# Multi-Objective Generative AI for Designing Novel Brain-Targeting Small
Molecules

Ayush Noori† & Iñaki Arango†
Harvard University
Cambridge, MA 02138, USA
{anoori<EMAIL_ADDRESS>
William E. Byrd
Hugh Kaul Precision Medicine Institute
Heersink School of Medicine
University of Alabama at Birmingham
Birmingham, AL 35233, USA
<EMAIL_ADDRESS>
Nada Amin
Harvard John A. Paulson School of Engineering and Applied Sciences
Cambridge, MA 02139, USA
<EMAIL_ADDRESS>
Correspondence<EMAIL_ADDRESS>$\dagger$ These authors contributed
equally to this work.
This work was accepted to the Generative and Experimental Perspectives for
Biomolecular Design Workshop at the 12th International Conference on Learning
Representations.

###### Abstract

The strict selectivity of the blood-brain barrier (BBB) represents one of the
most formidable challenges to successful central nervous system (CNS) drug
delivery, preventing the diagnosis and treatment of CNS disorders.
Computational methods to generate BBB permeable lead compounds in silico may
be valuable tools in the CNS drug design pipeline. However, in real-world
applications, BBB penetration alone is insufficient; rather, after transiting
the BBB, molecules must perform some desired function – such as binding to a
specific target or receptor in the brain – and must also be safe and non-toxic
for use in human patients. To discover small molecules that concurrently
satisfy these constraints, we use multi‑objective generative AI to synthesize
drug-like blood-brain-barrier permeable small molecules that also have high
predicted binding affinity to a disease-relevant CNS target. Specifically, we
computationally synthesize molecules with predicted bioactivity against
dopamine receptor D2, the primary target for almost all clinically effective
antipsychotic drugs. After training several graph neural network-based
property predictors, we adapt SyntheMol (Swanson et al., 2024), a recently
developed Monte Carlo Tree Search-based algorithm for antibiotic design, to
perform a multi‑objective guided traversal over an easily synthesizable
molecular space. We design a library of 26,581 novel and diverse small
molecules containing hits with high predicted BBB permeability and favorable
predicted safety and toxicity profiles, and that could readily be synthesized
for experimental validation in the wet lab. We also validate top scoring
molecules with molecular docking simulation against the D2 receptor and
demonstrate predicted binding affinity on par with risperidone, a clinically
prescribed D2-targeting antipsychotic. In the future, the SyntheMol-based
computational approach described here may enable the discovery of novel
neurotherapeutics for currently intractable disorders of the CNS.

##  1 Introduction

In this paper, we design small molecule drug candidates in a multi-objective
setting to transit the blood-brain-barrier (BBB) and target the human brain,
while also satisfying other therapeutically relevant constraints. As the
interface between the central nervous system (CNS) and peripheral blood
circulation, the BBB is responsible for regulating CNS homeostasis, protecting
the brain microenvironment, and preventing the infiltration of toxins or
pathogens across all levels of the neurovascular tree (Daneman & Prat, 2015;
Obermeier et al., 2013) (Figure S1). Although the strict selectivity of the
BBB is critical to protecting CNS health and integrity, it also represents the
most formidable challenge to successful CNS drug delivery in disease. The BBB
blocks the access of most drugs to the brain, including approximately 100% of
all large-molecule and 98% of all small-molecule neurotherapeutics, i.e.,
those with molecular masses greater than 400-500 kDa or with low lipid
solubilities (Pardridge, 2005; 2012). Further, the limited small molecule
drugs which penetrate the BBB only target select brain disorders; among these
are depression, schizophrenia, chronic pain, and epilepsy. Meanwhile, most CNS
conditions – including neurodegenerative disorders such as Alzheimer’s,
Parkinson’s, and Huntington’s diseases; multiple sclerosis; HIV-associated
neurocognitive disorders; brain cancer; neurotrauma; and cerebrovascular
disease – remain refractory to neurotherapeutic administration and often lack
effective treatment options (Pardridge, 2005). BBB-imposed transport
selectivity also prevents the uptake of radiotracers in positron emission
tomography (PET) and single photon emission computed tomography (SPECT)
studies of the brain, impeding the diagnosis of CNS disorders and hindering
neuroimaging-based disease staging.

Few strategies to evade or circumvent BBB regulation exist. Current approaches
include intracerebroventricular injection, intranasal administration, exosome
or nanoparticle-based delivery systems, and focused ultrasound (fUS) with
intravenous microbubble agents to cause localized BBB disruption (He et al.,
2018). However, these methods are frequently invasive with poor safety and
efficacy profiles and remain an area of active research. For example, nasal
epithelial barriers and the arachnoid membrane – which separates the nasal
submucosa and olfactory cerebrospinal fluid (CSF) – limit brain uptake of
drugs administered intranasally (Merkus et al., 2003), while fUS-mediated BBB
disruption may cause microhemorrhages, direct vascular rupture, ischemia,
edema, thermocoagulative necrosis, or injury by microbubble-induced mechanical
forces (Meng et al., 2019). Thus, the most reliable solution to drug targeting
in the brain is to engineer drug candidates which can cross the BBB without
secondary BBB disruption or evasion. To that end, tools to discover or design
CNS therapeutics that are BBB permeable with desirable pharmacokinetic
profiles would be valuable assets in the CNS drug discovery process.

Wet lab methods to produce BBB permeable drugs such as high-throughput drug
screening in BBB models have encountered practical challenges and achieved
limited success. As a basic model of the BBB, static monolayers of primary
endothelial cells or immortalized human endothelial cell lines grown in cell
culture inserts can be used to study signaling pathways or transporter
kinetics and may also be co-cultured with other BBB-associated cell types such
as astrocytes and pericytes (Avdeef et al., 2014; Bagchi et al., 2019).
However, such models fail to accurately replicate cell-to-cell signaling at
the BBB interface and do not account for shear stress generated by
physiological blood flow which regulates TJ formation and barrier function. In
response, iPSC-based, microfluidic, organ-on-chip, and multicellular organoid
models of the BBB continue to be developed (Bagchi et al., 2019). Drug
screening via these dynamic systems remains expensive and has yet to achieve
widespread acceptance as an effective assay of BBB integrity. Further, all
existing in vitro and in vivo models (e.g., the rodent brain) lack sufficient
throughput required of modern, automated drug discovery programs. That is, wet
lab approaches cannot be used to characterize BBB penetration at scale across
the vast, unexplored regions of the neurotherapeutic molecular space.

By contrast, in silico generation of BBB permeable drug candidates would allow
for the rapid, economical, and high-throughput screening of CNS drug
candidates, and may enable the discovery of novel neurotherapeutics for
currently intractable CNS disorders. However, in silico methods to generate
BBB-penetrant molecules alone are insufficient. In real-world applications,
after transiting the BBB, molecules must perform some desired function, such
as binding to a specific target or receptor in the brain. Molecules must also
be drug-like: they must be safe and non-toxic for use in human patients. To
discover small molecules that concurrently satisfy these constraints, we use
multi-objective generative AI to synthesize blood-brain-barrier permeable
small molecules that also feature other therapeutically relevant properties,
including safety, toxicity, and binding affinity to specific targets in the
CNS, such as the dopamine receptor D2.

##  2 Methodology

Due to the intricate dynamics of transport regulation across the BBB, the
complexity of drug safety and toxicity in the human body, and the large space
of potential CNS neurotherapeutics, the generation of BBB permeable drug-like
molecules is a task well-suited to machine learning (ML) (Butler et al.,
2018). By training on small molecules with known properties, supervised ML
algorithms attempt to learn inductive biases about the underlying biochemical
rules which govern BBB penetration capacity, which can then be leveraged to
guide a molecular generation process and propose candidate molecules that
satisfy bioactivity and pharmacokinetic constraints. Here, we sought to design
molecules that meet three such objectives:

  1. 1.

Molecules must be able to transit the BBB.

  2. 2.

Molecules must exhibit binding affinity to a specific target in the brain. In
this work, we select for binding affinity against the dopamine receptor D2
(D2R), however, we emphasize that our approach is modular. Should a different
target be of interest, our method can be adapted to generate small molecules
with bioactivity against this new target.

  3. 3.

Molecules must be safe and non-toxic.

For each objective, we obtained labeled data to act as a supervisory signal
for the training of an ML model, which we refer to as a “property predictor.”
Briefly, for BBB permeability, we used the Blood-Brain-Barrier Dataset (B3DB),
a curated resource of 4,956 BBB permeable (BBB+) and 2,851 BBB non-permeable
small molecules (Meng et al., 2021). For D2R binding affinity, we retrieved
8,034 D2R-active molecules and sampled 12,000 D2R-inactive molecules from the
ExCAPE-DB database (Sun et al., 2017). Finally, for safety and toxicity, we
retrieved a range of absorption, distribution, metabolism, excretion, and
toxicity (ADME-Tox) datasets from the Therapeutics Data Commons (TDC) (Huang
et al., 2021a; 2022).

Next, we trained graph neural networks on these datasets (see Appendix B) to
predict the properties of interest. Specifically, for each model, we used a
graph convolutional network encoder and concatenated with structured molecular
features to produce likelihood scores of BBB permeability or D2R bioactivity
(Figures 1A, S2). To evaluate out-of-sample model performance in a data-
efficient manner, property predictor models were trained with 10-fold cross-
validation, and the average prediction across all 10 folds was taken as the
final likelihood score. For the ADME-Tox property predictor, we use ADMET-AI,
an ML platform for ADME-Tox prediction trained on the TDC datasets (Swanson et
al., 2023b).

Finally, we combined the outputs of all three property predictors to guide a
search across the small molecule space. As described in Appendix B, we adapted
SyntheMol, a recently developed Monte Carlo Tree Search (MCTS)-based algorithm
for antibiotic design, to perform a multi-objective guided traversal over 29.6
billion molecules in the Enamine REadily AccessibLe (REAL) space (Swanson et
al., 2024; Grygorenko et al., 2020). Like in SyntheMol, in each iteration of
the algorithm, we perform an MCTS rollout to construct a molecule using the
132,479 commercially available molecular building blocks and 13 chemical
synthesis reactions; however, we then evaluate the molecule using both the BBB
permeability and D2R bioactivity property predictors. Using this search
strategy, we generated a molecular library of 26,581 small molecule drug
candidates with putative BBB permeability, D2R bioactivity, safety, and non-
toxicity, which we characterized to evaluate the success of our method.

##  3 Results and Discussion

To predict BBB permeability, D2R bioactivity, and ADME-Tox characteristics,
three separate property predictors were used, trained on the B3DB, pre-
processed ExCAPE-DB, and TDC databases, respectively (Meng et al., 2021; Sun
et al., 2017; Huang et al., 2021a; Swanson et al., 2023b). Encouragingly, our
BBB and D2R property predictor models demonstrated strong out-of-sample
performance, as visualized by AUROC curves in Figure S3. The BBB predictor
achieved greater than 0.95 AUC across all folds, while the D2R bioactivity
predictor achieved near-perfect performance of 1.00 AUC, again across all
folds. We highlight the performance of the first cross-validation fold for the
BBB and D2R predictors (Figure S3A and S3B) and also show the performance
across a set of randomly sampled folds for both models (Figures S3C, S3D).
These results suggest that our trained models are likely useful oracles for
predicting both BBB permeability and binding affinity to D2R.

Guided by these high-performing property predictors, we performed a SyntheMol-
based MCTS search over 29.6 billion molecules in the pre-processed subset of
the Enamine REAL combinatorial molecular space to generate a molecular library
of 26,581 small molecules over 20,000 MCTS rollouts (Swanson et al., 2024;
Grygorenko et al., 2020). First, we investigated diversity in building blocks
and reactions sampled by the SyntheMol-based search. Across all 26,581
molecules, no individual molecular fragment was employed more than 27 times;
that is, at most, any individual building block will be contained in only
0.102% of the molecular library (Figure 1B). Further, all 13 chemical
reactions are employed in the search process, and 11 of the 13 chemical
reactions are used to generate more than 300 molecules (Figure 1C).

Figure 1: Results of SyntheMol-based guided Monte Carlo Tree Search for multi-
objective small molecule design. Guided by predictors of BBB permeability, D2R
binding, and toxicity, MCTS was performed over 29.6 billion compounds to
generate a library of 26,581 molecules.

Next, we explored the similarity of the generated molecules to the labeled
molecules in the B3DB (Figure 1D) and ExCAPE-DB training (Figure 1E) data, for
the BBB permeability and D2R bioactivity prediction tasks, respectively. For
each molecule in the library, we computed the maximum Tversky similarity (an
asymmetric similarity measure) between the Morgan fingerprints of that
molecule and the fingerprints of all other molecules in each of the training
sets. We consider a Tversky similarity score greater than 0.9 to be
undesirable, and find that the 73.61% and 99.47% of the molecules have
similarity scores less than 0.9 against both the B3DB (Figure 1D) and ExCAPE-
DB (Figure 1E) datasets, respectively. The diversity of generated molecules
was more preferable for ExCAPE-DB as compared to B3DB, which was expected due
to the increased size and diversity of the ExCAPE-DB database as compared to
B3DB. These results indicate that the SyntheMol-based search did not merely
memorize and reproduce molecules from the training data.

We also characterized the structural diversity within the 26,581 compounds
that comprise our molecular library (Figure 1F). For each molecule in the
library, we computed the maximum Tanimoto distance (a symmetric distance
metric) between the Morgan fingerprints of that molecule and the fingerprints
of all other molecules in the library. We find that 73.61% of the molecules
have an internal similarity less than 0.8, and 99.29% of the molecules have an
internal similarity less than 0.9. These results suggest that the molecular
library generated here is diverse and amenable to experimental screening; that
is, the SyntheMol-based search did not produce a structurally homogeneous
library. Our results also align with those of Swanson et al. (2024), even
though the prior work focused on antibiotic discovery for infectious diseases
and bacterial pathogens, while our synthesis efforts concern a wholly
different biological domain of neurotherapeutics for CNS disorders.

Finally, we use the trained BBB, D2R, and ADME-Tox property predictors to
evaluate each of the molecules in the library and produce an aggregate
likelihood score $\alpha$ of each biomolecular property (Figure 1H). We
visualize the average joint likelihood and compare against an unguided MCTS
baseline where building blocks were randomly sampled (Figure 1G). Not all
molecules are predicted as BBB permeable, active against D2R, and non-toxic –
indeed, there is a peak at $\alpha=0.55$, suggesting that many molecules are
predicted to have some, but not all, properties of interest. However, in the
guided search, there is a second peak at $\alpha=0.87$: 2904 molecules have
$\alpha>0.65$ and 1662 molecules have $\alpha>0.80$. By contrast, in the
unguided random baseline, only 573 molecules have $\alpha>0.65$ and only 224
molecules have $\alpha>0.80$. Thus, our multi-objective SyntheMol-based guided
Monte Carlo Tree Search synthesized high-scoring molecules at a 5.07-fold and
7.42-fold higher hit rate for $\alpha>0.65$ and $\alpha>0.80$, respectively,
as compared to an unguided baseline.

We further validated top scoring generated molecules with protein-ligand
molecular docking simulation using AutoDock Vina (Trott & Olson, 2010;
Eberhardt et al., 2021; Murail et al., 2021). We retrieved the crystal
structure of the dopamine receptor D2 (Protein Data Bank (PDB) ID: 6cm4) (Wang
et al., 2018), and simulated the binding of D2R against the top 5 generated
molecules ranked by D2R property predictor score, as well as the bottom 5
scoring molecules and risperidone (a second generation antipsychotic
prescribed for the treatment of bipolar disorder and schizophrenia which acts
by binding to D2R) as negative and positive controls, respectively. After
docking simulation, the predicted binding affinity of risperidone was -11.40
kcal/mol, comparable to the mean predicted affinities of the top scoring
molecules at -11.50 kcal/mol (Figure 1I, error bars show mean $\pm$ SE). By
contrast, under simulation, the bottom scoring molecules bound more weakly,
with mean predicted affinities of -6.36 kcal/mol. These results indicate that
our SyntheMol-based molecular design strategy discovered small molecules with
strong predicted binding affinity to the dopamine receptor D2 as well as high
predicted BBB permeability and non-toxicity. These compounds may represent
early drug candidates for challenging CNS conditions such as schizophrenia,
Parkinson’s disease, and substance abuse disorders, and could merit further
investigation and experimental characterization in the wet lab.

###  3.1 Future directions

Various future directions exist to extend this work, address the limitations
of this proof-of-concept study, and validate our findings. First, when
training property predictors, rather than a uniform, random split, a scaffold
split could be used to partition molecules based on their Bemis-Murcko
scaffolds (Wu et al., 2018; Landrum et al., 2022). Such two-dimensional
scaffolds simplify the chemical structure of a compound by ignoring side
chains, atom type, hybridization, and bond order; only ring systems are
preserved (Bemis & Murcko, 1996). By ensuring greater structural diversity
among the training and test sets, scaffold splitting would improve the out-of-
distribution generalization capacity of our trained property predictors and
likely increase the quality of our generated molecular library. Other areas of
improvement include evaluation of different neural architectures in the
encoder of our property predictor models (e.g., graph transformers), addition
of DeepPurpose-based ADME-Tox predictors over ADMET-AI (Huang et al., 2021b),
provisioning of additional computational resources to enable more MCTS
rollouts and and a larger generated library, logic-based integration of the
individual property predictor outputs in $P(N)$ rather than taking the average
score, and parallelization of the MCTS search to improve performance and
increase coverage of the MCTS rollouts over the Enamine REAL space (see
Appendix B). In particular, the use of Transformer-based encoders would allow
us to identify functional groups of synthesized molecules with the highest
attention weights that may be most relevant to classification of BBB
permeability and could inform future rational drug design efforts in medicinal
chemistry or suggest novel mechanisms for BBB transport and uptake into the
brain.

Nonetheless, these results establish groundwork in multi-objective in silico
discovery of novel CNS therapeutic candidates. By training multiple property
predictors to drive a search over the Enamine REAL space, we jointly optimized
for several important drug prerequisites – namely, BBB permeability, toxicity,
synthesizability, and bioactivity against the important dopamine receptor D2.
Further, we note that the method described here could be used to discover
small molecules with binding affinity to targets other than D2R. Although D2R
is indeed a disease-relevant target of high priority, we emphasize that our
approach is modular and not restricted to D2R or the conditions in which D2R
is implicated. Rather, by leveraging the 70,850,163 bioactivity annotations in
the chemogenomics database ExCAPE-DB, we could feasibly train predictors of
bioactivity against 1666 additional protein targets beyond D2R (Sun et al.,
2017). Coupled with SyntheMol-based guided search, this strategy would allow
us to develop molecular libraries relevant to a range of other conditions that
could again be validated by molecular docking simulation. The approach
advanced here could be used to profile unexplored regions of the CNS
neurotherapeutic space and suggest new drug and radiotracer candidates to
better diagnose and treat CNS disease. Finally, the most exciting and urgent
future direction of this work is experimental validation in the wet lab. We
are eager to partner with experimentalists to evaluate our molecular library
in both in vitro and in vivo models of schizophrenia and Parkinson’s disease,
in an effort to work towards pharmacological interventions for these currently
intractable neurological disorders.

#### Code Availability

Code is available via GitHub at https://github.com/ayushnoori/molecule-
synthesis.

#### Acknowledgments

We gratefully acknowledge Kyle Swanson and James Zou for their support and
collaboration, allowing us to extend their prior work. K.S. and J.Z. shared an
unpublished copy of their manuscript with us, “Generative AI for designing and
validating easily synthesizable and structurally novel antibiotics”; this work
is now published at the Conference on Neural Information Processing Systems
(NeurIPS) 2023 Generative AI and Biology Workshop and in Nature Machine
Intelligence. K.S. and J.Z. also provided us with early access to their code
base, now available at https://github.com/swansonk14/SyntheMol on GitHub. We
thank K.S. and J.Z. for their critical review of our manuscript and for their
constructive feedback. We also thank Venkatesh Murthy and Tyler Holloway for
valuable discussions.

Figure S1 was created with BioRender.com.

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##  Appendix A Blood-Brain-Barrier

The blood-brain-barrier (BBB) is comprised of specialized endothelial cells
and mural cells which form the continuous nonfenestrated vasculature of the
brain and spinal cord. Unlike non-CNS endothelium, various barrier functions
of the cerebral capillary endothelial cells allow selective transcellular but
not paracellular transport across the BBB, which allows close regulation of
molecular traffic entering the brain (Abbott et al., 2006). These functions
include:

  1. 1.

Physical barriers, such as continuous intercellular tight junctions (TJs)
formed of occludin, claudins, tricellulins, and junctional adhesion molecules;
other junctional complexes, such as adherens and gap junctions; the lack of
membrane fenestra, or pores; and low rates of transcellular vesicle
trafficking and other mechanisms of transcytosis (Figure S1) (Abbott et al.,
2010; Langen et al., 2019; Stamatovic et al., 2016).

  2. 2.

Molecular barriers, such efflux transporters to counteract the diffusion of
lipophilic substances (e.g., drugs or xenobiotics; see Figure S1) and low
expression of leukocyte adhesion molecules (LAMs) to hinder leukocyte
extravasation into the CNS parenchyma (Daneman & Prat, 2015).

  3. 3.

Metabolic barriers, including intracellular (e.g., monoamine oxidase and
cytochrome P450) and extracellular (e.g., peptidases and nucleotidases) drug-
metabolizing enzymes (Abbott et al., 2006; Minn et al., 1991).

  4. 4.

Extravascular barriers formed by other cell types of the neurovascular unit
(NVU), such as astrocytes and pericytes – which are embedded in the basement
membrane of the abluminal surface of the BBB endothelium (Obermeier et al.,
2013). For example, extravascular structures such as the endothelial
glycocalyx and astrocyte endfeet (Figure S1) may inhibit the delivery of large
molecules across the BBB (Kutuzov et al., 2018). Further, ECM receptors in the
basement membrane, including dystroglycans and integrins, mediate cell-matrix
adhesion and influence barrier function (e.g., by regulating TJ organization)
(Langen et al., 2019).

These barrier systems prevent the passive (i.e., transendothelial) diffusion
of all but small gaseous molecules (e.g., O2 and CO2) and certain lipophilic
agents (e.g., barbiturates, ethanol) across the BBB (Figure S1) (Abbott et
al., 2006); all other transport is restricted to facilitated or active
pathways. These pathways – which include carrier-mediated transport (i.e.,
ATP-independent solute carriers) and receptor-mediated transcytosis –
provision select nutrients and metabolites to satisfy the energetic and
chemical requirements of the brain (Campos-Bedolla et al., 2014). Nutrient
delivery notwithstanding, the capillaries of the BBB remain the least
permeable in the human body.

##  Appendix B Supplemental Methods

###  B.1 Training Data

The labeled datasets used to train property predictors for each design
objective are described below.

Blood-brain-barrier permeability. To train a property predictor for BBB
permeability, we use the Blood-Brain-Barrier Dataset (B3DB), a curated
resource of 7,807 small molecules classified as either BBB permeable (BBB+) or
BBB non-permeable (BBB-), with 4,956 BBB+ and 2,851 BBB- molecules included
(Meng et al., 2021). BBB permeability is measured the logarithm of the brain-
plasma concentration ratio:

$\displaystyle\log{\text{BB}}$
$\displaystyle=\log{\frac{C_{\text{brain}}}{C_{\text{blood}}}}$

Although numerical data is included for 1,058 of the 7,807 molecules in the
dataset, we seek to produce a property predictor that can be trained across a
maximally diverse set of molecules to guide the search across a similarly
diverse and much larger space of possible small molecules. Therefore, we train
a supervised classifier to predict the binary BBB+ or BBB- label, where
$\log{\text{BB}}>0=$ BBB+ and $\log{\text{BB}}<0=$ BBB-. To represent each
molecule, we use the simplified molecular-input line-entry system, or SMILES,
a specification for describing the structure of chemical species using short
ASCII strings (Figure S2A) (Weininger, 1988). The final dataset included the
SMILES representation of each molecule and binary classification label (i.e.,
0 for BBB- and 1 for BBB+).

Bioactivity against target of interest. To discover molecules that exhibit
binding affinity to a specific target in the brain, we use ExCAPE-DB, a large-
scale database of chemogenomics data that includes 70,850,163 bioactivity
annotations of 998,131 unique chemical compounds against 1667 protein targets
(Sun et al., 2017). ExCAPE-DB was assembled from the PubChem (Kim et al.,
2023) and ChEMBL (Gaulton et al., 2012; Bento et al., 2014) databases, and is
a valuable resource for quantitative structure-activity relationship (QSAR)
modeling. Of note, ExCAPE-DB is already filtered for drug-like compounds;
therefore, all molecules in the database are organic (i.e., lack metal atoms),
have molecular weights less than 1,000 Da, and have more than 12 heavy atoms.
We download Version 2.0 of ExCAPE‑DB from the Zenodo repository at DOI:
10.5281/zenodo.2543724.

In this work, we select the dopamine receptor D2 (D2R), encoded by the DRD2
gene in humans, as our target of interest. D2R is one of a family of five
G-protein-coupled receptors that facilitate the function of the catecholamine
neurotransmitter dopamine; D2R does so by coupling to the inhibitory G protein
Gi/o, likely to regulate presynaptic firing rate (Zhuang et al., 2021;
Beaulieu & Gainetdinov, 2011). D2R is the primary target for almost all
clinically effective antipsychotic drugs and its function can be affected by
binding of both agonist or antagonist ligands, which activate or inhibit D2R,
respectively. Importantly, D2R signaling has been implicated in various
neurological and neuropsychiatric diseases, including schizophrenia, tardive
dyskinesia, attention-deficit hyperactivity disorder, Tourette’s syndrome,
Parkinson’s disease, and substance abuse disorders (Beaulieu & Gainetdinov,
2011; Kostrzewa et al., 2018; Di Chiara et al., 2004; Volkow & Morales, 2015;
Seeman, 2006). Further, D2R is expressed across the brain, with highest
expression in the striatum, nucleus accumbens, and olfactory tubercle, but
also in the substantia nigra, ventral tegmental area, hypothalamus, cortical
regions, septum, amygdala, and hippocampus (Beaulieu & Gainetdinov, 2011).
Therefore, the discovery of novel small molecules that can bind to and
modulate the function of D2R may be important tools in the clinical arsenal to
restore homeostatic dopaminergic signaling in challenging disorders of the
CNS.

In line with previous work (Olivecrona et al., 2017; Yang et al., 2020), the
ExCAPE-DB database was subset for molecules with bioactivity data against D2R.
After filtering for duplicate molecules by canonical SMILES structure, 8,034
active molecules and 342,204 inactive molecules remained. Bioactivity is often
measured by $\text{pIC}_{50}$, where $\text{IC}_{50}$ is a measure of the
concentration of a chemical species required to inhibit a biological target in
vitro, and $\text{pIC}_{50}=-\log_{10}(\text{IC}_{50})$. In ExCAPE-DB,
compounds were classified as active against a specific target if
$\text{pIC}_{50}>5$ and inactive if $\text{pIC}_{50}<5$. However, numerical
data was only available for 293 of the 342,204 inactive compounds; therefore,
we train a supervised classifier to predict the binary active or inactive
label. We randomly sample 12,000 compounds from the 342,204 inactive
compounds, to produce a final dataset of approximately 20,000 molecules with
labeled bioactivity against D2R. Like before, the final dataset included the
SMILES representation of each molecule and binary classification label (i.e.,
0 for inactive and 1 for active).

Safety and toxicity. To predict safety and toxicity, we retrieve data from the
Therapeutics Data Commons, a centralized resource of curated datasets and
benchmarks for the development of AI models for therapeutics tasks (Huang et
al., 2021a; 2022). We retrieve a range of absorption, distribution,
metabolism, and excretion (ADME) datasets as well as several toxicity
datasets. ADME-related benchmarks include including data on human intestinal
absorption and permeability (Wang et al., 2016a; Siramshetty et al., 2021; Hou
et al., 2007), absorption-related P-glycoprotein inhibition (Broccatelli et
al., 2011), bioavailability (Ma et al., 2008), lipophilicity (Wu et al.,
2018), solubility (Sorkun et al., 2019), hydration free energy (Wu et al.,
2018; Mobley & Guthrie, 2014), plasma protein binding rate, volume of
distribution at steady state (Lombardo & Jing, 2016), inhibition of various
metabolism-related cytochrome P450 genes (Veith et al., 2009; Carbon-Mangels &
Hutter, 2011; Cheng et al., 2012), half-life (Obach et al., 2008), and
clearance (Di et al., 2012). Toxicity-related benchmarks include data on acute
toxicity (Richard et al., 2016; Zhu et al., 2009), blockade of the human
ether-à-go-go related gene (hERG) channel responsible for coordinating
cardiovascular activity (Wang et al., 2016b; Du et al., 2011; Karim et al.,
2021), mutagenicity (Xu et al., 2012), drug-induced liver injury (Xu et al.,
2015), chemically-induced skin reaction (Alves et al., 2015), carcinogenicty
(Cheng et al., 2012; Lagunin et al., 2009), and toxicity-related clinical
trial failures (Gayvert et al., 2016). All datasets were retrieved in a
harmonized format from the Therapeutics Data Commons.

###  B.2 Property Predictors

Feature engineering. To guide search over the molecular space, computational
prediction of molecular properties was facilitated by encoding the chemical
space into mathematical descriptors. First, we used RDKit, an open-source
cheminformatics toolkit, to compute a global pharmacophore descriptor
represented as 200-element vector from the SMILES representations of each
molecule (Figure S2A) (Landrum et al., 2022). Next, the SMILES representations
of each molecule in the training data were converted to molecular graphs,
where nodes correspond to atoms and edges correspond to chemical bonds. Based
on the molecular graphs, we then created molecular fingerprints, or vector
representations of the structural properties of each molecule which encode the
local chemical environment of a molecule by iteratively applying a hashing
function to molecular substructures (Cereto-Massagué et al., 2015). In
particular, we use the Morgan fingerprint, also known as the extended-
connectivity fingerprint ECFP4, a 1024-bit vector where bits are assigned
based on the presence of circular substructures around each atom in a molecule
(Morgan, 1965). We use a radius of 2; therefore, the hashing function is
applied to all substructures within 2 bonds of each atom in the molecule.

Graph neural networks. To predict blood-brain-barrier permeability and D2R
bioactivity, two separate property predictors were trained (Figure S2A). As in
Swanson et al. (2024), using the Chemprop framework, we developed graph neural
networks (GNNs) with three layers of message-passing, followed by a two-layer
feed-forward neural network that combines the output of the GCN with the
200-element RDKit chemical descriptor to predict the likelihood of either
blood-brain-barrier permeability or D2R bioactivity. We included the RDKit
chemical descriptors based on our observation that structured features like
molecular weight are correlated with blood-brain-barrier permeability, as
measured by correlation with numerical ratio for 1,058 of the 7,807 molecules
in the B3DB dataset (Figure S2B and S2C). To evaluate out-of-sample model
performance while maximizing usage of the available training data, models were
trained with 10-fold cross-validation with 30 epochs of training per fold and
80%-10%-10% split across training, validation, and test sets, respectively.
The average prediction across all 10 folds was taken as the final likelihood
score, and performance was visualized in area under the receiver operating
characteristic (AUROC) curves (Figure S3).

Next, to predict absorption, distribution, metabolism, excretion, and
toxicity, we evaluated two approaches. First, we used the DeepPurpose library
to train our own individual property predictors for each of the ADME and
toxicity (ADME-Tox) datasets described in the “Training Data” section.
DeepPurpose is a library for drug-target interaction prediction that supports
15 compound and protein encoders and over 50 neural architectures (Huang et
al., 2021b). We trained 2-layer message passing GNNs on the molecular graph of
each compound to predict ADME-Tox features. Models were trained for 30 epochs,
with a learning rate of and a batch size of 128. After training, an aggregate
model was assembled to take as input a single molecular query and output the
results of the ADME-Tox models in a single concatenated vector. We also
considered ADMET-AI, a recently developed ML platform for ADME-Tox property
prediction trained on the TDC datasets described above (Swanson et al.,
2023b). For each molecule, computed the weighted average of the ADMET-AI
prediction scores (with a weight of 1 for non-toxic properties and -1 for
toxic properties), and linearly scaled the scores from 0 to 1. In this work,
we use ADMET-AI as the ADME-Tox property prediction oracle; the inclusion of
our own DeepPurpose-based ADME-Tox predictors represents an interesting avenue
for future work.

###  B.3 SyntheMol-based Monte Carlo Tree Search

After training the property predictors, we adapt SyntheMol, a recently
developed Monte Carlo Tree Search (MCTS)-based algorithm for antibiotic
design, to perform a multi-objective guided traversal over an easily
synthesizable molecular space (Swanson et al., 2023a; 2024). SyntheMol was
designed to overcome a key challenge in generative AI for molecular design:
most generative models for de novo molecular design often produce compounds
that are intractable to synthesize in the wet lab, and thus cannot be
validated experimentally (Gao & Coley, 2020). To ensure synthesizability,
SyntheMol assembles compounds using a pre-processed subset of the Enamine
REadily AccessibLe (REAL) Space, composed of 132,479 commercially available
molecular building blocks with known reactivities and 13 chemical synthesis
reactions (Grygorenko et al., 2020). The resulting combinatorial chemical
search space spans 29.6 billion molecules that are tractable to synthesize
experimentally.

In this work, we adapt the SyntheMol algorithm in Swanson et al. (2024) to
perform multi-objective molecular search guided by the property prediction
models for BBB permeability, D2R bioactivity, and ADME-Tox described above.
Like in SyntheMol, in each iteration of the algorithm, we perform an MCTS
rollout to construct a molecule using the 132,479 building blocks and 13
chemical reactions; however, we then evaluate the molecule using both the BBB
permeability and D2R bioactivity property predictors.

In the MCTS search, molecular building blocks and their possible combinations
via chemical synthesis reactions are represented by nodes in a synthesis tree.
During the MCTS rollouts, each potential child node $N$ is evaluated by a
scoring function that balances both exploitation of nodes that lead to high
scoring molecules as well as exploration of nodes that have rarely been
visited during the search. We use the SyntheMol scoring function as described
in Swanson et al. (2024); however, we adapt the molecular property score
$P(N)$ to perform guided molecular search in a multi-objective setting by
concurrently using the property predictors for BBB permeability, D2R
bioactivity, and ADME-Tox. Let $M_{\text{BBB}}$ be the predictor of BBB
permeability, let $M_{\text{D2R}}$ be the predictor of D2R bioactivity, and
let $M_{\text{Tox}}$ be the ADME-Tox predictor. Then, $P(N)$ is given by:

$\displaystyle
P(N)=\frac{1}{|N_{\text{mols}}|}\sum_{i=1}^{|N_{\text{mols}}|}{\frac{M_{\text{BBB}}(N^{i}_{\text{mols}})+M_{\text{D2R}}(N^{i}_{\text{mols}})+M_{\text{Tox}}(N^{i}_{\text{mols}})}{3}}$

Here, $N^{i}_{\text{mols}}$ is the $i$-th molecule in node $N$ (in case
reactions produce more than 1 molecule). This approach is also inspired by
MolSearch, a multi-objective MCTS-based method for molecular generation with a
two-stage design which alternates between optimizing for biological properties
and optimizing for non-biological properties, instead of taking the average
across the property predictors as we do here (Sun et al., 2022).

We use this SyntheMol-based guided search strategy to produce a molecular
library of 26,581 novel and diverse molecules. To characterize the library, we
visualize the distribution of molecular building blocks and chemical synthesis
pathways employed. We also investigate the molecular diversity of the library
using maximum internal Tanimoto similarity as well as maximum Tversky
similarity with the training data. Finally, we evaluate each of the molecules
using both the BBB and D2R predictors, compare the average score to an
unguided random sampling baseline, and validate top scoring hits with protein-
ligand molecular docking simulation against D2R.

##  Appendix C Supplemental Figures

Figure S1: Transport and barrier functions of the CNS epithelium. Physical,
molecular, metabolic, and extravascular barriers of the BBB regulate access to
the CNS milieu. Created using Biorender.com. Figure S2: Model architecture of
property predictors. To predict blood-brain-barrier permeability and D2R
bioactivity, two separate property predictors were trained. (A) First, the
SMILES representations of each molecule in the training data were converted to
molecular graphs; for example, the SMILES structure and molecular graph of
moxalactam, a third-generation cephalosporin antibiotic in the B3DB database,
are shown here. Next, we developed graph neural networks (GNNs) with three
layers of message-passing, followed by a two-layer feed-forward neural network
that combined the output of the GCN with a 200-element RDKit chemical
descriptor to predict the likelihood of either blood-brain-barrier
permeability or D2R bioactivity. The structured chemical descriptors are
included based on the observation that properties like (B) molecular weight
and (C) number of heavy atoms are correlated with blood-brain-barrier
permeability, as measured by correlation with the numerical blood-brain-
barrier concentration ratios for 1,058 of the 7,807 molecules in the B3DB
dataset for which the data was available. Figure S3: Property predictor
performance. To evaluate out-of-sample model performance while maximizing
usage of the available training data, property predictor models were trained
with 10-fold cross-validation with 30 epochs of training per fold and
80%-10%-10% split across training, validation, and test sets, respectively.
The average prediction across all 10 folds was taken as the final likelihood
score, and model performance on the independent test set was visualized in
area under the receiver operating characteristic (AUROC) curves. Here, we
highlight the performance of the first cross-validation fold for the (A)
blood-brain-barrier permeability predictor and (B) the dopamine receptor D2
bioactivity predictor. We also show the performance across a set of randomly
sampled folds for both the (C) BBB and (D) D2R predictors.