# Artificial Intelligence based Autonomous Molecular Design for Medical
Therapeutic: A Perspective

Rajendra P. Joshi  Neeraj Kumar∗<EMAIL_ADDRESS>Pacific Northwest
National Laboratory, Richland, WA 99352, United States

###### Abstract

Domain-aware machine learning (ML) models have been increasingly adopted for
accelerating small molecule therapeutic design in the recent years. These
models have been enabled by significant advancement in state-of-the-art
artificial intelligence (AI) and computing infrastructures. Several ML
architectures are predominantly and independently used either for predicting
the properties of small molecules, or for generating lead therapeutic
candidates. Synergetically using these individual components along with robust
representation and data generation techniques autonomously in closed loops
holds enormous promise for accelerated drug design which is a time consuming
and expensive task otherwise. In this perspective, we present the most recent
breakthrough achieved by each of the components, and how such autonomous AI
and ML workflow can be realized to radically accelerate the hit identification
and lead optimization. Taken together, this could significantly shorten the
timeline for end-to-end antiviral discovery and optimization times to weeks
upon the arrival of a novel zoonotic transmission event. Our perspective
serves as a guide for researchers to practice autonomous molecular design in
therapeutic discovery.

††journal: ArXiv

##  1 Introduction

Synthesizing and characterizing small molecules in a laboratory with desired
properties is a time consuming task[1]. Until recently, experimental
laboratories are mostly human operated; they relied completely on the experts
of the field to design experiments, carry out characterization, analyze,
validate, and conduct decision making for the final product. Moreover, the
experimental process involves a series of steps, each requiring several
correlated parameters that need to be tuned[2, 3], which is a daunting task,
as each parameter set conventionally demands individual experiments. This has
slowed down the discovery of high impact small molecules and/or materials, in
some case by decades, with possible implications for diverse fields such as in
energy storage, electronics, catalysis, drug discovery etc. Besides, the high
impact materials of today come from exploring only a fraction of the known
chemical space. Larger portions of the chemical space are still uncovered and
it is expected to contain exotic materials with the potential to bring
unprecedented advances to state-of-art technologies. Exploring such a large
space with conventional experiments will take time and lot of resources[4, 5,
6, 7]. In this scenario, complete automation of laboratories is long overdue
and have been used with limited success in the past[8, 9, 10, 11].

Automating the computational design of molecules that integrates physics-based
simulations and optimization with ML approaches are a feasible and efficient
alternative instead; it can contribute significantly in accelerating
autonomous molecular design. High throughput quantum mechanical calculations
such as efficient density functional theory (DFT) based simulations are the
first step towards this goal of providing insight into larger chemical space
and have shown some promise to accelerate novel molecule discovery. However,
it still requires human intelligence for different decision-making processes
and it cannot autonomously guide small molecule therapeutic discovery steps,
thus slowing down the entire process. Additionally, inverse design of
molecules is notoriously difficult with DFT alone. The amount of data produced
by these high throughput methods is so large that it cannot be analyzed in
real time with conventional methods. Autonomous computational design and
characterization of molecules is more important in the scenarios where
existing experimental/computational approaches are inefficient[12, 13]. One
particular example is the challenge associated with identifying new
metabolites in a biological sample from mass spectrometry data, which requires
mapping the fragmented spectra of novel molecules to the existing spectral
library making it slow and tedious. In many cases, such references libraries
do not exist, and a machine learning integrated, automated workflow could be
an ideal choice to deploy for rapid identification of metabolites as well as
to expand the existing libraries for future reference. Such a workflow has
shown the early ability to quickly screen molecules and accurately predict
their properties for different applications. The synergistic use of high
throughput methods in a closed loop with machine learning based methods,
capable of inverse design, is considered vital for autonomous and accelerated
discovery of molecules[11]. In this perspective, we discuss how computational
workflows for autonomous molecular design can guide the goal of laboratory
automation and review the current state-of-the art artificial intelligence
(AI) guided autonomous molecular design focusing mainly on small molecule
therapeutic discovery.

##  2 Components of Computational Autonomous Molecular Design Workflow

The workflow for computational autonomous molecular design (CAMD) must be an
integrated and closed loop system with (i) efficient data generation and
extraction tools, (ii) robust data representation techniques, (iii) physics
based predictive machine learning models, and (iv) tools to generate new
molecules using the knowledge learned from steps i-iii. Ideally, an autonomous
computational workflow for molecule discovery would learn from its own
experience and adjust its functionality as the chemical environment or the
targeted functionality changes. This can be achieved when all the components
work in collaboration with each other, providing feedback while improving
model performance as we move from one step to other.

Figure 1: Closed loop workflow for computational autonomous molecular design
(CAMD) for medical therapeutics. Individual component of the workflow are
labelled. It consists of data generation, feature extraction, predictive
modeling and an inverse molecular design engine.

For data generation in CAMD, high-throughput density functional theory
(DFT)[14, 15] is a common choice mainly because of its reasonable accuracy and
efficiency[16, 17]. In DFT, we feed in 3D-structures to predict the properties
of interest. Data generated from DFT simulations is processed to extract the
more relevant data, which is then either used as input to learn the
representation[18, 19], or as a target required for the ML models[20, 21].
Data generated can be used in two different ways i.e. to predict the
properties of new molecules using a direct supervised ML approach, and to
generate new molecules having desired properties of interest using inverse
design. CAMD can be tied with supplementary components such as databases to
store the data and visualize it. The AI-assisted CAMD workflow presented here
is the first step in developing automated workflows for molecular design. It
will only accelerate the lead optimization. These workflows, in principle, can
be combined with experimental setups for computer aided synthesis planning
that includes synthesis and characterization tools at the cost of an increase
in the complexity and expense. Instead, experimental measurements and
characterization should be performed for only the lead compounds obtained from
CAMD.

The data generated from inverse design in principle should be validated by
using an integrated DFT method for the desired properties, or by docking with
a target protein to find out its affinity in the closed loop system, then
accordingly update the rest of the CAMD. These steps are then repeated in a
closed loop, thus improving and optimizing the data representation, property
prediction and new data generation component. Once we have confidence in our
workflow to generate valid new molecules, the validation step with DFT can be
bypassed or replaced with an ML predictive tool to make the workflow
computationally more efficient. In the following, we briefly discuss the main
component of the CAMD, while reviewing the recent breakthroughs achieved.

##  3 Data Generation and Molecular Representation

Machine learning models are data centric. Lack of accurate and well curated
data is the main bottleneck limiting their use in many domains of physical and
biological science. For some sub-domains, a limited amount of data exists that
comes mainly from physics-based simulations in databases[22, 23], or from
experimental databases such as NIST[24]. For other fields such as for bio-
chemical reactions[25], we have databases with the free energy of reactions,
but they are obtained with empirical methods, which are not considered ideal
as ground truth for machine learning models. For many domains, accurate and
curated data does not exist. In these scenarios, slightly unconventional yet
very effective approaches of creating data from published scientific
literature and patents for ML have recently gained adoption[26, 27, 28, 29].

Robust representation of molecules is required for accurate functioning of the
machine learning models[30]. An ideal representation of molecules should be
unique, invariant, invertible, efficient to obtain, and should capture the
physics, chemistry and structural motif of the molecules. Some of these can be
achieved by using all the physical, chemical and structural properties[31],
which all together are rarely well documented so getting this information is
considered cumbersome task. Over time, this has been tackled by using several
alternative approaches that work well for specific problems[32, 33, 34, 35,
36, 37]. However, obtaining robust representations of molecules for diverse
machine learning problems is still a challenging task and any gold standard
method that works consistently for all kind of problems is yet to be known.
Molecular representations used in the literature falls into two broad groups,
(a) 1D and/or 2D representations designed by experts using domain specific
knowledge including properties from the simulation and experiments, and (b)
iteratively learned molecular representations directly from the 3D nuclear
coordinates/properties within ML frameworks.

Figure 2: Different form of molecular representation used in the literature
for predictive and generative modeling.

Expert engineered molecular representations have been extensively used for
predictive modeling in the last decade which includes properties of the
molecules[38, 39], structured text sequences[40, 41, 42] (SMILES, InChI),
molecular fingerprints[43] among others. Such representations are carefully
selected for each specific problem using domain expertise, a lot of resources,
and time. The SMILES representation of molecules is the main workhorse as a
starting point for both representation learning as well as for generating
expert engineered molecular descriptors. For the latter, SMILES strings can be
used directly as one hot encoded vector, to calculate fingerprints or to
calculate the range of empirical properties using different open source
platforms such as RDkit[44], chemaxon[45] thereby by-passing expensive
features generation from quantum chemistry/experiments by providing faster
speed and diverse properties, including 3D-coordinates, for molecular
representations. Moreover, they can be easily converted into 2D-graphs, which
is preferred choice to date for generative modelling, where molecules are
treated as graphs with nodes and edges. Although significant progress has been
made in molecular generative modeling using mainly SMILES strings[40], they
often lead to generation of syntactically invalid molecules and are non-
unique. In addition, they are also known to violate fundamental physics and
chemistry based constraints[46, 47]. Case specific solutions to circumvent
some of these problems exist, but a universal solution is still unknown.
Extension of SMILES were attempted by more robustly encoding rings and
branches of molecules to find more concrete representations with high
semantical and syntactical validity using canonical SMILES[48, 49], InChI [41,
42], SMARTS[50], DeepSMILES[51], DESMILES[52] etc. More recently, Alan et al.
proposed 100 % syntactically correct and robust string based representation of
molecules known as SELFIES[46], which has been increasingly adopted for
predictive and generative modelling[53].

Recently, molecular representations that can be iteratively learned directly
from molecules have increasingly gained adoption, mainly for predictive
molecular modeling achieving chemical accuracy for range of properties[31, 54,
55]. Such representations are more robust and out perform expert designed
representations in drug discovery.[56] For representation learning, different
variants of graph neural networks are a popular choice[34, 57]. It starts with
generating atom (node) and bond (edge) features for all the atoms and bonds
within a molecule which are iteratively updated using graph traversal
algorithms, taking into account the chemical environment information to learn
a robust molecular representation. Starting atom and bond features of the
molecule may just be one hot encoded vector to include only atom-type, bond-
type or a list of properties of the atom and bonds derived from SMILES
strings. Yang et al. achieved the chemical accuracy for predicting a number of
properties with their ML models by combining the atom and bond features of
molecules with global state features before being updated during the iterative
process[58].

Molecules are 3-dimensional multiconformational objects and hence it is
natural to assume that they can be well represented by the nuclear coordinates
as is the case of quantum mechanics based molecular simulations[59]. However,
with coordinates the representation of molecules is non-invariant, non-
invertible and non unique in nature[32] and hence not commonly used in
conventional machine learning. In addition, the coordinates by itself do not
carry information about the key attribute of molecules such as bond types,
symmetry, spin states, charge etc in a molecule. Approaches/architectures have
been proposed to create the robust, unique, invariant representations from
nuclear coordinates using atom centered Gaussian functions, tensor field
networks, and more robustly by using representation learning techniques[60,
61, 62, 55, 31, 63].

Chen et al.[31] achieved the chemical accuracy for predicting a number of
properties with their ML models by combining the atom and bond features of
molecules with global state features of the molecules before being updated
during the iterative process. Robust representation of molecules can also be
learned only from the nuclear charge and coordinates of molecules as
demonstrated by Schutt et al[62, 55, 60]. Different variants (see Table 1) of
message passing neural networks for representation learning have been
proposed, with the main differences being how message are passed between the
nodes and edges and how they are updated during the iterative process using
hidden states $h_{v}^{t}$. Hidden states at each nodes during message passing
phase are updated using

$m_{v}^{t+1}=\sum
M_{t}(h_{v}^{t},h_{w}^{t},h_{vw}^{t}),~{}~{}~{}h_{v}^{t+1}=S_{t}(h_{v}^{t},m_{v}^{t+1})$
(1)

where $M_{t}$, $S_{t}$ are message and vertex update functions whereas
$h_{v}^{t}$, $h_{vw}^{t}$ are node and edge features. The summation runs over
all the neighbour of $v$ in the entire molecular graph. This information is
used by a readout phase to generate the feature vector for molecule which is
then used for property prediction.

These approaches however, require the relatively large amount of data and
computationally intensive DFT optimized ground state coordinates for the
desired accuracy, thus limiting their use for domains/data-sets lacking them.
Moreover, representations learned from a particular 3D coordinate of a
molecule fail to capture the conformer flexibility on its potential energy
surface[63] thus requiring expensive multiple QM based calculations for each
conformer of the molecule. Some work in this direction is based on semi-
empirical DFT calculations to produce a database of conformers with
3D-geometry has been recently published[63]. This however does not provide any
significant improvement in predictive power. These methods in practice can be
used with empirical coordinates generated from SMILES, using RDkit/chemaxon
but still require the corresponding ground state target properties for
building a robust predictive modeling engine as well as to optimize the
properties of new molecules with generative modelling.

Moreover, in these models, the cutoff distance is used to restrict the
interaction among the atoms to the local environments only, hence generating
local representations. In many molecular systems and for several applications,
explicit non-local interactions is equally important[64]. Long range
interactions have been implemented in convolutional neural networks, however,
are known to be inefficient in information propagation. Matlock et al.[65]
proposed a novel architecture to encode non-local features of molecules in
terms of efficient local features in aromatic and conjugated systems using
gated recurrent units. In their models, information is propagated back and
forth in the molecules in the form of waves making it possible to pass the
information locally while simultaneously travelling the entire molecule in a
single pass. With the unprecedented success of learned molecular
representations for predictive modelling, they are also adopted with success
for generative models[54, 66]

##  4 Predictive modeling

Predictive modeling is the most widely studied area of applied machine
learning in molecular modeling, drug discovery and medicine[67, 68, 69, 70,
71, 72, 62, 60, 55, 73]. Depending upon whether the ML architecture requires
the pre-defined input representations as input features or can learn their own
input representation by itself, predictive modeling can be broadly classified
into two sub-categories. The former is well covered in several recent review
articles[67, 68, 69, 70, 71, 72]. We will focus only on the latter, which has
been increasingly adopted in predictive machine learning recently with
unprecedented accuracy for a range of properties and data-sets. A number of
related approaches for predictive feature/property learning have been proposed
in recent years under the umbrella term Graph Neural Network (GNN) [74, 75,
76] and extensively tested on different quantum chemistry benchmark datasets.
GNN for predictive molecular modeling consists of two phases; representation
learning and property prediction, integrated end-to-end in a way to learn the
meaningful representation of the molecules while simultaneously learning how
to use the learned feature for the accurate prediction of properties. In the
feature learning phase, atoms and bond connectivity information read from the
nuclear coordinates or graph inputs are updated by passing through a sequence
of layers for robust chemical encoding which are then used in subsequent
property prediction blocks.

In one of the first works on embedded feature learning, Schütt et al.[60] used
the concept of many body Hamiltonians to devise the size of extensive,
rotational, translational and permutationally invariant deep tensorial neural
network architecture (DTNN) architecture for molecular feature learning and
property prediction. Starting with the embedded atomic number and nuclear
coordinates as input and after a series of refinement steps to encode the
chemical environment, their approach learns the atom centered Gaussian-basis
function as a feature which can be used to predict the atomic contribution for
a given molecular property. The total property of the molecule is the sum over
the atomic contribution. They demonstrated chemical accuracy of 1 kcal mol-1
in total energy prediction for relatively small molecules in QM7/QM9 dataset
that contains only H, C, N, O, F atoms.

Methods Key feature Advantage Drawbacks MPNN[57] 1. Message exchanged between
the atoms depends only on the feature of sending atom and corresponding edge
features and is independent of representation of atom receiving the message 2.
Generate global representation of the molecule 3. Predicted property of the
molecule is the function of global representations of the molecule 4. Generate
messages centered on the atoms 1. Achieved chemical accuracy in 11 out of 13
properties in QM9 data 2. Performs well for intensive properties 1. Requires
optimized coordinates as a part of input features 2. Including the state of
message receiving atom (dubbed as pair message) increase the property
prediction error 3. The message passed from an atom A to atom B can be
transmitted back to atom B resulting in noise d-MPNN[58] 1. Learns molecular
representation centered on bonds instead of atoms 2. Update on MPNN that
combines learned representation with the prior known fixed atomic, bond and
global molecular descriptors 1. Avoid noise resulting from message being
passed along any path by using directed messages 2. Use only SMILES string to
generate input representation 1. Requires optimized coordinates as a part of
input features SchNet[55] 1. Learns the atomistic representations of the
molecules 2. Total property of the molecule is the sum over the atomic
contributions 3. Learns representations only by using the atomic number and
geometry as atom and bond features respectively 1. Improve the performance on
8 out of 13 properties in QM9 data compared to MPNN 2. Performs relatively
well compared to MPNN for extensive properties 3. Requires only nuclear charge
and nuclear coordinates for learning input representations 1. Relatively poor
performance for intensive properties compared to MPNN 2. Requires optimized 3D
coordinates MEGNet[31] 1. Learns the atomistic representations of the
molecules 2. Total property of the molecule is the sum over the atomic
contributions 3. Use several atomic and bond properties of the atom and bond
as atom and bond features 4. Add the global state attribute of molecule in
addition to atom and bond feature. 1. Improves the performance on all the
extensive properties compared to MPNN and SchNet 2. Works equally well for
molecules and solid 1. Larger error for intensive properties compared to MPNN
2. It calculate MAE errors for atomization energies of U0, U, H and G and
compares with MAE on U0, U, H and G of SchNet 3. Requires optimized 3D
coordinates SchNet-edge[77] 1. Edge feature also depends upon the features of
the atom receiving the message 1. Improve the accuracy of the model over
SchNet/MPNN in all the properties in QM9 dataset 1. Requires optimized 3D
coordinates

Table 1: Benchmarks predictive modeling methods from literature with their key
features, advantage and disadvantages

Building on DTNN, Schütt et al.[55] also proposed a SchNet model, where the
interactions between the atoms are encoded by using a continuous filter
convolution layer before being processed by filter generating neural networks.
They also expanded the predictive power of their model for electronic,
optical, and thermodynamic properties of molecules in the QM9 dataset compared
to only total energy in DTNN achieving state-of-the-art chemical accuracy in 8
out of 12 properties. They also improved on accuracy over a related approach
of Gilmer et al.[34] known as message passing neural network (MPNN) on a
number of properties except polarizability and electronic spatial extent. In
contrast to the SchNet/DTNN model which learns atom-wise representation of
molecule, MPNN learns the global representation of molecules from the atomic
number, nuclear coordinates and other relevant bond-attributes and uses it for
the molecular property prediction. MPNN is more accurate for the intensive
properties ($\alpha$, $\braket{R^{2}}$) where the decomposition into
individual atomic contribution is not required. Performance of SchNet is
further improved by Jørgensen et al.[77] by making edge features inclusive of
the atom receiving the message.

Property Units MPNN SchNet-edge SchNet MegNet Target HOMO eV 0.043 0.037 0.041
0.038$\pm$0.001 0.043 LUMO eV 0.037 0.031 0.034 0.031$\pm$0.000 0.043 band gap
eV 0.069 0.058 0.063 0.061$\pm$0.001 0.043 ZPVE meV 1.500 1.490 1.700
1.400$\pm$0.060 1.200 dipole moment Debye 0.030 0.029 0.033 0.040$\pm$0.001
0.100 polarizability Bohr2 0.092 0.077 0.235 0.083$\pm$0.001 0.100 R2 Bohr2
0.180 0.072 0.073 0.265$\pm$0.001 1.200 U0 eV 0.019 0.011${}^{\textbf{*}}$
0.014 0.009$\pm$0.000${}^{\textbf{*}}$ 0.043 U eV 0.019 0.016${}^{\textbf{*}}$
0.019 0.010$\pm$0.000${}^{\textbf{*}}$ 0.043 H eV 0.017 0.011${}^{\textbf{*}}$
0.014 0.010$\pm$0.000${}^{\textbf{*}}$ 0.043 G eV 0.019 0.012${}^{\textbf{*}}$
0.014 0.010$\pm$0.000${}^{\textbf{*}}$ 0.043 Cv cal (mol K)-1 0.040 0.032
0.033 0.030$\pm$0.000 0.050

Table 2: Mean absolute errors obtained from several benchmark methods on 12
different properties of molecules in QM9 dataset. Bold represents the lowest
mean absolute errors among the models. * represents property are trained for
respective atomization energies. Target corresponds to chemical accuracy for
each properties desired from predictive models.

In another related model, Chen et al.[31] proposed an integrated framework
with unique feature update steps that work equally well for molecules and
solids. They used several atom attributes & bond-attributes and then combined
it with the global state attribute to learn the feature representation of
molecule. They claimed to out-perform SchNet model in 11 out of 13 properties
including U0, U, H, and G in the benchmark QM9 dataset. However, they trained
their model for respective atomization energies (P - nXXp, P = U0, U, H, and
G) in-contrast to the parent U0, U, H, and G trained model of Schnet. A fair
comparison of the model should be made between the similar quantities. These
models also demonstrated that a model trained for predicting a single property
of molecules with GNN will always outperform the model optimized for
predicting all the properties simultaneously. Other variants of MPNN are also
published in the literature with slight improvements in accuracy for
predicting some of the properties in the QM9 data set over the parent MPNN[77,
58]. The key features of a few benchmark models with their advantages and
disadvantages are listed in Table 1. The comparison of mean absolute errors
obtained from some of the benchmark models with their target chemical accuracy
are reported in Table 2. This shows that appropriate ML models when used with
proper representation of molecules, a well curated accurate data set, and a
well sought state-of-the-art chemical accuracy from machine learning can be
achieved.

##  5 Inverse Molecular Design

To achieve the long overdue goal of exploring a large chemical space,
accelerated molecular design, and generation of molecules with desired
properties, inverse design is unavoidable. It is generally known that a
molecule should have specific functionalities for it to be a effective
therapeutic candidate against a particular disease, but in many cases new
molecules that host such functionalities are not easily known with a direct
approach. Furthermore, the pool where such molecules may exist is
astronomically large[78, 79, 80] (approx. 1060 molecules), making it
impossible to explore each of them by quantum mechanics based simulations or
experiment.

In such scenarios, inverse design is of significant interest where the focus
is on quickly identifying novel molecules with desired properties, in contrast
to the conventional, so called direct approach where known molecules are
explored for different properties. In inverse design, we start with the
initial data set for which we know the structure and properties, map this to a
probability distribution and then use it to generate new, previously unknown
candidate molecules with desired properties very efficiently. Inverse design
uses optimization and search algorithms[81, 82] for the purpose and by itself
can accelerate the lead molecule discovery process, which is the first step
for any drug development. This paradigm holds even more promise when used in a
closed loop with synthesis, characterization and different test tools in such
a way that each of these steps receives and transmits feedback concurrently,
thus improving each other over time. This has shown some promise recently by
substantially reducing the timeline for commercialization of molecules from
its discovery to days, which is otherwise known to span over a decade in most
cases. In one recent work, Zhavoronkov et al.[1] designed, developed and
tested a workflow that integrates deep reinforcement learning with
experimental synthesis, characterization and test tools for de novo design of
drug molecules as potential inhibitors of discoidin domain receptor-1 in 21
days. Such a paradigm shift in the design of drugs is possible only because of
recently developed deep generative model architectures. Here, we briefly
discuss some of the breakthrough architectures along with the recent
applications in drug discovery.

Variational autoencoders[83] (VAE) and its different variants have been
extensively used for generating small molecules with optimal physico-chemical
and biological properties. VAE consist of encoder and decoder network, where
the encoder functions as a compression tool for compressing high-dimensional
discrete molecular representations to a continuous vector in low-dimensional
latent space, whereas the decoder recreates the original molecules from the
compressed space. Within VAE, recurrent neural networks (RNN)[84] and
convolution neural networks (CNN)[85] are commonly used as encoding networks
whereas several RNN based architectures such as GRU, LSTM are used as decoder
network. RNN independently has also been used to generate molecules.
Bombarelli et al.[83] first used VAE’s to generate molecules, in the form of
SMILES strings, from latent space while simultaneously predicting their
properties. For property prediction, they coupled the encoder, decoder network
with the predictor network which uses vector from latent space as an input.
SMILES strings generated from their VAE’s do not always correspond to valid
molecules. To improve on this, Kusner et al.[86] proposed a variant of VAE
known as grammar VAE that imposes constraint on SMILES generation by using
context free grammars rules. Both of these works employed string based
molecular representations. More recent works have focused on using molecular
graphs as input and output for variational auto-encoders[87] using different
variants of VAE among others[87, 86, 88], stacked auto-encoder, semi-
supervised deep autoencoders, adversial autoencoder, Junction Tree Variational
Auto-Encoder (JT-VAE) for generating molecules for drug discovery. In JT-
VAE[88], tree-like structures are generated from the valid sub-graph
components of molecules and encoded along with a full graph to form two
complementary latent spaces, one for molecular graph and another for the
corresponding junction tree. These two spaces are then used for hierarchical
decoding, generating 100 % valid small molecules. Further improvement on this
includes using JT-VAE in combination with auto-regressive and graph-to-graph
translation methods for valid large molecule generation[89].

Generative adversarial network (GAN) are another class of NN popular for
generating molecules[90, 91, 92]. They consist of generative and
discriminative models that work in coordination with each other where the
generator is trained to generate a molecule and the discriminator is trained
to check the accuracy of the generated molecules. Kadurin et al.[92]
successfully first used the GAN architecture for de novo generation of
molecules with anti-cancer properties, where they demonstrated higher
flexibility, more efficient training, and processing of a larger data set
compared to VAE. However, it uses unconventional binary chemical compound
feature vectors and requires cumbersome validation of output fingerprints
against the PubChem chemical library. Guimaraes et al.[93] and Sanchez-
Lengeling et al.[94] used sequence based Generative Adversarial Network in
combination with reinforcement learning for molecule generation, where they
bias the generator to produce molecules with desired properties. The works of
Guimaraes et al. and Sanchez-Lengeling et al. suffer from several issues
associated with GAN, including mode collapse during training, among others.
Some of these issues can be eliminated by using the reinforced adversarial
neural computer method[95], which extends their work. Similar to VAE’s, GAN’s
have also been used for molecular graph generation, which is considered more
robust compared to SMILES string generation. Cao et al.[91] non-sequentially
and efficiently generated the molecular graph of small molecules with high
validity and novelty from jointly trained GAN and Reinforcement learning
architectures. Maziarka et al.[89] proposed a method for graph-to-graph
translation, where they generated 100 % valid molecules identical with the
input molecules, but with different desired properties. Their approach relies
on the latent space trained for JT-VAE and a degree of similarity of the
generated molecules to the starting ones can be tuned. Mendez-Lucio et al.[96]
proposed conditional generative adversarial networks to generate molecules
that produce a desired biological effect at a cellular level, thus bridging
systems biology and molecular design. deep convolution NN based GAN[90] was
used for de novo drug design targeting types of cannabinoid receptors.

Generative models such as GAN’s, RNN, VAE have been used together with reward-
driven and dynamic decision making, reinforcement learning (RL) techniques in
many cases with unprecedented success in generating molecules. Popova et
al.[97] recently used deep-RL for de novo design of molecules with desired
hydrophobicity or inhibitory activity against Janus protein kinase 2. They
trained generative and predictive model separately first and then trained both
together using a RL approach by biasing the model for generating molecules
with desired properties. In RL, an agent, which is a neural network takes
actions to maximize the desired outcome by exploring the chemical space and
taking actions based on the reward, penalties, and policies setup to maximize
the desired outcome. Olivecrona et al.[98] trained a policy-based RL model for
generating the bioactives against dopamine receptor type 2, and generated
molecules with more than 95 % active molecules. Furthermore, taking an example
of the drug Celecoxib, they demonstrated that, RL can generate structure
similar to Celecoxib even when no Celecoxib was included in the training set.
de novo drug design has so far focused only on generating structures that
satisfy one of the several required criterion when used as drug. Stahl et al.
[99] proposed a fragment-based RL approach employing an actor-critic model,
for generating more than 90 % valid molecules while optimizing multiple
properties. Genetic algorithms (GA) have also been used for generating
molecules while optimizing their properties[100, 101, 102, 103]. GA based
models suffer from stagnation while being trapped in at the regions of local
optima[104]. One notable work alleviating these problems is by Nigam et
al.[53] where they hybridize GA and deep neural network to generate diverse
molecules while outperforming related models in optimization.

All of the generative models discussed above generate molecules in the form of
2D graphs, or SMILES strings. Models to generate molecules directly in the
form of 3D coordinates have also recently gained attention[105, 106, 107].
Such generated 3D coordinates can be directly used for further simulation
using quantum mechanics or by using docking methods. One of such first models
is proposed by Niklas et al.[107] where they generate 3D coordinates of small
molecules with light atoms (H, C, N, O, F). They then use the 3D coordinates
of the molecules to learn representation to map it to a space which is then
used to generate 3D coordinates of the novel molecules. Building on this for a
drug discovery application, we recently proposed a model[66] to generate 3D
coordinates of molecules while always preserving desired scaffolds. This
approach has generated synthesizable drug-like molecules that show high
docking score against the target protein. Other scaffolds based models to
generates molecules in the form of 2D-graphs/SMILES strings are also published
in the literature[108, 109, 110, 111, 112].

Recently, with the huge interest in the development of architecture and
algorithms required for quantum computing, quantum version of generative
models such as the quantum auto-encoder[113] and quantum GANs[114] have been
proposed which carry huge potential, among others, for drug discovery. The
preliminary proof of concept work of Romero et al.[114, 113] shows that it is
possible to encode and decode molecular information using a quantum encoder,
demonstrating generative modeling is possible with quantum VAE’s and more work
especially in the development of supporting hardware architecture is required
in this direction.

##  6 Conclusions and Future Perspectives

The success of current ML approaches depends upon how accurately we can
represent a chemical structure for a given model. Finding a robust,
transferable, interpretable, and easy to obtain representation which obeys the
fundamental physics and chemistry of molecules that work for all different
kind of applications is a critical task. If available, this would save lot of
resources, while increasing the accuracy and flexibility of molecular
representations. Efficiently using such representations with robust and
reproducible ML architectures will provide predictive modeling engine. Once a
desired accuracy for diverse molecular systems for a given property prediction
is achieved, it can routinely be used as an alternative to expensive QM based
simulations or experiments. In the chemical and biological sciences, a main
bottleneck for deploying ML models is the lack of sufficiently curated data
under similar conditions that is required for training the models. Finding
architecture that works consistently well enough for relatively small amount
of data is equally important. Strategies such as active learning (AL) and
transfer learning (TL) are ideal for such scenarios to tackle problems[115,
116, 117, 118, 119]. Graph based methods for end-to-end feature learning and
predictive modeling so far have been successfully used on small molecules
consisting of lighter atoms. For larger molecules, robust representation
learning and molecule generation parts must include non-local interactions
such as vander-waals and H-bonding while building predictive and generative
models.

Equally important is to develop and tie a robust, transferable, and scalable
state-of-the-art platform for inverse molecular design in a closed loop with
predictive modeling engine to accelerate therapeutic design ultimately
reducing the cost and time required. Many of the inverse ML models used for
inverse design use single bio-chemical activity as the criteria to measure the
success of generated candidate therapeutic, which is in-contrast to real
clinical trial, where small molecule therapeutics are optimized for several
bio-activities simultaneously. CAMD workflow should be designed in a way to
optimize multiple objective functions while generating and validating
therapeutic molecules. Validation of newly generated lead molecules for a
given drug usage, if done by experiments or quantum mechanical simulations, is
an expensive task for all generated lead molecules. Ways to auto-validate
molecules (using an inbuilt robust predictive model) would be ideal to save
resources. In addition, CAMD workflows should be able to quantify uncertainty
associated with it using statistical measures. For an ideal case, such
uncertainty should decrease over the time as it learns from its own experience
in series of closed loop.

CAMD workflows are generally built and trained with a specific goal in mind.
Such workflows need to be re-configured and re-trained to work for different
objective in therapeutic design and discovery. Design and build a single
automated CAMD setup for multiple experiment (multi-parameter optimization) in
a kind of transfer learning fashion is a challenge.  It would be particularly
helpful for the domains where a relatively small amount of data exist. Having
such a CAMD infrastructure, algorithm and software stack would speedup end-to-
end antiviral lead design and optimization for any future pandemics like
Covid-19.

##  7 Acknowledgement

This research was supported by the DOE Office of Science through the National
Virtual Biotechnology Laboratory, a consortium of DOE national laboratories
focused on response to COVID-19, with funding provided by the Coronavirus
CARES Act. Additional support was provided by the Laboratory Directed Research
and Development Program at Pacific Northwest National Laboratory (PNNL). PNNL
is a multiprogram national laboratory operated by Battelle for the DOE under
Contract DE-AC05-76RLO 1830. C. Computing resources was supported by the
Intramural program at the William R. Wiley Environmental Molecular Sciences
Laboratory (EMSL; grid.436923.9), a DOE Office of Science User Facility
sponsored by the Office of Biological and Environmental Research and operated
under Contract No. DE-AC05-76RL01830.

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