC-RL112 shed light on the
complex nature of PROTAC linker design, though they are not the only recent
works to tackle this problem.

PROTAC-RL is a deep generative model combining an augmented transformer
architecture with memory-assisted RL capable of generating PROTACs with
favorable PK properties, including solubility, stability, and
bioavailability.112 Notably, the authors experimentally validated their model
by testing the synthetic feasibility of six of their designs. To address the
challenge of limited training data, the model was pre-trained using a large
dataset of PROTAC-like structures, termed quasi-PROTACs, followed by fine-
tuning on actual PROTAC data. Given a pair of E3 ligand and warhead SMILES,
the model generates optimized linkers, which aim to optimize the PK attributes
of the returned PROTACs. PROTAC-RL achieved a recovery rate of 43.0%, much
higher than the recovery rates of the baseline models, DeLinker and
SyntaLinker, even after these were retrained using the PROTAC training
datasets. After retraining, Delinker and SyntaLinker achieved recovery rates
of 4.8% and 10.4%, respectively. This stark contrast in recovery rates between
PROTAC-RL and the benchmarked models after retraining further strengthens the
argument that models designed and trained for small molecular fragments cannot
adequately capture the unique aspects of PROTACs, as the design strategies and
principles for these two classes of molecules are fundamentally different.
Because the RL component allows for the conditional generation of PROTACs with
specific properties, such as a desired protein target, the authors applied
PROTAC-RL to the design of BRD4-targeting PROTACs. To this end, they generated
5k compounds which they then filtered through a combination of ML classifiers
and molecular simulations to identify candidates with favorable PK properties
and synthetic accessibility. Of the six candidate PROTACs which were
synthesized and experimentally tested, three showed inhibitory activity
against BRD4 in cell-based assays. One lead candidate demonstrated high anti-
proliferative potency and a favorable PK profile in mice.

AIMLinker is a GGNN109 model for autoregressive PROTAC linker generation at
the atomic/bond level.111 Like GRELinker, it seeks to improve upon previously-
developed graph-based deep generative models like DeLinker, CGVAE113, and
GraphINVENT110 via the incorporation of 3D information. AIMLinker was trained
on a dataset combining molecules from ZINC and PROTAC-DB114. The training
focused on predicting viable 2D linker structures from fragment-molecule
pairs. Generated molecules were then validated via molecular docking and
simulations to verify binding to the target proteins via binding affinity and
conformational predictions. AIMLinker was used to successfully generate a
diverse library of novel PROTACs. The model demonstrated superiority over
other fragment-linking methods (DeLinker and DiffLinker) in generating
molecules with favorable PK properties and high binding affinities, with a few
designed PROTACs even outperforming the reference compound dBET6 in binding
affinity and structural alignment (Figure 1c). Despite a promising performance
in PROTAC linker design, AIMLinker does have two current limitations, namely
the focus on a single PROTAC target (BRD4) and the reliance on docking
predictions, which are known to be inaccurate. 115

Finally, ShapeLinker116 is a model based on Link-INVENT, but with an important
shape alignment contribution to the scoring function, and less significant but
still important contributions from the ratio of rotatable bonds and the linker
length ratio. The authors train on PROTAC-DB114 data, as well as on ten well-
known ternary complexes from the Protein Data Bank (PDB): 5T35, 7ZNT, 6BN7,
6BOY, 6HAY, 6HAX, 7S4E, 7JTP, 7Q2J, and 7JTO. All of these complexes have
binding PROTACs that were optimized in individual structure-based drug studies
and cover a diverse range of PROTAC (and linker) “shapes.” These are included
in the training of the shape alignment model. Nevertheless, it is not clear
whether these additions indeed improve the performance of ShapeLinker over
that of the base Link-INVENT. The results suggest that perhaps larger changes
to the model architecture are required for step-changes in performance.

##  3 Machine learning in de novo PROTAC design: going beyond linker
optimization

###  3.1 Comprehensive PROTAC design strategies

The design of a PROTAC involves more than the design of the linker; it also
includes the optimization of the ligands that bind to the target protein and
the E3 ligase. The challenge lies in designing a molecule where these
components work in harmony to achieve ubiquitination and subsequent
degradation of the target protein. The design process begins with the
selection of a ligand, if known, that can selectively bind to the POI. This
step is crucial because the efficacy of a PROTAC largely depends on the
ability of this warhead to recognize and attach to the intended POI. 18 When
numerous known binders exist for a protein, the binding affinity,
physicochemical properties, and synthetic feasibility of these binders are
crucial factors to consider. Approved drugs, drug candidates in clinical
trials, or highly active inhibitors may be preferred starting points due to
their optimized PK and PD properties, for example. 117

Equally important is the choice of an E3 ligase and a corresponding ligand.
The selection of the E3 ligase often involves considering a range of factors,
such as the ubiquitination efficiency, the specificity of the ligase, and its
expression levels within the relevant cells or tissues. For instance, if a
PROTAC is being developed for cancer therapy, the chosen E3 ligase should be
highly expressed in cancer cells and less so in healthy cells to minimize off-
target effects. 118 Furthermore, different E3 ligases can induce varying
degrees of degradation even with the same POI ligands and linkers. The
selection of an effective E3 ligase and ligand is thus a critical aspect of
PROTAC design, and structural knowledge of the E3 ligase-POI interaction can
significantly aid this process. Interestingly, neither the binding affinity of
the warhead nor of the E3 ligase ligand seems to directly influence the
degradation efficiency of the PROTAC. 117

Once the individual ligands have been selected, the next challenge is to
design a molecule where these components work in concert. This harmony is
essential for the formation of an effective ternary complex between the
PROTAC, the target protein, and the E3 ligase. The spatial and temporal
dynamics of this complex formation are critical. It’s not just about bringing
these entities into proximity; it’s also about ensuring that they interact in
a manner that facilitates the transfer of ubiquitin from the E3 ligase to the
target protein. For instance, the spatial arrangement in a potential ternary
complex needs to allow the POI’s ubiquitination site to be accessible to the
E3 ligase once the complex is formed. This may involve tweaking the linker
length, rigidity, or chemical composition to achieve the optimal orientation.

Nevertheless, some ML-driven methods for PROTAC design seek to tackle the
problem in a more holistic manner. Though less common than modular approaches
which focus heavily on PROTAC linker optimization, comprehensive PROTAC design
strategies can be advantageous for a few reasons. They allow, in principle,
for the simultaneous optimization of multiple parameters, such as flexibility,
cell permeability, and degradation efficiency, factors which are determined
not only by the linker composition, but also by that of the warhead and E3
ligand. A holistic approach may also better account for the complex
interactions between the different PROTAC components, leading to the design of
more effective and specific PROTACs; nevertheless, this remains to be
rigorously demonstrated. In the next section, we examine the only study which,
to our knowledge, has tackled the problem of engineering PROTACs in a holistic
fashion, challenging traditional FBDD principles.

###  3.2 Previous work in comprehensive PROTAC design

A case study in ML-driven de novo PROTAC design is the application of
GraphINVENT, a graph-based deep generative model, to the generation of novel
PROTAC graphs predicted to represent highly active degraders.119 The authors
used policy-gradient reinforcement learning (RL) and a surrogate model for
protein degradation activity to guide the model toward a chemical space of
more active potential degraders. The open-source PROTAC-DB114 database was
used to train the model, which included 638 “complete” entries detailing
degradation activity in various systems. For an entry to be considered
complete, it needed to include a PROTAC SMILES, an E3 ligase, a POI, a defined
cell type, and, crucially, a DC50 value. The authors reported that, despite
their large size, GraphINVENT did not struggle to propose novel PROTAC-like
structures starting from empty graphs; they go on to show that, following RL
fine-tuning, the model could generate diverse molecules with not only higher
predicted activity than the prior, but also substructures found in known
degraders. An analysis was conducted to demonstrate that the model could be
used to generate novel compounds with high predicted activity for IRAK3
degradation, but none of the proposed structures were experimentally
validated.

###  3.3 Modeling degradation activity in PROTACs

A critical component of any generative model for PROTACs is a reliable
surrogate model for degradation activity. To this end, a few data-driven
approaches have been developed to tackle the prediction of degradation
activity in PROTACs. Nori _et al._ 119 first used eXtreme Gradient Boosting
(XGBoost) and Morgan fingerprints to classify potential degraders into
“active” or “inactive” compounds. Following this work, Li _et al._ 120
introduced DeepPROTACs, a deep neural network architecture integrating graph
convolutional networks (GCNs) and bidirectional long short-term memory (LSTM)
layers for predicting degradation activity in PROTACs. DeepPROTACs was trained
using data from PROTAC-DB and other public sources, including 2832 labeled
datasets split into 988 “good” degraders and 1844 “bad” degraders based on
their DC50 and D${}_{\text{max}}$ values. When tested on a dataset of 16
PROTACs targeting ER and VHL, the model achieved a prediction accuracy of
68.75%. For other PROTAC targets like EZH2, STAT3, eIF4E, and FLT3, the
accuracy rates ranged from 65% to 80%. More recently, Ribes _et al._ 121
introduced a neural network ensemble model for the classification of PROTACs
into “active” and “inactive” compounds. Here, the authors combined data from
PROTAC-DB and PROTACpedia122, where only entries which had both a DC50 and
D${}_{\text{max}}$ value reported were used. PROTACs were represented using
Morgan fingerprints, while target proteins, E3 ligases, and cell types are
each embedded into feature vectors using linear layers, and normalized. The
model demonstrated superior performance to previous models for degradation
activity classification, reaching a top test accuracy of 82.6% when using a
stratified data split, and a test accuracy of 61% on unseen POIs. The authors
conclude that the model will generalize well to novel PROTAC structures so
long as both the POI and E3 ligase have been seen before in training.

###  3.4 Innovations and emerging trends

The field of PROTAC design is rapidly evolving along with new ML approaches to
molecular design. One common thread in many of the aforementioned methods is
the use of RL to learn optimal policies for PROTAC design through trial and
error. Another emerging trend is the integration of 3D information into
generative approaches. This allows for a more holistic view of the
interactions between proteins and can lead to more effective PROTAC designs.
Additionally, there is a growing trend towards the use of transfer learning,
where a model developed for one task is reused as the starting point for a
model on a second task. This is particularly useful in PROTAC design where the
limited amount of public data poses a challenge.

Although diffusion models have not yet been applied to PROTAC design (only
FBDD, as in DiffLinker104), we believe they present a promising direction in
PROTAC engineering, both for linker-only and holistic design strategies.
Firstly, diffusion models excel at generating high-quality molecular
structures by gradually transforming simple distributions into complex data
distributions. 123 Secondly, diffusion models can naturally integrate 3D
information, which allows for the design of PROTACs that account for the
spatial arrangement and interactions between the POI, the PROTAC, and the E3
ligase. Diffusion models are also known for their robustness in handling noisy
data,124 and they can be integrated with existing generative and predictive
frameworks in an online setting. 125 These capabilities of diffusion models
make them natural choices to explore further for generating diverse and novel
PROTAC structures.

For a detailed summary of all models surveyed in this work, please see Table
1.

Table 1: Summary of previous work on ML-guided PROTAC design. SMILES is a molecular string representation constructed from 2D graphs. RL: reinforcement learning. LSTM: long short-term memory, a class of recurrent neural network. JT-VAE: the junction-tree variational autoencoder.126 CNN: convolutional neural network. GGNN: gated-graph neural network.109 CL: curriculum learning. GCNs: graph convolutional networks. MLP: multi-layer perceptron. The annotation “(w/ 3D coords)” indicates that 3D information was used indirectly during structure generation (e.g., scoring), whereas “+3D coords” indicates 3D information was directly used in structure generation.  Model | Year | Data | Type | Focus
---|---|---|---|---
SyntaLinker96 | 2020 | SMILES | transformers | fragment linking
PROTAC-RL112 | 2022 | SMILES | transformers+RL | fragment linking & PROTAC linker design
Link-INVENT93 | 2023 | SMILES | LSTM+RL | fragment linking & PROTAC linker design
ShapeLinker116 | 2023 | SMILES (w/ 3D coords) | Link-INVENT | PROTAC linker design
DRlinker106 | 2022 | SMILES (w/ 3D coords) | transformers+RL | fragment linking
DeLinker98 | 2020 | 2D graphs (w/ 3D coords) | JT-VAE | fragment linking
DEVELOP100 | 2021 | 2D graphs+3D coords | JT-VAE+CNN | fragment linking
3DLinker101 | 2022 | 2D graphs+3D coords | E(3) eq. graph VAE | fragment linking
Nori _et al._ 119 | 2022 | 2D graphs |  GraphINVENT110 (GGNN+RL) | full (“holistic”) PROTAC design
AIMLinker111 | 2023 | 2D graphs (w/ 3D coords) | GGNN | PROTAC linker design
GRELinker108 | 2024 | 2D graphs (w/ 3D coords) | GGNN+RL+CL | fragment linking
DiffLinker104 | 2024 | 2D graphs+3D coords | 2D GNN+E(3) eq. 3D diffusion | linker size prediction & fragment linking
Nori _et al._ 119 | 2022 | Morgan fingerprints | XGBoost | degradation activity prediction
DeepPROTACs120 | 2022 | SMILES+3D graphs | GCNs+LSTMs | degradation activity prediction
Ribes _et al._ 121 | 2024 | Morgan fingerprints | MLP | degradation activity prediction

###  3.5 Datasets

There are currently two main sources of openly-accessible, structured PROTAC
data: PROTAC-DB and PROTACpedia.

PROTAC-DB is a public database designed to support the research and
development of PROTACs.114 It offers an online repository of structural and
experimental data related to these molecules. Data in the database is manually
extracted from the literature or calculated using specific programs. In the
second release, the number of PROTACs was expanded to 3,270 and featured
$\sim$360 warheads, $\sim$1,500 linkers, and $\sim$80 E3 ligands. As of June
2024, PROTAC-DB contains 5,388 entries. It also includes ternary complex
structures for PROTACs. PROTAC-DB covers key aspects of PROTAC activity,
including degradation capacity, quantified by metrics like DC50 and
D${}_{\text{max}}$; binding affinities between PROTACs (or PROTAC ligands) and
target proteins and E3 ligases; cellular activities such as IC50, EC50, GI50,
and GR50; and PAMPA and Caco-2 permeability data. Nevertheless, entries are
not necessarily complete and there is a lot of missing data in PROTAC-DB,
often because the original source does not report all aforementioned
metrics.121

PROTACpedia is a curated database focused on PROTACs, containing detailed
entries on 1,190 PROTAC molecules as of the latest update (October 2022).122
It contains high-quality data that has been carefully curated by experts,
including information on $\sim$202 warheads, $\sim$65 E3 ligands, and
$\sim$806 linkers. This platform facilitates the sharing and dissemination of
critical PROTAC-related data to help expand PROTACpedia. Its collaborative
nature encourages contributions to ensure that the database remains an up-to-
date resource for researchers exploring PROTACs.

There is a significant overlap in the activity distributions of structures
deposited in PROTACpedia with those in PROTAC-DB. As of June 2024, there are
807 PROTACs present in both databases, identified via string comparison
following canonicalization of PROTAC SMILES from both databases. In other
words, roughly 68% of PROTACs in PROTACpedia and 25% of PROTACs in PROTAC-DB
are present in both databases. We did not explore what fraction of the
duplicate PROTAC structures correspond to duplicate entries between the two
databases, as it is possible that a PROTAC may be present in both databases
but still contain information about different sets of experiments.

As PROTACs represent a relatively new therapeutic modality, there is a
relative scarcity in the number of publicly available crystal structures,
especially for ternary complexes. Structures that are available in the PDB
have most frequently been determined using cryogenic electron microscopy
(cryo-EM), a technology that has revolutionized the field of protein
structural biology. Nevertheless, because the PROTAC MoA is not particularly
well-understood, generalizations are being made across a range of PROTACs
based on limited mechanistic data. Researchers would be wise to exercise
caution when generalizing too far beyond the scope of their models or
experiments.

Part of the challenge in ML-driven PROTAC engineering stems from the limited
amount of structured data available. While public databases such as these have
been greatly influential thus far in driving the development of ML tools for
PROTAC design, without more comprehensive datasets, data-driven models will
only be able to access a fraction of the vast chemical space accessible with
PROTACs. Data scarcity becomes even more of a concern when considering factors
like bioactivity, PK properties, and 3D structure in PROTACs. Low-data and
low-resource learning can provide valuable strategies in the current scarce
data landscape 127, but, ultimately, more high-quality, structured data will
need to be systematically generated and deposited following FAIR data-sharing
principles for researchers to truly harness the powers of ML in PROTAC design.
We hope that, just as ML has become an invaluable tool for identifying hits
and optimizing leads in small-molecule drug discovery pipelines, it will also
transform the current paradigm of PROTAC engineering, making us wonder how we
ever managed without it.

##  4 Discussion

ML models trained on small-molecule datasets often struggle with
generalization to novel chemical spaces not represented in the training data.
128, 129 This can limit their predictive accuracy for entirely new classes of
compounds. PROTACs, with their bifunctional nature and larger size, represent
a significant departure from small molecules. PROTACs generally have larger
molecular weights and require higher degrees of conformational flexibility to
achieve their function. Notably, the physicochemical properties and PK
profiles of PROTACs are markedly different from those of traditional small
molecules. In Figure 3, we highlight some of the key physicochemical
differences between PROTACs and SMDs. These include differences in molecular
weight (MW), partition coefficient (LogP), number of rotatable bonds
(flexibility and conformational dynamics), number of HBDs and HBAs, and number
of carbon atoms. By analyzing these descriptors, we can gain precise insights
into the structural and physicochemical differences between PROTACs and small
molecules.

  * •

Molecular weight. The small-molecule MW distribution peaks around 250-500 Da.
This is the typical range expected for drug-like small molecules as it is
considered optimal for oral bioavailability according to Lipinski’s Rule of
Five. The PROTAC distribution peaks around 750-1000 Da, highlighting how much
larger they are than traditional small molecules. The small-molecule
distribution is relatively narrow and sharply peaked, indicating a more
uniform range of MWs, while the PROTAC distribution is broader, reflecting
greater variability in the size of these complex molecules. The clear
separation between the two distributions highlights a key difference between
PROTACs and traditional small molecules: their size.

  * •

Partition coefficient. The LogP distribution for small molecules peaks around
2-3. This is consistent with drug-likeness criteria, where a LogP value
between 1 and 3 is typically considered favorable for oral bioavailability.
The LogP distribution for PROTACs is broader and peaks around 5. Higher LogP
values indicate that PROTACs are generally more hydrophobic than small
molecules, which can affect their solubility and cellular permeability. The
higher LogP values for PROTACs may pose challenges for their solubility in
aqueous environments like the extracelullar environment and the cytosol, and
may require formulation strategies to enhance their solubility and
bioavailability.

  * •

Rotatable bonds. The distribution in the number of rotatable bonds peaks
around 1-5 rotatable bonds for small molecules. Fewer rotatable bonds are
associated with greater rigidity. For PROTACs, the distribution instead peaks
around 15-20 rotatable bonds. The higher number of rotatable bonds can be
largely attributed to the flexible linker regions in PROTACs.

  * •

Hydrogen bonds. The HBD distribution of small molecules peaks around 0-2. This
is in line with the drug-likeness criteria that suggest a limited number of
hydrogen bond donors to ensure good membrane permeability. On the other hand,
the HBD distribution of PROTACs peaks around 3-5. Similar trends are observed
for the HBA distributions.

  * •

Carbon composition. Both PROTACs and small molecules have a high normalized
carbon count, peaking between 0.7-0.8, with the peak being slightly lower for
PROTACs. Small molecules display a slightly broader distribution in normalized
carbon atom count. No significant differences in normalized nitrogen, oxygen,
or fluorine atom composition were observed, although small molecules do
display marginally broader distributions for all these atom types.

Fig. 3: The distributions of various molecular descriptors in PROTACs versus
small molecules. PROTACs were downloaded from PROTAC-DB and PROTACpedia, while
small molecules were randomly sampled from ZINC-250k102, a popular database
used in drug discovery containing commercially-available compounds for virtual
screening (e.g., drug-like compounds). This comparative analysis of their
chemical and physical properties highlights the differences between both
classes of molecules. The descriptors include molecular weight, partition
coefficient (LogP), number of rotatable bonds, number of hydrogen bond donors
(HBDs) and acceptors (HBAs), and normalized atom counts for carbon.

This comparative analysis highlights the unique challenges and opportunities
facing ML models for PROTAC design. Furthermore, it should be evident that
models trained on small fragments will not capture the distinct features of
PROTACs, as small molecule fragments and PROTACs exist in largely non-
overlapping areas of chemical space. Extending ML models developed for small
molecules to PROTACs requires modifications; this could entail small changes,
like re-training on larger molecules and/or more diverse datasets that include
PROTACs, or changes to fundamental design principles. It is well-known in deep
learning, including generative modeling, that the predictive accuracy of a
model depends heavily on the availability of high-quality datasets.130
However, in drug design, datasets often have various forms of inconsistencies
and missing data. 131 These challenges are even more pronounced when focusing
on PROTAC design; here, experimental data on the efficacy and specificity of
PROTAC molecules is even scarcer. The multifaceted nature of PROTACs
necessitates detailed and high-quality datasets to uncover the subtle patterns
underlying their biological activity. This is especially crucial for turning
predictive models into tools for robust PROTAC optimization and design.

Models that fail to incorporate structural, or even dynamical, information
regarding PROTACs and their target proteins might not effectively capture the
feasibility of ternary complex formation. We know that the specificity and
activity of PROTACs towards a specific POI are influenced by precise 3D
interactions at the molecular level, more so than by the binding affinity.
Without detailed 3D structural data, ML models may not be accurate enough to
generalize to new PROTAC structures or even new POIs, a concern reflected in
the changing landscape of ML models for PROTAC design: while early work
focused primarily on 2D representations or simplified 3D information, all work
we surveyed from the past two years involved the incorporation of more complex
3D data. We don’t believe this change is due solely to advances in computing
hardware and software. Rather, models capable of handling 3D data offer a
superior capability to capture the interplay of molecular shapes and complex
spatial arrangements especially relevant in PROTAC function.

Another big challenge with PROTAC design is getting them into the cell.59 As
they depend on the proteasome for degrading their target proteins, PROTACs can
only be used to target proteins found in the cytosol or with cytosolic domains
(for membrane proteins), thus excluding as targets any proteins found outside
the cell. According to the Human Protein Atlas, $\sim$25% of all protein-
coding human genes have been shown to encode proteins that localize to the
cytosol and its substructures,132 though this estimate does not include
proteins that transiently reside in the cytosol. How to improve cellular
permeability in PROTACs is thus an active area of research, as it imposes hard
constraints on their efficacy. Notwithstanding, exactly which mechanism
PROTACs use for entering the cell is not fully understood and may very well
vary depending on the specific molecule and cell type, adding another layer of
complexity to the task of cell permeability prediction.

To overcome the many challenges facing de novo PROTAC design, future ML
methods must place a greater emphasis on accurately modeling the 3D structures
of PROTACs and the corresponding ternary complexes they form. This could
involve the development of molecular dynamics or physics-based approaches that
leverage ML to simulate important molecular interactions and conformational
changes at a coarse-grained or even atomistic level, and it could also involve
experimental advances that allow us to better isolate and characterize these
complexes, possibly with the assistance of active learning or other ML-driven
strategies. Scientists leveraging ML have undeniably driven many recent
breakthroughs in protein structure prediction133 and conditional protein
structure generation.134 Perhaps it’s time to apply similar guiding principles
to PROTAC engineering (e.g., systematic data collection and accessibility,
better algorithms harnessing biological knowledge), and see what breakthroughs
we can achieve in this domain.

##  5 Conclusion

PROTACs differ fundamentally from small-molecule drugs (SMDs) in their
mechanism of action. SMDs such as inhibitors typically function by blocking a
protein’s active site and thus its activity, whereas PROTACs instead carry out
a complicated dance inside the cell, which, if performed correctly, will lead
to the degradation of the target protein. This means that rather than simply
inhibiting a protein’s function, a PROTAC removes it from the cell. Notably,
it is not consumed in the process, which means it can go on to cause the
degradation of many other copies of the target protein before it is
metabolized and/or excreted. This can be more effective in cases where simple
inhibition of activity is insufficient for a therapeutic effect, as is the
case in many diseases lacking effective first-line treatments, including many
cancers. PROTACs offer an alternative pathway to drugging otherwise
“undruggable” proteins in the cytosol.

In this comprehensive review, we have highlighted the significant impact of ML
on PROTAC design. The complexities involved in PROTACs make traditional ML in
the context of FBDD less effective. These complexities include the unique
mechanism of action of PROTACs, the delicate spatial configuration required
for effective protein degradation, and the need for favorable PK profiles for
drug-like compounds, which are not adequately captured by models designed for
small molecular fragments. Advanced ML techniques, such as generative models
tailored to PROTAC peculiarities offer promising solutions for optimizing
PROTAC design.

In the hope of spurring more research in what we view as a hugely impactful
but formidable research direction, we have prepared this comprehensive review
on ML for PROTAC design. We hope that this review and the insights described
in it serve as a comprehensive guide to researchers looking to apply their
deep ML knowledge to the design of an exciting “new” therapeutic modality, or
conversely, to enable biologists to venture into the rewarding world of deep
generative models. The synergy between ML and PROTAC design holds immense
potential, and we encourage further research in this pioneering domain.

## Author Contributions

YG conducted the initial literature review, with guidance from RM. YG prepared
the initial draft of the manuscript and figures. RM and YG engaged in
extensive discussions and iterations of writing and feedback throughout the
process. Both authors contributed significantly to the development and
refinement of the manuscript. Both authors have read and approved the final
version of the manuscript.

## Conflicts of interest

There are no conflicts to declare.

## Acknowledgements

YG and RM acknowledge the funding provided by the Wallenberg AI, Autonomous
Systems, and Software Program (WASP), supported by the Knut and Alice
Wallenberg Foundation. Some of the figures use icons from BioRender and
Flaticon.

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