# Structure-based drug discovery with deep learning

Rıza Özçelik  Eindhoven University of Technology, Institute for Complex
Molecular Systems and Dept. Biomedical Engineering, Eindhoven, Netherlands.
Derek van Tilborg  Eindhoven University of Technology, Institute for Complex
Molecular Systems and Dept. Biomedical Engineering, Eindhoven, Netherlands.
José Jiménez-Luna  Microsoft Research Cambridge, Cambridge, United Kingdom.
Francesca Grisoni  Eindhoven University of Technology, Institute for Complex
Molecular Systems and Dept. Biomedical Engineering, Eindhoven, Netherlands.

###### Abstract

Artificial intelligence (AI) in the form of deep learning bears promise for
drug discovery and chemical biology, e.g., to predict protein structure and
molecular bioactivity, plan organic synthesis, and design molecules de novo.
While most of the deep learning efforts in drug discovery have focused on
ligand-based approaches, structure-based drug discovery has the potential to
tackle unsolved challenges, such as affinity prediction for unexplored protein
targets, binding-mechanism elucidation, and the rationalization of related
chemical kinetic properties. Advances in deep learning methodologies and the
availability of accurate predictions for protein tertiary structure advocate
for a renaissance in structure-based approaches for drug discovery guided by
AI. This review summarizes the most prominent algorithmic concepts in
structure-based deep learning for drug discovery, and forecasts opportunities,
applications, and challenges ahead.

### Keywords

Artificial intelligence $\bullet$ De novo design $\bullet$ Machine learning
$\bullet$ Medicinal chemistry $\bullet$ Structural biology

## Introduction

Deep learning – a subfield of artificial intelligence (AI) based on multi-
layer neural networks1 – has gained remarkable traction in science and
technology, e.g., to advance mathematics 2, 3, investigate galaxies 4, and
generate realistic images5. Chemistry and biology have witnessed several AI
breakthroughs, for instance, in protein structure prediction6, 7, chemical
synthesis planning8, 9, and atomistic simulations10, 11. Drug discovery has
particularly benefited from the advent of deep learning12, 13, with success in
molecule prioritization and automated de novo design14, 15, 16, 17. Here, deep
learning can accelerate the navigation of the extremely vast chemical space of
drug-like molecules18 in search for potential therapeutics, and complement
resource- and time-intensive high-throughput screening campaigns 19. Most deep
learning studies have focused on ligand-based approaches 12, which leverage
solely the structural information of small molecule ligands to provide
predictions. For these applications, numerous systematic studies 20, 21 and
experimental proofs-of-concept16, 17, 22 have been published. On the other
hand, structure-based deep learning approaches – which leverage information on
the target protein – have not found parallel interest yet.

Structure-based drug discovery (SBDD) methods augmented with AI are arguably a
more complex and a higher-potential endeavor compared to their ligand-based
counterparts. Numerous marketed drugs have been identified by ‘traditional’
SBDD (e.g., HIV-1 protease inhibitors23, the thymidylate synthase inhibitor
raltitrexed 24, and the antibiotic norfloxacin 25). Accelerating SBDD with
deep learning can help address existing drug discovery challenges, such as
polypharmacology by design 26, selectivity optimization 27, activity cliff
prediction 28, and target deorphanization 29. Deep learning does not require
explicit feature engineering and can thus be applied to learn directly from
molecular representations of both ligands and proteins. This is particularly
relevant for SBDD, where engineering numerical features for complex molecular
entities like proteins 30 is inevitably more laborious than for small
molecules31. Therefore, deep learning for SBDD bears an untapped potential to
capture highly non-linear structure-activity relationships and has recently
started to show its promise. Accurate protein structure prediction efforts
like AlphaFold6, 7 are expected to further accelerate computer-assisted SBDD.
Deep learning for SBDD is still in its infancy but is moving forward at a fast
pace, and its relevance in the years to come is expected to increase.

This review provides a comprehensive overview of how deep learning can be
leveraged for SBDD, to incorporate protein information at different levels of
complexity (e.g., aminoacid sequence, and/or tertiary structure). After
addressing how proteins can be represented for deep learning, we address
current state-of-the-art methods for structure-based drug discovery, with a
particular focus on drug-target interaction prediction, binding site
detection, and de novo design (Fig. 1). Finally, we discuss current
limitations and research gaps, along with foreseen future directions and
opportunities. A glossary of selected terms can be found in Box 1.

Figure 1: Structure-based drug discovery tasks discussed in this review: (a)
drug-target interaction prediction, which aims to predict the affinity between
a protein and a ligand, using the structural information of both molecular
entities; (b) binding site detection, which aims to identify ‘druggable’
cavities in the protein structure, (c) de novo design, aiming to design
bioactive molecules from scratch using the information of a protein target.

[t]

### Box 1: Glossary of selected terms

Binding site. Protein region that is responsible for the interaction with
another molecule (e.g., small molecule inhibitors, activators, and other
proteins).
Convolutional neural network (CNN). Neural network architecture that learns
from local information on grid-like data through convolution operations.
Molecular docking. Computational procedure used to predict the predominant
three-dimensional binding mode(s) of a molecule w.r.t. another (macro)molecule
it binds to. These typically involve the use of a conformational search method
and pose scoring functions32.
Generative deep learning. Deep learning methods that aim to model the
underlying data distribution of a given set of samples and, by sampling from
the modeled distribution, generate new data points without the need for
explicit hard-coded design rules33.
Geometric deep learning. Umbrella term to identify neural network
architectures that incorporate and process symmetry information in their
design34.
Molecular descriptors. Numerical features obtained from a molecular
representation with the goal of capturing pre-defined chemical information31.
Ligand. Any molecule that binds to a protein with high affinity and
specificity.
Protein. (Macro)molecule consisting of amino acid residues joined by peptide
bonds.
Reinforcement learning. Subfield of machine learning whose goal is to study
the agents that learn to act towards maximizing a cumulative reward in an
environment.
Simplified Molecular Input Line Entry System (SMILES)35. String-based chemical
notation capturing two-dimensional molecular information, in which letters are
used to represent atoms, whereas symbols and numbers encode bond types,
connectivity, branching, and stereochemistry.
Transfer learning. A machine learning method where a model trained on one task
is reused as the starting point for a model on a second, related, task.
Voxel. A volumetric pixel.

## Representing proteins for deep learning

The design of deep learning approaches for SBDD is inherently more intricate
than those that are ligand-based, due to the need to represent protein
information at different levels of complexity. Proteins are large polypeptide
chains, organized in a ‘structural hierarchy’36: (a) primary structure,
referring to the sequential arrangement of amino acids along the polypeptide
chain, (b) secondary structure, capturing the occurrence of alpha-helices and
beta-pleated sheets along the protein sequence, and (c) tertiary structure,
capturing how proteins fold in the three-dimensional space. Such complexity is
reflected in the various protein representations used for deep learning (Fig.
2):

  * •

Primary (amino acid) sequence. The amino acid sequence is specified starting
from the amino-terminal end (N-terminus) and ending at the carboxyl-terminal
(C-terminus) end. For deep learning purposes, the primary sequence is often
represented as a character string, where each letter represents one of the
twenty naturally-occurring amino acids (e.g., ‘AIR’ corresponds to alanine,
isoleucine, and arginine). These representations are at the core of
established protein ‘featurization’ techniques, such as ProtVec 37, SeqVec 38,
and ProtTrans 39. Although less frequently encountered 40, 41, the primary
sequence can also be represented as a graph, whose nodes are amino acids
(featurized by type or corresponding physicochemical features) and whose edges
capture their adjacency in the chain.

  * •

Tertiary (three-dimensional) structure. The three-dimensional shape of a
protein (tertiary structure) is determined by the interactions among its side
chains, and features a certain degree of conformational plasticity 42. The
protein structure contains key information for SBDD, since it relates to
protein function 43, 44, and it determines ligand binding 45. Moreover,
inducing conformational changes is often the goal of drug discovery42. Several
ways exist to learn from tertiary structures with deep learning. Early
approaches 46, 47 have used grid-based voxel representations (see Box 1) to
capture the spatial distribution of the protein’s physicochemical or
pharmacophore properties. While these representations are suited to well-
established deep learning architectures (e.g., convolutional neural networks,
CNNs), many voxels representing empty space do not carry relevant information,
and they involve increased computational costs at higher spatial grid
resolutions. Other approaches48 represent proteins as molecular graphs, where
each atom is a node and each bond is an edge. Depending on the chosen level of
coarse-graining, often only backbone atoms are chosen to correspond to nodes,
while edges often represent geometrical proximity in the coordinate space
rather than direct chemical bonds 49. Edges and bonds can be characterized by
additional geometrical and/or physicochemical properties.

  * •

Protein surface. The protein surface is usually defined as the separation
between solvent-accessible and inaccessible regions 50, 51 (Fig. 2a) and it
plays a key role in the protein interactions with (macro)molecular entities.
Protein surfaces are usually represented as either meshes (i.e., a set of
polygons capturing the location of the surface, whose vertices can be
described by a 2D grid or a 3D graph structure) or point clouds (i.e., graphs
whose nodes describe the location of the surface at a certain resolution).
Although often computed from the 3D structure, the surface representation
might better reflect the physicochemical features responsible for the
interaction with other (macro)molecules, as well as aspects of protein
function that go beyond sequence similarity.

The chosen protein representation for deep learning affects the type, quality,
and quantity of chemical information captured. It will also affect the type of
suitable deep learning strategies (Box 2) and the corresponding advantages and
drawbacks. Additionally, the number of data available for machine learning
depends on the chosen protein representation (Table 1). Primary sequences are
easy to obtain but lack information about the spatial configuration of atoms,
particularly relevant to determine the binding pose of ligands. On the other
hand, 3D protein structures contain potentially richer information for drug
discovery, but they are more difficult to obtain experimentally and thus
relatively scarce (although the cost has been steadily decreasing with the
advent of experimental techniques like cryogenic electron microscopy 52). In
this context, AI breakthroughs in protein structure prediction like AlphaFold6
bear promise to bridge the gap in data availability, by making thousands of
predicted protein structures available in an unprecedented effort53, 54.

Small molecule ligands can be represented in analogous ways to protein
structures. The most commonly used representations are: (a) molecular strings
(e.g., SMILES strings35), which capture 2D information (atom occurrence and
connectivity), (b) 2D and 3D molecular graphs (based on the availability of
experimentally determined or computed conformational information), and (c)
molecular surfaces. An in-depth description of small molecule representations
and corresponding deep learning approaches can be found in a recent work 55.

Figure 2: Representing proteins for deep learning.  (a) Structural hierarchy of protein information: (primary) amino-acid sequence, referring to the sequential arrangement of amino acids along the polypeptide chain; (tertiary) 3D structure, capturing protein folding in the three-dimensional space; protein surface, delimiting solvent-accessible and inaccessible regions. Each level of information is characterized by a different availability of data (1). (b) Protein representations for deep learning, capturing information on the protein sequence (strings), the 3D structure (graphs, voxels and point clouds), and the surface (point clouds and meshes). Each representation is suited to different neural network architectures (Box 2). Table 1: Summary of selected datasets for structure-based deep learning. Dataset name, description and number of entries (updated as of June 2022) are provided. Dataset | Description | No. entries | Link (if available)
---|---|---|---
Protein Data Bank (PDB)56 |  Structural data of biological macromolecules. | 189,735 structures | rcsb.org
scPDB57 |  Druggable binding sites and ligands extracted from the PDB. |  4782 protein structures and 6326 ligands. | bioinfo-pharma.u-strasbg.fr/scPDB
BioLip58 |  Semi-manually curated ligand–protein interactions. | 573,225 entries. | zhanggroup.org/BioLiP
PDBbind59 |  Protein-ligand co-complexes and associated affinities extracted from the PDB. | 23,496 complexes. | pdbbind.org.cn
UniProt59 |  Protein sequence and functional information. | > 60 million sequences. | uniprot.org
AlphaFold Protein Structure Database53 |  Predictions of protein structures by AlphaFold v 2.0. 6 |  992,316 predicted structures. | alphafold.ebi.ac.uk
AlphaFill 54 |  Common ligands and cofactor transplants for AlphaFold models. |  12,029,789 transplants. | alphafill.eu
Binding MOAD60, 61 |  X-ray crystal structures with bound ligands and experimental binding affinities. |  41,409 protein-ligand complexes, and 15,223 binding measurements. | bindingmoad.org
Directory of Useful Decoys (DUD-E)62 |  Directory of decoys designed to benchmark molecular docking programs. |  22,886 active molecules and affinities on 102 targets; 59 decoys per compound. | dude.docking.org
BindingDB 63 validation sets |  Binding affinities of protein-ligand pairs curated from the literature. |  $\leavevmode\nobreak\ \sim$1200 series with at least 1 cocrystal available in each. | bindingdb.org
BigBind64 |  Associated protein structures to ChEMBL 65 assay data via Pocketome. 66 |  818,995 activities with associated protein structures. | Brocidiacono et al., 2022
KIBA67 |  Bioactivity measurements of compounds against kinases. | 246,088 measurements. | Tang et al., 2014
Davis68 |  Binding affinities ($K_{d}$ values) of inhibitors against kinases. | 30,056 measurements. | Davis et al., 2011

## Deep learning for structure-based drug discovery

This section aims to provide a concise overview of SBDD approaches fueled by
deep learning. SBDD will be considered in its broader sense, i.e., not only
limited to 3D protein structure, but also including sequence and surface
representations. We focus on three key tasks (Fig. 1), namely binding site
detection, drug-target interaction prediction, and structure-based de novo
design. For each task, selected deep learning approaches are described through
the lenses of the protein representation they rely on (sequence, structure, or
surface). A summary of selected deep learning studies is reported in Table 2.

Table 2: Selected deep learning approaches applied to SBDD. Models are categorized by task and frequently adopted molecular representations. Task | Description | Protein representation
---|---|---
Drug-target interaction prediction |  Predict the interaction between one or more proteins and one or more ligands. |  Amino-acid sequence 69, 70, 71, 72, 73, 74 3D structure 75, 46, 76, 77, 78
Docking |  Determination of a ligand pose within a target binding site. |  3D structure 79, 80, 81
Binding site detection |  Identification and/or localization of functional protein binding sites. |  Amino-acid sequence 82, 70, 83, 84 3D structure 47, 85, 86, 87, 88 Surface 89, 90, 91
De novo design |  Generation of ligands with desired properties conditioned on a protein. |  Amino-acid sequence 92, 93 3D structure 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104

### Drug-target interaction prediction

The identification of interactions between molecules and macromolecular
targets is a key step in drug discovery, drug repurposing, and off-target
activity prediction. Drug-target interaction (DTI) prediction aims to predict
the bioactivity (e.g., binding affinity) of a given set of molecules on one or
more macromolecular targets, by leveraging both protein and ligand
information. Given the complexity of ligand-protein interactions and of
engineering suitable molecular features for DTI, it is no surprise that this
topic has found a widespread application of deep learning techniques 105. In
what follows, deep learning models developed for DTI prediction are
categorized on the basis of the protein representation they rely on.

  * •

Sequence-based approaches. Sequence-based DTI prediction models use amino acid
sequences in combination with ligand representations to perform a prediction.
One of the earliest approaches, DeepDTA69, applied 1-dimensional CNNs to
simultaneously learn from string representations of both ligands (in the form
of SMILES) and protein sequence, by first creating separated embeddings and
then concatenating them to perform a prediction. Later works have replaced
CNNs with other methods, such as recurrent neural networks (Box 2),70, 106 and
attention-based70, 106, 107 or transformer architectures71, 108, 72. Several
works have addressed how to improve the representation of protein sequences,
e.g., by incorporating evolutionary information,109, 110 or protein sequence
composition descriptors 73, 111. Ligands are usually represented as strings
(e.g., SMILES 69, 110 or DeepSMILES 112, 109), binary fingerprints,73, 113
frequently occurring substructures109, or molecular graphs.111, 114, 115

  * •

3D structure-based approaches. These models leverage atom coordinates, usually
of co-crystallized protein-ligand complexes, for training. Early approaches
projected 3D protein-ligand complexes into grids featurized with physio-
chemical properties, and subsequently applied CNNs for binding affinity
prediction75, 46, 116. Later works extended this idea by including more
sophisticated features, e.g., inter-molecular interaction fingerprints76 and
computed molecular energies117. 3D grid-based approaches have also been used
for lead optimization by predicting relative binding free energies linked to
small modifications of ligand structures118, 119. More recent approaches have
replaced grid-based representations with graphs 48, 120, 77, 78, allowing to
explicitly represent atom neighborhoods and connectivity, and apply roto-
translational invariant graph neural networks for binding affinity prediction.

  * •

Surface-based approaches. Surface-based approaches have found limited
application for DTI prediction. OctSurf121 represents both binding pockets and
ligands as surfaces, by partitioning the 3D space recursively into octants and
considering only portions containing van der Waals surface points. Non-empty
octants, along with their physicochemical and geometric features, are then
used as the input to a CNN. Other approaches, such as HoloProt122, merge 3D
structure (graph) and surface (point cloud) information for task-specific
training, e.g., enzyme-catalyzed reaction classification and binding affinity
prediction.

Another topic of recent interest by the deep learning community is protein-
ligand docking, which aims to predict the putative binding pose of a ligand
upon binding to a macromolecular target (Box 1). Although these methods do not
aim to predict the affinity between a ligand-protein pair directly, they can
be used as a proxy to elucidate potential mechanisms of interaction. Deep
learning has been mostly applied to ligand pose optimization while considering
a rigid target structure, although recent approaches have started taking side-
chain flexibility into account 123. Early approaches used protein-ligand
interaction fingerprints124, 125, while successive approaches have leveraged
either a voxelized version of the protein structure combined with CNNs 126,
127 or graph-based representations with message-passing neural networks128,
129, 130, 131 in lieu of classical scoring functions. Finally, several
approaches have attempted to directly predict the ligand binding pose in an
end-to-end fashion 132, 79, 80, without the need for a classical search
algorithm by exploiting advances in equivariant deep learning.

Deep learning has undoubtedly accelerated DTI prediction, thanks to the
possibility to represent and learn from protein-ligand complexes more
efficiently. However, simpler models based on well-established descriptors
might reach comparable performance 133, due to undesired memorization and
hidden bias in ligand-protein interaction data134, 135, 136, 137. Moreover, no
relationship has been observed between the complexity of protein and ligand
representations and the accuracy of the resulting deep learning models 133.
Thus, more attention should be put on strategies for model evaluation and data
selection/splitting procedures to ensure a reliable prediction of DTIs with
deep learning 133, 138, 139.

### Binding site detection

The identification of ‘druggable’ binding sites in proteins plays a pivotal
role in SBDD, from hit identification and molecule screening to mechanism
formulation140. Over the years, a plethora of methods have been developed for
binding site detection140, 141, 142, 143, 144, e.g., via interatomic gap
volumes142 or regions of buried pocket surfaces145. Recently, deep learning
methods learning directly from ‘raw’ representations of proteins have gained
increasing traction to detect binding sites. These approaches can be grouped
by the molecular representations they rely on, i.e., protein sequence, 3D
structure, and surface, as described below:

  * •

Sequence-based models. Binding site detection can be performed by predicting
which residues of the amino-acid sequence are involved in ligand binding,
although sequence-based approaches have found limited application. Early
methods approached binding site detection as a ‘side-product’ of binding
affinity prediction, by using explainable AI techniques to highlight relevant
residues for a model’s prediction 82, 70, 83. Few works have addressed binding
site detection only 146, 147. Recently, sequence-based binding site detection
has been jointly modeled with drug-target affinity prediction, leading to
improved performance on both tasks 84.

  * •

3D structure-based models, which use the spatial information of proteins to
detect likely binding sites. Early approaches represented the protein
structure with voxels featurized with pharmacophore-like properties, along
with convolutional neural networks47, 85. Subsequent works have refined
structure-based binding site detection with additional techniques from the
computer vision domain, e.g., image segmentation.86, 87. BiteNet148 extended
‘static’ CNN-based approaches by incorporating conformational ensembles of
proteins. The approach was later adapted to predict protein-peptide binding
sites 149. A recent approach combined spatial properties with amino acid
sequence information to predict protein-protein interaction sites and is
applicable to ligand binding site prediction88.

  * •

Surface-based models. Voxelized representations of protein coordinates have
several drawbacks,91, 150 (e.g., carrying non-informative voxels that are
either deeply buried and not accessible by a ligand or represent empty space,
and information coarse-graining due to discretization of the input protein
space) and might lead to worse results than working with surfaces alone150.
For this reason, several methods based on protein surfaces have been developed
over the years. These approaches rely on the representation of protein
structures as continuous shapes characterized by geometric and physicochemical
features to perform a prediction. Geodesic CNNs have been used to determine
interaction fingerprints of molecular surfaces, and to predict protein and
ligand binding sites 89. The approach was later expanded to obtain fully-
learnable protein representations.90 DeepSurf91 discretizes the solvent
accessible surface using a combination of K-means clustering and and density
reduction91.

A recent analysis of computational approaches for protein-ligand binding site
recognition 151 has shown DeepSurf91 to perform remarkably well. Moreover,
non-machine-learning algorithms resulted competitive alternatives to deep
learning 151. All analyzed methods struggled on shallow binding sites, due to
the higher frequency of deep grooves used for model training 151. Despite the
recent progress of binding pocket detection, room for improvement remains,
e.g., to increase pocket coverage and detect sub-pockets151, and to predict
allosteric binding sites 152.

### Protein-based de novo design

De novo design refers to the generation of novel chemical entities possessing
desired properties from scratch 153 and is among the most challenging tasks in
computer-assisted drug discovery. Computational algorithms are faced with an
incredibly vast ‘chemical universe’, whose cardinality has been estimated
between 1024 and 10100 molecules 154, 18, 155. In this context, ‘brute-force’
molecule assembly or enumeration approaches are computationally unfeasible. In
recent years, generative deep learning has shown great promise for de novo
drug design 15, 17, 156 and to complement traditional approaches based on
human-engineered rules 157, 158, 24, 159.

Generative deep learning approaches for de novo design are usually applied to
produce molecules in the form of molecular graphs160, 161, 162 or strings15,
16, 17 (e.g., SMILES). While most de novo design approaches are ligand-based
156, 163, 164, 15, 165, 166, structure-based approaches have recently emerged
as a promising research direction, due to their potential to design molecules
interacting with pharmacologically-relevant targets on demand.

  * •

Sequence-based approaches. Sequence-based de novo design approaches usually
cast the problem into a machine translation task, where high-affinity protein-
ligand pairs are considered as sentences in different languages to be matched.
To this end, sequence and SMILES strings for proteins and ligands are used,
respectively. The first-in-kind approach 92 trained a transformer architecture
to ‘translate’ amino-acid sequences into the SMILES strings of the
corresponding ligand. This approach can be used for sequence-conditioned de
novo design. A recent work used a transformer-based pipeline 93 that combined
language models that were pre-trained on large corpora of proteins 167 and
small molecules 168.

  * •

3D structure-based approaches. De novo design conditioned on the tertiary
structure information can usually generate molecules in the form of 3D ligands
(molecular graphs) or strings (e.g., SMILES). In the former case, 3D
representations of protein-ligand complexes are used as the input to generate
novel 3D molecular graphs. As one of the earliest approaches, LigVoxel94
relied on 3D grids to generate spatial ‘blobs’ of ligand properties such as
occupancy, aromaticity, and hydrogen-bond donor/acceptors that match the
protein pocket. Later works used equivariant diffusion networks103, 104,
variational autoencoders95, and reinforcement learning97, 96 to directly
generate ligand conformations for the binding pocket. Recently, equivariant
neural networks coupled with point-cloud representations have been used for
molecule optimization, via pocket-based fragment expansion 169. Compared to 3D
graphs, molecular strings are usually easier to generate and might match or
outperform graph-based models 170. A pioneering work of this category
leverages generative adversarial networks171 to produce SMILES strings for
protein pockets, where a pocket is featurized and fed into a generator network
with a conditioning ligand99. A subsequent model adopts graph neural networks
to represent active sites and generates targeted SMILES strings101, whereas
another model uses a pharmacophore model to represent ligands and condition
the generation for the targeted pocket100. Recently 102, a recurrent neural
network model has been coupled with ligand–protein interaction fingerprints
(determined on ligand docking poses) for conditioned ligand generation in the
form of SMILES strings.

While ligand-based de novo design pipelines using deep learning have been
experimentally validated in multiple instances 17, 172, 173, structure-based
de novo design has to date not been applied prospectively. This highlights an
important gap in the potential for acceptance of structure-based de novo
design algorithms.

### Box 2: Simplified depiction of common neural networks applied to protein
representations.

Each protein representation useful for SBDD (Fig. 2) is suited to different
neural network architectures. Well-established examples of such architectures
are the following: a. Recurrent neural networks (RNNs) (e.g., with long-short
term memory cells 174) are commonly-chosen architecture to process the primary
sequence of a protein. RNNs incorporate feedback connections that allow
information in the previous inputs to flow into the subsequent inputs. The
feedback mechanism behaves as a ‘learned memory’ in the architecture and
enables capturing long-range dependencies in the input sequence. b.
Convolutional neural networks (CNNs) are a powerful architecture when paired
with voxelized representations to capture spatial dependencies. CNNs apply
learnable filters to the input and excel in capturing local patterns, which
renders them suited to binding affinity prediction and binding site detection.
c. Graph neural networks (GNNs) operate on molecular graphs (e.g., atoms and
their interactions) and can capture the structural and functional
relationships between, as well as within, atoms belonging to one or more
molecular complexes. The wide application domain of graphs enables GNNs to
find applications in every task of structure-based drug discovery.

## Gaps, opportunities, and outlook

Deep learning for structure-based drug discovery is gaining increasing
traction, as evidenced by the exponential increase in the number of published
approaches over the last few years. Breakthroughs in protein structure
prediction53, 7 not only exemplify the potential of deep learning in the
molecular sciences but are expected to further propel structure-based drug
discovery with AI. SBDD is arguably more challenging than its ligand-based
counterpart, due to the number and structural complexity of chemical entities
involved, and aspects like target conformational flexibility 175. Most of the
current SBDD algorithms are agnostic to dynamical information and, in certain
instances, might not outperform simpler methods 151, 133. However, SBDD has an
untapped potential to tackle drug discovery for new, uncharted protein
targets. Such a ‘zero-’ and ‘few-shot’ learning potential makes AI-driven SBDD
a high-risk/high-gain endeavor, expected to advance future drug discovery.

An area of continuous development that bears promise for SBDD is geometric
deep learning176, 34. Geometric deep learning attempts to unify neural
networks from the perspective of symmetry and topology. Roto-translational
invariance/equivariance is particularly relevant for the three-dimensional
representations of molecular systems, which is beneficial to limit function
search space during training 55. Geometric deep learning is expected to boost
AI’s capability to model molecular complexes and their interactions89, 177, as
well as molecular design104, 178. Diffusion models 179 – a family of
generative models inspired by non-equilibrium thermodynamics – are also
gaining increasing popularity in deep learning thanks to their generative
capabilities, and have found pioneering applications in the molecular
sciences, too 104, 180, 81. These approaches have reached state-of-the-art in
several deep learning applications and are expected to propel SBDD in the
future.

A current bottleneck of AI-driven SBDD is constituted by the available
training data. While protein sequence information and experimental assay data
are largely available, high-quality 3D data of co-crystallized proteins and
ligands with accompanying properties is largely missing. Furthermore,
obtaining high-quality protein structure information is resource-intensive and
challenging for several drug targets181, e.g., disordered and membrane
proteins. Furthermore, non- and poorly binding molecules are often strongly
under-reported in medicinal chemistry datasets. As a result, available three-
dimensional datasets are often highly biased in their content 134, 135, 136,
137, 182, which has historically led to poor generalizability 183, 184, 185,
186. Several studies have attempted to alleviate this issue, via data
curation185, 187, bias-controlled training98, and debiasing138, 188. Bridging
the gap between the different types of available information will be an active
task in upcoming years, with some recent work pointing in this direction
already 64.

Finally, the application of deep learning in well-established fields like
chemical biology and medicinal chemistry might at times be met with skepticism
by experimentalists. These well-grounded concerns commonly originate from the
black-box nature of deep learning. Additionally, robust performance benchmarks
and evaluation datasets are currently missing, especially for de novo design
studies. Moreover, to the best of our knowledge, no deep learning approaches
for SBDD have been validated in a prospective setting yet. To foster broader
acceptance of structure-based deep learning, we need to ‘open the box’ and
validate methods experimentally. We envision that more sophisticated
applications of explainable AI189, 190 will aid in identifying underlying
structure-activity relationships and binding modes, and bridging the gap
between theory and real-world applications.

## Conclusions

In recent years, deep learning has taken drug discovery by storm, offering new
opportunities for more efficient exploration of chemical space. Ligand binding
site detection, drug-target interaction prediction, and structure-based de
novo design can be valuable tools in early drug discovery, especially for
unexplored macromolecular targets. As a whole, these approaches bear great
promise to extend upon the successes of ligand-based methods. However,
structure-based methods have not yet proven their applicability in prospective
scenarios. Overcoming such barriers will mostly depend on additional efforts
in data collection and curation, as well as on methodologies that efficiently
exploit relationships between assay and structural data.

## Acknowledgements

F.G. acknowledges the support from the Institute for Complex Molecular Systems
(ICMS, TU/e) and the Centre for Living Technologies (Alliance TU/e, WUR, UU,
UMC Utrecht).

## Conflict of interest

None to declare.

## Author contribution

Conceptualization: F.G., with contributions from all authors; Investigation:
all authors; Visualization: D.v.T., R.O., F.G.; Writing – original draft:
F.G., R.O., D.v.T.; Writing – review & editing: all authors.

## List of abbreviations

AI: Artificial intelligence.
CNN: Convolutional neural network.
DTI: Drug-target interaction prediction.
GNN: Graph Neural Network.
SBDD: Structure-based drug discovery.
SMILES: Simplified molecular input line entry system.
RNN: Recurrent Neural Network.
2D (3D): Two- (three-)dimensional.

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