# Recent Developments in Structure-Based Virtual Screening Approaches

Christoph Gorgulla1,2,3 1 Harvard Medical School, Harvard University, Boston,
Massachusetts 02130, USA; email<EMAIL_ADDRESS>2 Physics Department,
Harvard University, Cambridge, Massachusetts 02138, USA  3 Department of
Cancer Biology, Dana Farber Cancer Institute, Boston, Massachusetts 02215, USA

###### Abstract

Drug development is a wide scientific field that faces many challenges these
days. Among them are extremely high development costs, long development times,
as well as a low number of new drugs that are approved each year. To solve
these problems, new and innovate technologies are needed that make the drug
discovery process of small-molecules more time and cost-efficient, and which
allow to target previously undruggable target classes such as protein-protein
interactions. Structure-based virtual screenings have become a leading
contender in this context. In this review, we give an introduction to the
foundations of structure-based virtual screenings, and survey their progress
in the past few years. We outline key principles, recent success stories, new
methods, available software, and promising future research directions. Virtual
screenings have an enormous potential for the development of new small-
molecule drugs, and are already starting to transform early-stage drug
discovery.

###### keywords:

drug discovery, structure-based virtual screenings, molecular docking, ultra-
large libraries, machine learning, deep learning, GPU acceleration, free
energy simulations

###### Contents

  1. 1 INTRODUCTION
  2. 2 FUNDAMENTALS
    1. 2.1 Virtual Screenings
    2. 2.2 Molecular Dockings
    3. 2.3 Receptor Structures and Preparation
    4. 2.4 Ligand Preparation and Ligand Libraries
  3. 3 DOCKING-BASED ULTRA-LARGE VIRTUAL SCREENINGS
    1. 3.1 Ultra-Large Ligand Libraries
      1. 3.1.1 Public Virtual Libraries
      2. 3.1.2 Commercial Libraries
    2. 3.2 History and Success Stories
    3. 3.3 Software and Automated Routines for ULVSs
      1. 3.3.1 VirtualFlow
      2. 3.3.2 UCSF DOCK
  4. 4 SYNTHON-BASED VIRTUAL SCREENINGS
    1. 4.1 V-SYNTHES
  5. 5 DEEP LEARNING APPROACHES TO MOLECULAR DOCKING
    1. 5.1 Scoring Functions
    2. 5.2 Binding Classification Functions
    3. 5.3 Binding Pose Prediction
    4. 5.4 Full Dockings
  6. 6 MACHINE LEARNING APPROACHES TO VIRTUAL SCREENINGS
    1. 6.1 Deep Docking
    2. 6.2 Active Learning Workflow with AutoQSAR/DeepChem (AQ/DC)
    3. 6.3 MolPAL
  7. 7 HARDWARE ACCELERATION OF MOLECULAR DOCKINGS WITH GPUS
  8. 8 HIGH-THROUGHPUT FREE ENERGY SIMULATIONS
  9. 9 CONCLUSIONS AND OUTLOOK

##  1 INTRODUCTION

The development of novel small-molecule drugs is a highly challenging process,
which is time-consuming and expensive [1]. One critical step in this process
is the discovery and optimization of hit and lead compounds. In the past few
decades, experimental high-throughput screening (HTS) has been the primary
method of discovering new hit compounds, and this approach is still the
dominant method today. However, HTSs often suffer from several major
disadvantages and challenges:

  * •

Time. HTSs can be time-consuming, e.g. because the binding assays that will be
used in the screen have to be prepared and optimized. The ligand library that
will be screened has to be prepared as well. The HTS itself can also be time-
consuming, depending on the binding assays used and the equipment that is
available to carry out the screen.

  * •

Costs. HTSs tend to be quite expensive, not least because of the robots that
are needed to carry out the screens, as well as the physical ligand library
[2].

  * •

Potency. Initial hit compounds obtained by HTSs are often only moderately
biologically active, which can in most cases be attributed to the fact that
the screened libraries are of relatively small size (typically tens to
hundreds of thousands of ligands).

  * •

Challenging targets. As hits obtained by HTSs usually do not lead to very
high-affinity binders, challenging target sites, such as flat protein-protein
interactions or allosteric sites, can often not be successfully targeted.

  * •

Scaffolds. In HTSs that lead to sufficiently good hit compounds, the number of
obtained hits and thus scaffolds are often quite limited. Furthermore, the
same screening libraries (and thus scaffolds) are often used again and again
in HTSs for different drug development campaigns, strongly limiting the
chemical space of molecules that is explored for discovering new hit
compounds.

  * •

Specificity HTSs typically do not take into account whether the discovered
hits are specific to the targeted receptor or are possibly binding to other
biological macromolecules as well.

  * •

Binding mechanism. HTSs do not provide mechanistic insight into how the
ligands are binding to their target receptors. In phenotypic kind of screens,
it is typically not even known to which receptor the ligand is binding. [] Hit
compoundA molecule identified in a (virtual or physical) screening procedure
that exhibits a sufficiently strong activity. Lead compoundA hit compound that
was selected for further optimization regarding certain properties, such as
potency, specificity, druglikeness, pharmacokinetic and pharmacodynamic
properties. High-throughput screening (HTS)Experimental technique in drug
discovery using robotics to test thousands of molecules for their biological
activity.

Virtual screening approaches can mitigate all of the above problems, each to a
different extent. Virtual screenings can be very fast and cost-effective. They
can lead to highly potent initial hit compounds, and address even challenging
target sites such as protein-protein interactions. In structure-based virtual
screenings (SBVSs), the binding mode of the ligand to the target receptor is
predicted, and thus (predicted) mechanistic insights are available. Virtual
screens can also lead to larger number of hit compounds with different
scaffolds, thus providing a range of backup compounds.

[] Structure-based virtual screenings (SBVS)Screens that are based on
molecular dockings which require the structure of the target receptor.
SynthonFragment together with reactivity information on how it can be used
with other fragments to synthesize complete molecules. Ligand-based virtual
screens (LBVS)Screens that use the binding information of other ligands, but
use no receptor structure.

In this review, we first give an introduction to some of the fundamentals of
virtual screenings (Section 2). Afterwards, we give an overview of recent
developments regarding virtual screening approaches for drug discovery,
starting with ultra-large virtual screens (ULVS, Section 3). Ultra-large
virtual screens in which more than 100 million ligands are screened. The
subsequent sections cover approaches that can speed up ultra-large virtual
screenings, and make them more cost-efficient. Among these approaches are
synthon-based virtual screens (Section 4), deep learning (DL) approaches to
molecular docking (Section 5), machine learning (ML) approaches to virtual
screens (Section 6) that combine structure-based virtual screens (SBVSs) with
ligand-based ultra-large virtual screens (ML-LB-ULVSs), and hardware
acceleration via GPUs (Section 7). Thereafter, we look into an approach that
can increase the accuracy of virtual screenings (as well as their
computational costs), namely their coupling with high-throughput free energy
simulations (HTFES, Section 8). Finally, we briefly discuss the potential that
virtual screening approaches have in the future (Section 9). A broad overview
of the topics discussed in this review, and how they relate to each other, is
shown in Figure 1.

Figure 1: Overview of the major topics covered in this review, and how
different techniques can be used in concert within the context of structure-
based virtual screening approaches. Ultra-large ligand libraries (Section 3.1
are the starting point of several modern screening paradigms, such as docking-
based ultra-large virtual screenings (Section 3), synthon-based approaches
(Section 4), and ML-based ULVS (Section 6). Deep learning-based (Section 5)
and GPU-based docking programs (Section 7) can be used to speed up structure-
based virtual screens in any of the three primary virtual screening approaches
mentioned above. Docking-based ULVSs require an ultra-large ligand library in
a ready-to-dock format, while ML- and ligand-based ULVS (ML-LB-ULVS) require
typically a SMILES version of the entire library and only a small fraction in
ready-to-dock format (for training the ML model). Synthon-based approaches use
the initial synthons that define the on-demand libraries for docking, and only
require a small additional fraction of the assembled molecules in ready-to-
dock format. High-throughput free energy simulations can be used as an
additional screening stage to refine the results of all three primary virtual
screening approaches discussed in this review.

##  2 FUNDAMENTALS

Several concepts and underlying methods are used in connection with SBVSs. In
this section, we introduce several of them.

###  2.1 Virtual Screenings

Virtual screenings are procedures, in which a collection of ligands is
computationally screened for their ability to bind to a given receptor
structure. Receptors are most often proteins in the biomedical sciences but
can be any type of biological macromolecule, including RNA or DNA. Virtual
screens can be classified as ligand-based or structure-based. In ligand-based
virtual screens, only the structure of known active compounds is used for the
screening procedure to identify new active compounds. If the 3D structure of
the receptor is available, structure-based methods can be used to carry out
binding free energy calculations to estimate how strong the ligand binds to
the targeted receptor, and the binding mode can be predicted as well. There
are a large number of free energy methods, and because of their high speed,
the predominant type used in virtual screens are molecular docking procedures.
Structure-based virtual screenings are normally preferred over ligand-based
screens when the goal is to identify novel scaffolds, since ligand-based
approaches are often biased towards compounds similar to known binders and do
not provide a docking pose.

Virtual screens can be carried out in multiple stages, where in each
subsequent step the best X compounds are used from the previous step, and
methods of higher accuracy and/or computational are used to improve the
quality of the dockings [3, 4]. This principle is illustrated in Figure 1,
where each stage acts as a funnel.

###  2.2 Molecular Dockings

A large variety of docking programs have been developed in the past several
decades [5, 6, 7, 8]. Docking programs typically consist of two components.
First, a conformational search algorithm that explores the possible binding
modes (conformation) of the ligand and the protein. The second part is the
scoring function, which assigns to each conformation a docking score that
correlates with the predicted binding affinity. The binding mode with the best
score is typically the winning and final docking pose.

Docking algorithms can be classified into four primary categories: Physics-
based (also called force field-based), knowledge-based (also called potential
of mean force-based), empirical (also called regression-based), and machine
learning-based (also called descriptor-based) docking routines that vary in
the methods they utilize [9, 10, 11, 12]. In Section 5, we explore deep
learning-based docking methods, which are a subclass of machine learning-based
methods that have been developed only recently and can greatly speed up the
docking procedures.

###  2.3 Receptor Structures and Preparation

Structure-based docking requires the receptor structure as input. One primary
source for receptor structures is the Protein Data Bank (PDB,
http://www.wwpdb.org/), which is the largest database regarding experimentally
determined protein structures [13]. Here, X-Ray crystallography, nuclear
magnetic resonance (NMR), as well as cryo-electron microscopy (cryo-EM) are
the primary experimental methods to determine the structures of biological
macromolecules. Cryo-EM is the youngest of these techniques and has
experienced tremendous progress regarding its capabilities and resolution.
Recently, even atomic resolution has been achieved [14]. Cryo-EM is
particularly powerful regarding the determination of larger protein complexes,
and thus also for the identification of protein-protein interactions.

[] Linear regressionMethod for predicting the relationship between a dependent
variable and one or more independent variables via a (multi)linear function.
Knowledge-based methodA method in the field of artificial intelligence that
makes decisions based on the knowledge of a human experts stored in a
knowledge base. Physics-based methodMethod that use molecular force fields and
the laws of physics directly in the computations, such as classical mechanics.

An alternative to experimentally determined structures is predicted
structures. Here, homology modeling and de novo structure prediction are the
two primary types of prediction methods. Homology modeling uses a structure of
a homologous protein that is known as a template. De novo structure prediction
does not require such a template and can predict new structures from scratch.
Structure prediction, similar to cryo-EM, has experienced a breakthrough in
the past few years. With the advent of AlphaFold [15, 16], the structure of
almost every protein known has been predicted and made freely available in the
AlphaFold Structure Database [17, 18]. The AlphaFold Structure Database
contains over 200 million protein structures. These recent developments in
structure determination enable structure-based virtual screenings for almost
any target protein.

###  2.4 Ligand Preparation and Ligand Libraries

Virtual screenings require ligand libraries that are used for the screening
procedure. In the case of structure-based virtual screenings, the ligands have
to be in a ready-to-dock format [3]. Preparing ligands into a ready-to-dock
format typically starts from the SMILES format and involves generating
stereoisomers, tautomerization states, predicting protonation states,
computing the 3D coordinates of the ligands, and converting them into the
target formats. The precise formats that are needed depend on the docking
programs that are used. Most docking programs require ligands in the PDBQT,
PDB, MOL2, or SDF formats.

In ligand-based approaches, the ligands can be given in line notation, such as
the SMILES format that is mostly used. However, sometimes 3D pharmacophore
models are used in ligand-based screenings, in which case the ligands might
need to be in a 3D format as well.

[] SMILES”Simplified Molecular Input Line Entry System”, is a line notation of
molecules that encodes its topological structure. PDBQT formatAn extension of
the PDB (Protein Data Bank) format, used by the AutoDock family of docking
programs, that also stores the partial charges (Q) and the AutoDock atom type
(T).

##  3 DOCKING-BASED ULTRA-LARGE VIRTUAL SCREENINGS

Ultra-large virtual screens have emerged as one of the most powerful virtual
screening approaches to date. Virtual screenings have historically been called
ultra-large when over 100 million compounds are screened (though what ultra-
large means might change in the future as computational power and library
sizes increase. Several high-profile papers have been published in the last
few years, including experimental validation, that have demonstrated the
potential of this approach (see Section 3.2 for more details). Ultra-large
virtual screenings have become possible in the past few years due to the
recent advancements of multiple technologies and resources:

  1. 1.

Ultra-large chemical libraries. In the last decade, the first ultra-large
ligand libraries with over 100 million commercially available molecules have
become available.

  2. 2.

Computational resources. The capabilities of university computers clusters,
national supercomputers, and commercial cloud computing infrastructures have
improved substantially. Through these computational resources, sufficient
computational power has become available to the scientific communities to
carry out ultra-large virtual screenings.

  3. 3.

Software. Software platforms, such as VirtualFlow [3, 19, 20], that can screen
ultra-large ligand libraries using the above-mentioned computational
resources, including cloud computing infrastructure, became freely available.

We will review available compound libraries and software in the sections
below.

###  3.1 Ultra-Large Ligand Libraries

Ultra-large ligand libraries can be of different types. They can be public,
commercial, or of proprietary nature [21]. Here, public refers to the free
availability of the ligand database without general commercial availability,
while commercial libraries contain compounds that are commercially available
for purchasing. Proprietary libraries on the other hand are generally not
available for purchasing, and usually only available for internal use by the
company that possesses the library. Commercial libraries of ultra-large size
contain mostly compounds that do not yet exist physically but can be
synthesized efficiently on-demand, meaning upon ordering, by compound vendors
with a certain success probability. These libraries are therefore also called
on-demand libraries. Below we review ultra-large ligand libraries of public
and commercial nature. Details on some proprietary libraries can be found in
[21].

####  3.1.1 Public Virtual Libraries

Public virtual libraries can be useful and interesting because they allow to
explore and test compounds via virtual screenings that are beyond current
commercial availability, and with that vastly increase the chemical space that
can be explored. Below we look at two types of these ultra-large public
virtual libraries.

##### GDBs.

The Generated Databases (GDBs) by the Reymond Research Group likely were the
first ligand libraries of ultra-large scale [22, 23]. The GDB-11, containing
all molecules with up to 11 atoms of type carbon, nitrogen, oxygen, and
fluorine that satisfy certain valency, chemical stability, and synthetic
accessibility criteria contained 110.9 million stereoisomers. Subsequently,
additional GDB libraries were published. Among them are the GDB-13 containing
molecules with up to 13 atoms resulting in 977 million molecules, as well as
the GDB-17 with up to 17 atoms of the aforementioned atom types giving rise to
166 billion molecules [24, 25]. While these libraries are among the first of
ultra-large size, one disadvantage is that these libraries are only available
in the SMILES format. Another disadvantage is that the compounds are not
commercially available for direct purchasing. Rather, custom synthesis of
these molecules is mostly required, which is possible via commercial companies
but can be expensive and time-consuming. To address this problem, GDBChEMBL
has been created, a library that contains only compounds from the GDB-17 that
are similar to molecules in ChEMBL [26]. However, it is of relatively small
size and only contains 10 million compounds. A similar library, GDBMedChem,
was created that contains 10 million compounds from the GDB-17. It only
contains compounds favorable from a medicinal chemistry point of view.

##### KnowledgeSpace.

The KnowledgeSpace is another chemical space, which is similar to eXplore (see
below), but more difficult to access in general [27, 28]. Like eXplore, it is
based on commercially available building blocks and reactions known from the
literature. However, there is no commercial service available that readily
provides these compounds. With $2.9*10^{14}$ molecules, this space might be
the largest public library for drug discovery available. It can be accessed
via infiniSee from BioSolveIT [28] but is not available in a ready-to-dock
format.

####  3.1.2 Commercial Libraries

Ultra-large commercial libraries are based on a smaller number of fragments
and combinatorial chemistry, which allows combining of the fragments via
combinatorial chemistry to complete molecules. One important aspect regarding
these on-demand libraries is their chemical diversity. Combinatorial libraries
like these might lead to large numbers of compounds, but do the resulting
compounds also represent a diverse chemical space, rather than a small region
in the chemical space with highly similar compounds? Two recent studies have
looked into this question. They concluded that the resulting chemical spaces
are surprisingly diverse and more diverse than typical physical HTS libraries
[29, 30]. Below we review the major ultra-large on-demand libraries that are
available.

##### ZINC Libraries.

The two major disadvantages of the GDBs, non-commercial availability and non-
ready-to-dock format were first addressed by the ZINC libraries [31, 32, 33,
30]. ZINC15 (published in the year 2015) was the first ultra-large ligand
library of commercially available compounds in a ready-to-dock format, with
initially approximately 120 million compounds [33]. The library has steadily
grown since then and has reached approximately 1.5 billion molecules to date,
out of which approximately 750 million are in a ready-to-dock format. A more
recent version of the ZINC library is called ZINC20 (published in 2020) [30].
ZINC contains and aggregates the catalogs of hundreds of different compound
vendors and other chemical libraries such as ChEMBL.

##### REAL Database.

The REAL Database from Enamine was the first library available from a single
chemical compound vendor reaching over 100 million compounds. The first
version was published in 2007, containing approximately 29 million molecules
[34]. The REAL Database has grown over time, and the latest version of the
REAL Database contains approximately 5.5 billion compounds [35] that comply
with Lipinski’s rule of five as well as Veber’s rule. The majority of the
compounds in the latest ZINC databases are from the REAL Database from
Enamine. The typical time until shipping is 3-4 weeks, and the success rate of
synthesis is approximately 80%.

##### REAL Space.

The REAL Space, another on-demand library from Enamine, can be considered to
be the larger sibling to the REAL Database. The REAL Space is mostly compliant
with Lipinski’s rule of five, but not completely [36]. This can be an
advantage since there exist many drugs that are not rule of five compliant
[37]. The latest version of the REAL Space (year 2022) contains approximately
31 billion molecules [38]. The REAL Space and the REAL Database overlap
regarding the molecules they contain, with approximately 50-70% of the
compounds of the REAL Database being part of the REAL Space as well. In other
words, approximately 10% of the REAL Space is contained in the REAL Database.
The delivery time and synthesis success rate are roughly the same for the REAL
Space and the REAL Database (approximately 80%).

[] Lipinski’s rule of fiveA rule of thumb that estimates whether a compound is
druglike, specifically ($\textnormal{molecular weight}\leq 500$,
$\textnormal{logP}\leq 5$, hydrogen bond acceptor count $\leq 10$, and
hydrogen bond donor count $\leq 5$. Veber’s ruleA rule of thumb that estimates
whether a compound is druglike, specifically less than 10 rotatable bonds and
a topological polar surface area of less than 140Å2.

##### CHEMriya.

Another on-demand library, CHEMriya, is provided by Otava Chemicals [39]. The
first version was released in the year 2021, and the latest version contains
approximately 12 billion molecules. It is based on approximately 33,000
building blocks, and around 45 chemical reactions to combine these building
blocks into complete drug-like molecules. CHEMriya can be accessed via
infiniSee from BioSolveIT [28], but is not yet available in a ready-to-dock
format.

##### GalaXi.

WuXi AppTec also provides an on-demand chemical library, called GalaXi [40].
The latest version of GalaXi contains approximately 8 billion molecules [28].
The molecules can be synthesized within 4-8 weeks, and the success rate is
approximately 60-80% [40]. Galaxi can be accessed via infiniSee from
BioSolveIT [28], but is not available in a ready-to-dock format.

Regarding the major commercial on-demand libraries that are available, the
REAL Database and REAL Space from Enamine, CHEMriya from Otava Chemicals, and
the GalaXi Space from WuXi AppTec, one might wonder if and how much these
libraries overlap in the chemical space. A recent study has looked at this
aspect and found that the overlap is marginal between the REAL Space,
CHEMriya, and the GalaXi Space [41]. These three libraries contained at the
point of study approximately 33 billion molecules jointly together. The number
of molecules that were present in at least two of these libraries was less
than 50 million, which is less than 1% of the total number of molecules under
consideration.

##### eXplore.

Another space that recently appeared is eXplore from eMolecules, containing
2.8 trillion molecules, being the largest on-demand chemical space for drug
discovery to date [42, 28]. Like the KnowledgeSpace, eXplore is based on
building blocks that are commercially and readily available from various
chemical compound vendors. In contrast to KnowledgeSpace, the synthesis steps
that are used to combine the fragments into molecules are based on 40 robust
reactions, mostly consisting of only one or two synthesis steps, facilitating
the synthesis of the compounds. The final molecules can either be synthesized
by the customers, or eMolecules can synthesize the molecules or for a fee.
eXplore can be accessed via infiniSee from BioSolveIT [28] but is not
available in a ready-to-dock format.

##### Freedom Space.

Another recent on-demand space is the Freedom Space from Chem-Space, located
in Ukraine [43, 28]. This library is based on fragments from various compound
vendors as well as known reactions. The library consists of 201 million
compounds, 74% of which comply with Lipinsiki’s rule of five, and is based on
fragments from various compound vendors. The synthesis is carried out by
contract research organizations, and the delivery time is approximately 4
weeks. The Freedom Space can be explored via infiniSee from BioSolveIT [28],
and can be downloaded in the SMILES format from the webpage of Chem-Space
(https://chem-space.com/compounds/freedom-space).

##### VirtualFlow Libraries.

The VirtualFlow open-source project currently provides two libraries in ready-
to-dock format on the project homepage (https://virtual-flow.org/, [3]). The
first library is the REAL Database from Enamine (version of the year 2018),
containing 1.4 billion molecules. The second library is ZINC library (year
2018), containing 1.4 billion molecules as well. In comparison to the original
libraries, the VirtualFlow versions are completely in a ready-to-dock format
and can be screened with VirtualFlow as well. In contrast, the original
ZINC15/ZINC20 library so far only provides approximately 50% of its molecules
in a ready-to-dock format. And Enamine provides the REAL Database only in the
SMILES format.

###  3.2 History and Success Stories

Ultra-large virtual screenings are relatively new. Below we summarize some of
the historical milestones, as well as major success stories.

##### Gorgulla (2018).

To our knowledge, the first ultra-large virtual screenings were reported in
the dissertation of the author in the year 2018 [44]. In this thesis,
VirtualFlow, an open-source virtual screening platform freely available to
anyone was published (for more details see Section 3.3.1). VirtualFlow was
applied in this work to prepare two snapshots of the ZINC library in a ready-
to-dock format, one from the year 2014 containing 133 million ligands, and one
from the year 2016 containing approximately 300 million ligands. The ultra-
large virtual screens reported included a 130 million compounds screen against
the target EBP1, a 180 million compound screen against the KIX-domain of MED15
to disrupt the MED15-SREBP interaction, and a 300 million compound screen
targeting KEAP1 to disrupt the KEAP1-NRF2 interaction. In addition, this work
provided a theoretical foundation on why the true hit rate is expected to
increase with the size of the screened library.

##### Lyu et al. (2019).

In 2019, the first peer-reviewed ultra-large virtual screens were published by
Brian Shoichet and John Irwin (the curator of the ZINC library) [45], and
contained extensive experimental validation. The first target in this study
was the D4 dopamine receptor, a G-protein-coupled receptor (GPCR), that was
screened with 138 million compounds. Picomolar compounds were discovered in
this work directly from the virtual screen. The second target was AmpC
$\beta$-lactamase, against which 99 million compounds were screened. Low-
nanomolar binders were identified after optimization based on similarity
searches of analog compounds in the same library, as well as their docking
scores. This study, therefore, demonstrated that virtual screens and molecular
docking cannot not only identify initial hits but also find improved analogs
of existing hits. Another important result of this study is that it was
experimentally observed that the true hit rate improves with the docking
score, and thus the scale of the library that is screened. This result matches
the theoretical prediction of this phenomenon in the author’s dissertation
[44].

[] GPCRA large family of membrane proteins that convert extracellular signals
into intracellular responses transduced primarily by G-proteins and
$\beta$-arrestins.

##### Stein et al. (2020).

In this study, the first results of ligands originating from ultra-large
virtual screenings involving animal studies are reported [46]. Here, the
Melatonin MT1 receptor, which is a GPCR, was targeted with 150 million
compounds from the ZINC library. The most potent identified ligand had a
binding affinity in the picomolar range. Subsequent optimization lead to two
inverse agonists, and mouse studies with these compounds showed that these
compounds modulate the melatonin receptor biology in a target-specific way.

##### Gorgulla et al. (2020).

In the year 2020, the VirtualFlow platform was published in Nature (see
Section 3.3.1 for more details), along with an experimental demonstration
study targeting the KEAP1-NRF2 interaction [3]. This study was the first to
demonstrate that ultra-large virtual screenings can be used to target protein-
protein interactions and the first study in which over 1 billion molecules
were screened. The most potent compounds obtained were in the low nanomolar
range and were able to disrupt the KEAP1-NRF2 interaction. In this study, a
ready-to-dock version of the REAL Database from Enamine (version of the year
2018) was published, containing 1.4 billion molecules (see 3.1 for more
details).

##### Alon et al. (2021).

In this study, the $\sigma 2$ GPCR was targeted with ultra-large virtual
screens [47]. In total 31 compounds were obtained with submicromolar binding
affinity. Three hits were optimized regarding their selectivity and their
binding affinity and reached 250-fold selectivity relative to the $\sigma 1$
receptor. Also in this study, it was observed experimentally that the true hit
rate improves with the docking score, similar to the study of Lyu [45].

##### Kaplan et al. (2022).

Another GPCR, the 5-HT2A receptor, was targeted in an ultra-large virtual
screen involving 75 million tetrahydropyridines [48]. Several compounds with
low micromolar activity were discovered. Structure-based optimization led to
compounds with half-maximal effective concentration values of up to 41
nanomolar. Cryo-electron microscopy structures of the ligand-protein complexes
were obtained, confirming the predicted binding modes by the docking program
(DOCK) that was used in this study.

##### Fink et al. (2022).

The $\alpha_{2A}$ adrenergic receptor was used as the target in another ultra-
large virtual screen in which 301 million compounds from the ZINC20 library
were docked [49]. Compounds with up to 12 nanomolar binding affinities were
obtained. Also in this study, cryo-electron microscopy structures of the
ligand bound to the protein confirmed the predicted binding modes of two
compounds, and these structures served as the starting point for optimizing
the compounds.

###  3.3 Software and Automated Routines for ULVSs

To carry out ultra-large virtual screenings, a large number of docking
procedures have to be carried out. Most docking programs only support docking
a single ligand at a time, therefore virtual screening platforms or automated
procedures that deploy the docking programs in parallel are required. Here, we
outline two of these approaches.

####  3.3.1 VirtualFlow

VirtualFlow was the first drug discovery platform dedicated to ultra-large
structure-based virtual screens [44, 3]. It is freely available, open-source,
and everyone is welcome to contribute to the project (https://virtual-
flow.org/). The VirtualFlow platform consists of two modules, as well as
ultra-large ligand libraries in a ready-to-dock format. The two modules of
VirtualFlow are VFLP (VirtualFlow for Ligand Preparation), and VFVS
(VirtualFlow for Virtual Screening). An overview of the workflow of
VirtualFlow is shown in Figure 2. VFLP and VFVS can be run using large numbers
of CPUs in parallel to achieve a high ligand throughput. Both modules exhibit
a perfectly linear scaling behavior with respect to the number of CPUs that
are used, which was demonstrated with up to 160,000 CPUs [3].

Figure 2: Overview of the VirtualFlow drug discovery platform. The first
module of VirtualFlow, VFLP, can prepare large numbers of molecules from the
SMILES format into a ready-to-dock format. The second module, VFVS, is able to
carry out ultra-large structure-based virtual screens. Here, a variety of
different docking programs can be used to dock the ligands to the target
protein. The screen can be set up in a multistaged manner to improve accuracy
while increasing the computation time only moderately. VFVS can also be used
to optimize hit and lead compounds by searching the ultra-large ligand
libraries for analogs, creating a custom analog library, and then screening
this custom analog library again with VFVS.

VFLP can generate ultra-large ligand libraries from the SMILES format into the
ready-to-dock PDBQT format. The preparation procedure includes the calculation
of the stereoisomers, tautomerization states, protonation states, and 3D
coordinates of the molecules.

One of the features of VFVS is that it supports a variety of external docking
programs, such as AutoDock Vina [50], Smina [51], QuickVina 2 [52],
QuickVina-W [53], Vina-Carb [54], or VinaXB [55]. Later GWO Vina and PLANTS
were added [19, 20]. Most of these docking programs have special features,
such as blind-docking capabilities (QuickVina-W), or enhanced accuracy for
docking carbohydrates (Vina-Carb). VirtualFlow was used for COVID-19 drug
discovery, where approximately 40 different sites on COVID-19 related proteins
were targeted, resulting in over 40 billion docking instances [56]. VFVS can
be used in a single or multi-staged manner, where more accurate docking
scenarios are used in subsequent screening steps. VFVS supports multiple ways
in which the accuracy can be increased, including the increase of the
conformation search by the docking programs, carrying out ensemble-dockings
(to account for protein backbone flexibility), consensus dockings, or by
including side-chain flexibility via the docking programs themselves.

####  3.3.2 UCSF DOCK

Several of the success stories listed above were carried out by using the
program UCSF DOCK 3.7 [57]. DOCK is a rich docking program that supports many
advanced features, but was not initially designed for ultra-large virtual
screenings, and therefore applying it to ultra-large ligand libraries can be
more complicated than using a platform that is dedicated to ultra-large
virtual screens. A detailed protocol was published by the creators of DOCK on
how one can use the program to carry out ultra-large virtual screens [58]. The
protocol consists of over 100 steps, that not only include the virtual
screening procedure, but also many other steps such as the preparation of the
molecules as well as postprocessing steps.

##  4 SYNTHON-BASED VIRTUAL SCREENINGS

Some of the ligand libraries mentioned in this manuscript are available in
enumerated form, meaning that each ligand is represented explicitly in a
molecular file format such as PDBQT or SMILES. An alternative representation
for combinatorial libraries such, as the REAL Database, REAL Space, GalaXi
Space, and CHEMriya, is to present the library implicitly via the initial
fragments and the reactions that are used to combine these fragments into
molecules. The fragments come along with information on how they can react
with other fragments. Those fragments together with their reactivity
information are called synthons. The advantage of the synthon-based
representation is that it requires dramatically less storage space than the
enumerated representation mode, typically megabytes vs terabytes. One
disadvantage is that the molecules in the synthon representation are not in a
ready-to-dock format.

###  4.1 V-SYNTHES

V-SYNTHES is a virtual screening method that uses the synthon representation
of the REAL Space from Enamine [59]. In their approach, they dock first all
the fragments and then assemble complete molecules out of a subset of the
fragments. The selection is based on the docking scores of the fragments, and
to ensure sufficient diversity several criteria are applied, such as that one
reaction type cannot be part of more than 20% of the chosen synthons. Finally,
the assembled molecules are docked against the target receptor (see also
Figure 1). The primary advantage compared to ultra-large virtual screens is
that only a few million compounds are typically docked in this approach, while
still exploring an ultra-large ligand library. To demonstrate the V-SYNTHES
approach, in their paper the authors applied the method to the cannabinoid
receptors, which led to fourteen submicromolar ligands directly out of the
screen. Virtual screening-based optimization of the initial hits led to
compounds with improved binding affinity, the best compounds having a binding
constant of 0.9 nanomolar, which is an excellent result.

##  5 DEEP LEARNING APPROACHES TO MOLECULAR DOCKING

Machine learning-based docking programs are one of the four types of docking
programs, and they use a relatively large number of descriptors [9, 10, 11,
12]. Like empirical docking programs, machine learning-based docking programs
need training on data [9]. Even though empirical scoring functions can be
considered to be machine learning-based as well (e.g. because they often use
multivariate linear regression), they use parameterized functions, whereas the
term machine learning-based docking programs refer to nonparametric methods
that can select the final functional form [11]. Machine learning-based docking
programs have a much larger number of descriptors (variables) than empirical
docking methods [9].

The first machine learning methods for molecular docking appeared around 2004
[9]. Deep learning is a subdivision of machine learning, and in the past few
years, the first deep learning approaches have been developed for molecular
docking. One advantage of deep learning approaches is that they can be orders
of magnitude faster compared to classical docking programs [60]. High
computational speeds become crucial when processing large ligand datasets,
implying that deep learning methods can be particularly beneficial for
screening ultra-large libraries. Apart from considerations of speed, deep
learning methods have the potential to become more accurate in the future in
terms of their accuracy regarding the predicted binding affinity and the
predicted docking pose than traditional docking programs. In this section, we
give an overview of deep learning approaches related to molecular docking.

Docking programs predict the binding pose of a ligand to a protein, along with
a score that characterizes the strength of the interaction. Historically, deep
learning-based scoring functions were developed first, while the first pose-
prediction methods utilizing deep learning appeared only recently. Deep
learning-based scoring functions are regression models that utilize known
datasets of ligand-protein interaction pairs to predict the strength of an
interaction by assigning a score. New deep learning-based docking programs
that include both scoring and pose prediction are still very rare. However,
deep learning-based scoring functions and pose prediction methods can be
combined into a full docking program, due to their modularity. Here, a deep
learning-based scoring function can for instance be used to score the binding
poses obtained by a deep learning-based pose-prediction method. Alternatively,
a deep learning-based scoring function can be used in combination with
existing docking programs to (re)rank the docking poses generated by the
existing docking program by reevaluating the generated conformations by the
new scoring function. A third possibility is to combine the deep learning-
based scoring functions with existing sampling methods, where the scoring
function is used as the cost function that is minimized during the
optimization procedure.

The new docking methods can be used in ultra-large virtual screenings, e.g.
with VirtualFlow, which already supports some of the new deep learning-based
methods out of the box. This can, in principle, lead to substantial speed-ups
compared to the deployment of traditional docking programs in virtual
screening campaigns.

###  5.1 Scoring Functions

A wide variety of deep learning-based scoring functions have been developed in
the past several years. Here in this section, we look at pure scoring
functions that were published without a sampling algorithm. To our knowledge,
historically one of the first such methods was NNScore 2.0 [61]. Later, a
large number of other deep learning-based scoring functions have been
developed. Among them are Pafnucy [62], DeepAffinity [63], DeepBindRG [64],
OnionNet [65], PotentialNet [66], KDEEP [67], DeepAtom [68], TopologyNet [69],
and Math-DL [70], Erdas-Cicek2019 [71], Cang2018 [72], Atomic CNN [73],
Francoeur2020 [74], and Zhu2020 [75]. Here, as well as in the next paragraph,
the name of the first author followed by the publication year was used as the
name for the method in case no proper name was provided by the authors. For
reviews on scoring functions, as well as for binding classification functions
for protein-ligand binding see also [76, 77, 78, 79].

###  5.2 Binding Classification Functions

Closely related to scoring functions are functions that try to distinguish
ligands between binders and non-binders for a specific target, which is a
classification rather than a regression task. We refer to this task in this
review as binding classification. Binding classification is also referred to
as virtual screening task by some authors because classification functions are
particularly useful in virtual screenings [79]. Several different
classification methods based on deep learning have been developed, such as
NNscore [80], AtomNet [81], DeepVS [82], Ragoza2017 [83], DenseFS [84],
Lim2019 [85], Torgn2019 [86], Tanebe2019 [87], Tsubaki2019 [88], Morrone2020
[89], Sato2010 [90], Li2019 [91], Sato2019 [92], BindScope [93], and Lim2019
[94].

###  5.3 Binding Pose Prediction

The major reason why classical molecular docking methods are relatively slow
is that they typically sample large numbers of ligand-protein conformations,
and assign each of them a score. Deep learning methods for pose-prediction can
eliminate the sampling step by directly predicting the conformation of the
ligand-receptor complex.

The first pose prediction method based on deep learning might have been
PoseNetDiMa [95], which uses a graph-convolution neural network and allows for
flexible protein-ligand docking, where the residues at the binding site are
allowed to be flexible. Another method from the same group that uses
intermolecular Euclidean distance matrices (EDMs) was reported in [96]. Both
methods are for local dockings (rather than blind dockings). The software of
both methods is not available for download at the time of writing, but the
software of the second paper will become available after publication in a
peer-reviewed journal (M. A. Lill, personal communication, October 31st 2022).

DeepDock is another pioneering pose prediction model, which is based on
geometric deep learning [97]. However, it is rather slow according to [98].
EquiBind is another geometric and graph-based deep learning model, which is
extremely fast with a fraction of a second per ligand using a 16-core CPU or a
GPU [98]. Both DeepDock and EquiBind are freely available. TANKBind has tried
to improve upon the results of EquiBind [99]. TANKBind improves on the
accuracy of EquiBind by using trigonometric constraints in the model, and by
dividing the protein into multiple regions.

Both EquiBind and TANKBind are substantially faster than traditional docking
programs. DiffDock, which is a diffusion generative model, was developed by
the same research group as EquiBind, and their results indicate that it can be
more accurate than any of the two previously mentioned blind docking methods
[100]. On the other hand it is considerably slower than EquiBind or TANKBind,
however, it is still faster than most traditional docking programs.

Most of the above pose prediction methods carry out blind pose prediction,
meaning that the entire protein surface is considered as potential binding
area, rather than a restricted binding site.

###  5.4 Full Dockings

Complete docking programs using deep learning, meaning for both pose
prediction as well as scoring, are still very rare as most approaches so far
use deep learning either on pose prediction or on scoring. GNINA [101, 102,
103, 104, 105, 106], is a fork SMINA [51], and SMINA itself is a fork of
AutoDock Vina [50]. GNINA was one of the first full docking programs using
deep learning. It uses convolutional neural networks (CNNs) for the scoring
function and uses a traditional sampling algorithm to explore the
conformational space. Another docking program that is conceptually similar to
GNINA in that it uses CNNs in combination with traditional search algorithms
is MedusaDock [107] in combination with MedusaNet [108]. MedusaNet is a CNN-
based scoring function, and MedusaDock provides the sampling algorithm.
MedusaGraph is another docking method of the same research group, which is
based on graph neural networks [109]. In contrast to MedusaDock-MedusaNet, it
can directly predict the docking pose without extensive sampling using
traditional methods. MedusaGraph is 10-100 times faster than traditional
docking programs. However, it requires an initial docking pose, which can be
generated for instance with MedusaDock, which subsequently reduces the
aforementioned speedup. MedusaGraph uses the scoring function MedusaScore
which is part of MedusaDock. MedusaNet and MedusaGraph are freely available.
TANKBind, which was mentioned in the previous subsection, is a full docking
program as well [99].

##  6 MACHINE LEARNING APPROACHES TO VIRTUAL SCREENINGS

Deep learning-based docking programs are one way to use deep learning in
structure-based virtual screenings to reduce computational costs and
potentially improve the accuracy of the dockings. Another way to use deep
learning, and more generally machine learning in virtual screenings is to
apply it on the screening level rather than only on the docking level. Here,
ligand-based machine learning models are usually trained by carrying out
molecular docking on a small subset of compounds from the entire ultra-large
ligand library. This elegant combination of LBVSs and SBVSs in ML-LB-ULVS can
reduce computational costs dramatically. Subsequently, the entire library is
screened with the trained ligand-based ML model (see also Figure 1). We
outline several of these deep learning approaches below, which focus on ultra-
large structure-based libraries.

###  6.1 Deep Docking

Deep Docking was one of the first deep learning approaches for ultra-large
virtual screenings [110]. Here, a small number of molecules (typically around
1% of the entire library) is docked, and then a quantitative structure-
activity relationship (QSAR)-based deep learning model is trained on them in
an iterative fashion. The majority of the ligand library is then screened
using the fast QSAR model, resulting in a speed-up of around 100 compared to
the docking of the entire library. The authors have applied their method by
screening over one billion compounds against twelve drug targets. In a
separate paper, the authors applied Deep Docking to SARS-CoV-2 drug discovery,
where they targeted the protein Mpro with approximately 40 billion molecules
[111]. The code of Deep Docking is freely available, and a detailed protocol
of the method is discussed in [112].

[] Quantitative structure-activity relationship (QSAR)A regression or
classification model that relates structural features of molecules to their
biological activities.

###  6.2 Active Learning Workflow with AutoQSAR/DeepChem (AQ/DC)

Similar to the Deep Docking approach, in the AutoQSAR/DeepChem (AQ/DC)
approach, a relatively small number of ligands of the library is used for
training a deep learning QSAR model in addition to molecular fingerprints
[113]. The QSAR model uses graph-convolutional neural networks via the
DeepChem package. Optionally active learning can be used to refine the model
during the workflow. Around 5% of the top hits are then docked with regular
docking programs, resulting in an overall speedup of around 15-20 compared to
the standard ultra-large virtual screenings. In the same paper, the authors
applied their new method to the target proteins AmpC and the D4 dopamine
receptor, which they had targeted previously with standard ultra-large virtual
screens [113, 45]. This resulted in a recovery of approximately 80% of the
previously experimentally confirmed hit compounds.

[] Molecular fingerprintA binary representation of a molecule, where each bit
represents a certain structural property.

###  6.3 MolPAL

Another deep learning-based approach is MolPAL [114], which stands for
Molecular Pool-based Active Learning. MolPAL uses Bayesian optimization as
well as molecular fingerprints and was tested with surrogate models based on
random forest, feedforward neural networks, and directed-message passing
neural networks. Only a small fraction of molecules is docked (around 2.5%),
i.e. ones more likely to be scored favorably, resulting in a speedup of
approximately 40. In their study, roughly 90-95% of the top 50,000 hits in a
virtual screening of around 100 million compounds are recovered. MolPAL is
freely available and open-source.

##  7 HARDWARE ACCELERATION OF MOLECULAR DOCKINGS WITH GPUS

Virtual screenings, in particular at the ultra-large scale, typically require
a large amount of computation time [3]. Therefore, often computer clusters or
the cloud are needed to carry them out. Almost all traditional docking
programs run on CPUs, and typically require seconds to minutes per ligand. In
the previous sections, we have seen multiple ways on how the runtime can be
reduced, by effectively reducing the number of traditional dockings required
via the synthon-based approaches, by combining ligand-based virtual screens
with structure-based screens to give rise to ML-LB-ULVSs, or by using deep
learning-based docking methods. Another approach, which can be used in
combination with the above approaches, is to use graphics processing units
(GPUs) instead of CPUs. GPUs can have up to 10-100 times better
price/performance ratio than CPUs.

Docking programs for GPUs have appeared only in the past few years. Some of
them try to port traditional docking programs to GPUs, such as AutoDock-GPU
[115], Vina-GPU [116], Uni-Dock [117], and MedusaDock GPU [118], while others
are new docking programs tailored for GPUs (e.g. Accelerated CDOCKER [119]).
In addition, most of the deep learning-based docking programs that were
discussed above run on CPUs and GPUs, e.g. GNINA [106], MedusaDock-MedusaNet
[108], MedusaGraph [109], and TANKBind [99].

##### AutoDock-GPU.

AutoDock-GPU [115] is the GPU version of the well-known docking program
AutoDock 4 [120]. AutoDock-GPU is available both as OpenCL as well as CUDA
version and can run on CPU, GPU, and FPGA architectures [121, 122]. Compared
to the single-threaded CPU version, the speedup is up to a factor of around
350 when using GPUs. However, since AutoDock 4 was very slow in general, the
GPU-accelerated version is roughly as fast as more modern CPU-based docking
programs [117]. The authors applied the new software to multiple case studies,
including the docking of approximately 1 billion molecules for COVID-19 drug
discovery in less than 24 hours on the Summit supercomputer.

##### Vina-GPU.

AutoDock Vina [50] is the most cited docking program of all time, with over
20,000 citations at the time of writing. In addition, several benchmark
studies show that it is among the most accurate docking programs. A GPU
version of AutoDock Vina is therefore of great interest. One attempt was the
software Viking, however, no speedup has been reported [116]. The first
successful GPU version of AutoDock Vina with a significant speedup, Vina-GPU,
was published in 2022 [116]. It makes minor modifications to the algorithm of
AutoDock Vina, but the final docking scores are almost identical. Vina-GPU
exhibited a speedup between 21 and 50 when using an NVIDIA RTX-3090 compared
to AutoDock Vina running on a 10-core Intel® CoreTM i9-10900K CPU at 3.7 GHz.

##### Uni-Dock.

Another software building on AutoDock Vina is Uni-Dock [117]. In contrast to
Vina-GPU, it builds on AutoDock Vina 1.2 [123], while Vina-GPU is based on the
original AutoDock Vina. Uni-Dock claims to have greater acceleration than
Vina-GPU, and it also supports multiple scoring functions: Vina, Vinardo, and
the AutoDock 4 scoring functions. Uni-Dock achieves a speedup of over 1000
when running on an NVIDIA V100 32G GPU compared to AutoDock Vina using a
single core of an Intel® Xeon® Platinum 8269CY (Cascade Lake) CPU running at
2.5 GHz. Uni-Dock is not freely available and executable, but available via a
webserver (https://academic.dp.tech/toolkits/uni-dock-serving/).

##### MedusaDock GPU.

MedusaDock GPU [118] is a GPU-enabled version of MedusaDock [107]. The
speedups observed are moderate, in the lower single-digit range, when
comparing MedusaDock running on a single core of an Intel® Xeon® E5-2680 v4
(Broadwell) and MedusaDock GPU running on an NVIDIA TESLA P100 GPU.

##### Accelerated CDOCKER.

CDOCKER (short for CHARMM Docker) and flexible CDOCKER are docking methods
that are part of the CHARMM package [124, 125]. Accelerated CDOCKER is a GPU-
accelerated version of the CDOCKER and flexible CDOCKER methods [119]. It uses
the fast Fourier transform as a key algorithm for the GPU acceleration and
achieves an impressive speedup of approximately 15,000 when using an NVIDIA
GeForce GTX 1080 vs the original CDOCKER running on an Intel® Xeon® Processor
E5645 at 2.4 GHz. CDOCKER also includes a parallel MD-based simulated
annealing method that can run on GPUs in parallel, with an observed speedup of
approximately 20.

##### Deep learning-based docking methods.

Most of the deep learning-based docking programs can use GPUs since deep
learning models can usually be run on GPUs for their evaluation. Among them
are GNINA, MedusaDock-MedusaNet, MedusaGraph, and TANKBind. In addition, most
of the pure scoring functions, classification functions and pose prediction
methods based on deep learning can be run on GPUs. However, they would need to
be implemented as full docking methods to be useful for most virtual screening
campaigns.

##  8 HIGH-THROUGHPUT FREE ENERGY SIMULATIONS

So far in this review, we have seen several examples of how multiple virtual
screening stages can be used to save computation time, e.g. by using a very
fast virtual screen (such as ML-LB-ULVSs or deep learning based docking
methods in standard ULVS) of the entire library as the first stage. Such
virtual screens are generally less accurate than full molecular dockings.
Staging can also be used in the opposite direction, to increase the accuracy
compared to molecular dockings. Here, free energy simulations based on
molecular dynamics (MD) simulations can be used to improve the accuracy of the
binding affinity estimates for the best compounds of the previous stage. Free
energy simulations are in theory exact (relative to the force field used),
provided that the free energy converges (for that the simulations need to run
long enough). They take into account the full dynamics of the molecular
system, can treat all solvent molecules explicitly, and include all enthalpic
and entropic components of the free energy. When applied to a larger number of
systems in an automated way, free energy simulations are also referred to as
high-throughput free energy simulations (HTFESs).

In addition, quantum mechanics/molecular mechanics (QM/MM) methods can be used
to treat the parts of the system (typically the ligand, the binding site of
the protein, and possibly some solvent molecules at the binding site) with
higher accuracy [44]. This can for example be beneficial if metals are located
at the binding site of the protein or part of the ligand.

Hardware acceleration, in particular the usage of GPUs, is key regarding
HTFESs. Multiple molecular dynamics software packages have been optimized for
GPUs in the past 15 years, such as GROMACS [126], AMBER [127], CHARMM [128],
and NAMD [129], which greatly enhances their simulation speed. In principle,
these MD simulation packages can be directly used to carry out free energy
simulations of protein-ligand systems. However, applying them to a large
number of ligands will likely require custom scripting to automate certain
processes. In the past few years, several high-throughput MD and free energy
simulation methods have been developed, which automate many processes and
allow to handle large numbers of protein-ligand systems.

[] Free energy methodsMethods for predicting the free energy differences
between different states of a molecular system. They can be used to predict
the binding affinity between molecules. Molecular dynamics (MD)
simulationComputer simulation of a molecular system in which the movements of
each atom are calculated for many time steps. Quantum mechanics/molecular
mechanics (QM/MM) methodMolecular models that treat one part of the molecular
system on the quantum level and the rest using classical mechanics.

##### HTMD.

One of the first packages dedicated to high-throughput molecular dynamics was
HTMD [130]. It supports a large number of different tasks and features, such
as molecular dynamics, Markov state modeling, clustering, free energy
calculations, and projection methods, adaptive sampling, and can run in the
cloud. The package has been extended to also allow simulation of membrane
proteins in an automated way, and it is freely available for academic groups
[131].

##### BRIDGE.

A package that is dedicated to high-throughput MD simulations that support
free energy calculations is BRIDGE (Biomolecular Reaction and Interaction
Dynamics Global) [132]. It is an open-source web platform and built on top of
the Galaxy platform. BRIDGE supports absolute and relative binding free energy
simulations. It also allows to easily share obtained simulation data and
supports independent verification of the data by external people, which can
for instance be useful for other scientists or during the peer review process.

##### Deep learning-based force fields.

Traditionally, classical force fields such as AMBER and CHARMM have mostly
been used in MD simulations and thus free energy simulations. Recently, deep
learning-based force fields have been developed, such as ANI (Accurate neural
network engine for molecular energies) and AIMNet (atoms-in-molecules net),
which have increased the accuracy of absolute binding free energy simulations
by up to 50% [133, 134]. [] Force fieldA method that predicts or specifies the
forces that act between atoms in a molecular system.

##### Case study with OpenMM.

High-throughput free energy simulations to estimate the ligand-protein binding
affinity have also been carried out on a larger scale in [135]. Here, the
authors have used AutoDock Vina to dock the ligands to the target proteins to
obtain an initial docking pose, and then run free energy simulations using
OpenMM. 56 target proteins were used, and 560 ligands in total. The results
show that the estimated binding free energies have improved in accuracy
compared to the original AutoDock Vina scores. The area under the ROC curve
improved by 22%.

##### Quantum mechanical simulations.

Packages for quantum mechanical simulations suitable for free energy
calculations of protein-ligand systems exist as well. In principle, many of
the standard packages for MD simulations provide some form of QM/MM support,
such as CHARMM [128] and AMBER [127]. However, they are mostly focused on
classical simulations. TeraChem [136] was the first package for quantum
mechanical simulations specifically written for GPUs, resulting in a
remarkably high performance. It can simulate entire proteins on the density
functional theory level, and can also carry out QM/MM simulations. Another
package that is currently developed for exascale supercomputers is NWChemEx
[137], which will support quantum mechanical simulations of molecular systems
considerably larger than possible today.

##  9 CONCLUSIONS AND OUTLOOK

Structure-based virtual screenings have evolved into a powerful tool for drug
discovery in the past few years. Ultra-large virtual screens allow to access
billions of compounds, allowing to find exceptionally tight-fitting ligands
for even highly challenging target sites. This approach allows to drug new
target classes such as allosteric sites or protein-protein interactions, which
with approximately 500,000 types are considered to be one of the holy grails
of drug discovery. Machine learning and deep learning techniques on the
molecular docking and virtual screening levels allow to speed up the
calculations by orders of magnitude, as do other algorithmic approaches such
as synthon-based methods, or the use of hardware accelerators such as GPUs. It
is expected that virtual screenings will further expand the accessed chemical
space by orders of magnitude in the coming years, with no limits in sight
given the vastness of the chemical space of molecules suitable for drug
discovery. Deep learning approaches for molecular docking and for free energy
simulations have the potential to significantly improve the accuracy and
reliability of free energy predictions, though significant works lies still
ahead to achieve this goal. GPUs and other hardware accelerators will aid in
accessing larger regions of the chemical space of druglike molecules in the
future. GPUs will be key for achieving highly accurate free energy
simulations, whether on the classical or the quantum level, which hold great
promise to improve the reliability of binding affinity prediction. In
addition, structure-based virtual screens can be extended to tasks other than
hit and lead discovery and optimization, such as predicting off-target binding
via inverse virtual screenings or combining them with the prediction of
pharmacokinetic properties [138].

In summary, structure-based virtual screens are on the one hand already a
mature technique, and able to be routinely applied in drug discovery campaigns
to identify potent hit and lead compounds and to optimize them. On the other
hand, they have only recently become powerful enough to be of real value in
actual drug discovery projects, and have barely begun to scratch the surface
of what they can achieve in the future. A bright future therefore likely lies
ahead for structure-based virtual screens.

[SUMMARY POINTS]

  1. 1.

Early-stage drug discovery using traditional methods such as experimental
high-throughput screens faces several major problems, such as high costs,
labor, and poor initial hit compounds. Structure-based virtual screenings can
solve most of these problems, by reducing the costs and workload, and by
improving the quality of hit as well as lead compounds.

  2. 2.

Ultra-large structure-based virtual screenings in particular have proven to be
able to discover highly potent hit and lead compounds, as well as to optimize
initial hit compounds. This in turn leads to the ability to find potent
binders to more challenging target classes such as protein-protein
interactions.

  3. 3.

Synthon-based virtual screenings allow exploration of ultra-large ligand
libraries without the need to screen the entire library, but only a small
fraction of it. This allows reducing the computational costs by a factor of
around 100.

  4. 4.

Deep learning methods for molecular dockings can be computationally much
faster than traditional docking methods, and can in some cases already exceed
their accuracy. These methods can be applied during ultra-large virtual
screens as well as in synthon-based approaches.

  5. 5.

Machine learning approaches can be applied on the virtual screening level to
dramatically speed up the computations, which is particularly beneficial when
screening ultra-large libraries. Here, normally ligand-based machine learning
models are trained on a training set obtained by applying structure-based
virtual screens on a smaller part of the library and then applying the trained
model on the entire ligand library.

  6. 6.

GPUs can be used instead of, or in complement with, CPUs. They can provide one
to two orders of magnitude better cost/performance ratio than CPUs. Several
docking programs are available that can make use of GPUs, such as AutoDock-
GPU, Vina-GPU, and most of the deep learning-based docking methods.

  7. 7.

Free energy simulation methods to estimate the binding affinity of ligands to
receptors can be more accurate than molecular dockings. They can model the
complete flexibility of the receptor, as well as solvent molecules. High-
throughput free energy simulations have been devised that can be employed as
an additional stage in structure-based virtual screening approaches.

  8. 8.

There have been multiple successive stories involving ultra-large virtual
screens, which included experimental validation. In these studies, nanomolar
to picomolar binders have been reported for different types of receptors,
including enzymes, GPCRs, and protein-protein interactions.

[FUTURE ISSUES]

  1. 1.

The chemical space of small druglike molecules is estimated to contain over
$10^{60}$ molecules, and even ultra-large virtual screens only sample a tiny
fraction of this space. Further extension of structure-based virtual screens
to larger parts of the entire chemical space of druglike molecules are
promising.

  2. 2.

Deep learning approaches to molecular docking have the potential to
significantly improve their accuracy. This is of high importance, as current
molecular docking programs are highly inaccurate regarding their binding
affinity estimates. Large, high-quality datasets will be key to achieving this
goal with deep learning methods.

  3. 3.

Machine learning approaches to virtual screens hold great promise to enable
the exploration of larger regions of the chemical space than possible today.
The same is true of new methods that reduce the number of molecules that need
to be explicitly screened, and intelligently decide which molecules are
promising for a particular target protein.

  4. 4.

Further utilization of hardware acceleration via GPUs and other hardware (such
as Google’s Tensor Processing Units) has the potential to further reduce the
costs of virtual screens while allowing for large chemical spaces to be
explored.

  5. 5.

High-throughput free energy simulations have great potential to improve the
accuracy in structure-based virtual screens via multi-staging approaches. To
date, only very few tools are available to aid with high-throughput free
energy simulations, and more convenient and easy-to-use tools are needed.
Ideally, these can be applied in an automatic way after docking-based virtual
screening stages.

  6. 6.

Quantum-mechanical scoring functions for molecular docking could improve the
accuracy, in particular when there are metals at the active site. In the long
term, quantum computers might provide substantial speed-ups for quantum
mechanical scoring functions for protein-ligand docking.

  7. 7.

While there exist already a few web servers for virtual screens, they are
mostly not yet available for ultra-large virtual screens. Visual interfaces
that interface with the cloud for ultra-large virtual screens that are easy to
use would facilitate the wider adoption of structure-based virtual screens by
the drug development communities.

  8. 8.

Structure-based virtual screens could be of great value to predict off-target
effects, for instance via inverse virtual screens of specific small molecules
against the entire human proteome. The AlphaFold Structure Database
essentially opened the door for such approaches. This could reduce the risk of
toxicity of new drug candidates.

## DISCLOSURE STATEMENT

C.G. is a cofounder of Quantum Therapeutics Inc., a company that uses
computational methods for drug development. C.G. is a cofounder of Virtual
Discovery Inc., which is a fee-for-service company for computational drug
discovery.

## ACKNOWLEDGMENTS

C.G. would like to thank Brigitte Klein, Konstantin Fackeldey, and Akshat
Nigam for proofreading the manuscript and for their valuable feedback. C.G.
would like thank ChemAxon for a free academic license for their software.

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