Virtual Screening Algorithms in Drug Discovery: A Review Focused on Machine and Deep Learning Methods

Abstract

Drug discovery and repositioning are important processes for the pharmaceutical industry. These processes demand a high investment in resources and are time-consuming. Several strategies have been used to address this problem, including computer-aided drug design (CADD). Among CADD approaches, it is essential to highlight virtual screening (VS), an in silico approach based on a computer simulation that can select organic molecules toward the therapeutic targets of interest. The techniques applied by VS are based on the structure of ligands (LBVS), receptors (SBVS), or fragments (FBVS). Regardless of the type of VS to be applied, they can be divided into categories depending on the used algorithms: similarity-based, quantitative, machine learning, meta-heuristics, and other algorithms. Each category has its objectives, advantages, and disadvantages. This review presents an overview of the algorithms used in VS, describing them and showing their use in drug design and their contribution to the drug development process.

Full text

Virtual Screening Algorithms in
Drug Discovery: A Review Focused
on Machine and Deep Learning
Methods
Tiago Alves de Oliveira, Michel Pires da Silva, Eduardo Habib Bechelane Maia,
Alisson Marques da Silva and Alex Gutterres Taranto
Topic Collection
Artificial Intelligence Applied to Medicinal Chemistry
Edited by
Dr. Osvaldo Andrade Santos-Filho and Dr. Alex Gutterres Taranto
Review
https://doi.org/10.3390/ddc2020017

Citation: Oliveira, T.A.d.; Silva,
M.P.d.; Maia, E.H.B.; Silva, A.M.d.;
Taranto, A.G. Virtual Screening
Algorithms in Drug Discovery: A
Review Focused on Machine and
Deep Learning Methods. Drugs Drug
Candidates 2023,2, 311–334. https://
doi.org/10.3390/ddc2020017
Academic Editor: George
Mihai Nitulescu
Received: 28 December 2022
Revised: 23 February 2023
Accepted: 6 March 2023
Published: 5 May 2023
Copyright: © 2023 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
Review
Virtual Screening Algorithms in Drug Discovery: A Review
Focused on Machine and Deep Learning Methods
Tiago Alves de Oliveira 1,2 ,*, Michel Pires da Silva 2, Eduardo Habib Bechelane Maia 2,
Alisson Marques da Silva 2and Alex Gutterres Taranto 1,*
1Department of Bioengineering, Federal University of São João del-Rei, Praça Dom Helvécio, 74-Fábricas,
São João del-Rei 36301-1601, Brazil
2Federal Center for Technological Education of Minas Gerais (CEFET-MG), Department of Informatics,
Management and Design, Campus Divinópolis, Rua Álvares de Azevedo, 400-Bela Vista,
Divinópolis 35503-822, Brazil
*Correspondence: tiago@cefetmg.br (T.A.d.O.); proftaranto@hotmail.com (A.G.T.);
Tel.: +55-(37)-3229-1175 (T.A.d.O.); +55-(32)-3379-5134 (A.G.T.)
Abstract:
Drug discovery and repositioning are important processes for the pharmaceutical industry.
These processes demand a high investment in resources and are time-consuming. Several strategies
have been used to address this problem, including computer-aided drug design (CADD). Among
CADD approaches, it is essential to highlight virtual screening (VS), an in silico approach based on
computer simulation that can select organic molecules toward the therapeutic targets of interest. The
techniques applied by VS are based on the structure of ligands (LBVS), receptors (SBVS), or fragments
(FBVS). Regardless of the type of VS to be applied, they can be divided into categories depending
on the used algorithms: similarity-based, quantitative, machine learning, meta-heuristics, and other
algorithms. Each category has its objectives, advantages, and disadvantages. This review presents an
overview of the algorithms used in VS, describing them and showing their use in drug design and
their contribution to the drug development process.
Keywords:
drug discovery; virtual screening; machine learning; deep learning; CADD; algorithms;
structural bioinformatics
1. Introduction
The discovery and manufacturing processes for new drugs have been in constant
evolution in the last decade, with benefits and results directly related to the life quality
improvement of the world population [
1
]. The effectiveness of the production and manu-
facturing strategies is increasing, although the costs involved remain a challenge [
2
]. The
development of a new drug has an average cost between 1 and 2 billion USD and could
take 10 to 17 years, from target discovery to drug registration [
3
]. These costs reflect a
time-consuming, laborious, and expensive process, which implies drugs with high added
value and, often, of restricted acquisition, at least for a third of the world’s population [
4
,
5
].
In addition, part of these costs are related to a low success rate, with only 5% of phase
I clinical trial drugs entering the market [
6
]. These impacts are even more significant in
places such as Asia and Africa, where more than 50% of the population lives in poverty or
with a lower average income than in the rest of the world [
4
,
7
]. The conditions that restrict
the access of this fraction of the population to essential medicines contribute to the increase
in the mortality of tens of thousands of people every year, 18 million of which could be
avoided if these medicines became accessible [8].
One way to reduce drug discovery and manufacturing limitations, minimizing cost
and time impacts, is by using computer-aided drug design (CADD), also known as molecu-
lar modeling. In such an approach, the design stages and analysis of drugs are carried out
through a cyclic-assisted process entirely conducted through in silico simulations. These
Drugs Drug Candidates 2023,2, 311–334. https://doi.org/10.3390/ddc2020017 https://www.mdpi.com/journal/ddc

Drugs Drug Candidates 2023,2312
simulation techniques can evaluate essential factors in drug discovery, such as toxicity,
activity, biological activity, and bioavailability, even before carrying out
in vitro
and
in vivo
clinical trials [
9
]. An initial step of the CADD approach is screening virtual compound
libraries, known as virtual screening (VS). VS is a molecule classification method that
exploits biological or chemical properties available in large datasets. The International
Union of Pure and Applied Chemistry (IUPAC) defines VS as computational methods that
classify molecules in a database according to their ability to present biological properties
against a given molecular target [10].
Popular VS techniques originated in the 1980s, but the VS word first appeared in a 1997
publication [
11
]. In the early 1990s, the rapid evolution of computers created the hope that
companies could accelerate the discovery process of new drugs. These computing advances
have enabled several advances in combinatorial chemistry and high-throughput screening
(HTS) technologies, allowing for vast libraries of compounds to be synthesized and screened
in short periods. The VS process aims to choose an appropriate set of compounds, removing
inappropriate structures and limiting the use of a significant number of resources. In this
way, identifying hits with computational methods proved to be a promising approach
because, even before carrying out the biological assays, computational simulations could
indicate which compounds were most likely to be good hits.
Currently, VS represents a crucial step in the early stage of drug discovery due to
it having been proven to be an excellent alternative to HTS, especially in terms of cost–
effectiveness and probability of finding the appropriate result through a large virtual
database [
12
,
13
]. In this context, we present a review of the main VS techniques. This paper
aims to show an overview of the most recent algorithms used in VS, with a particular focus
on machine learning (ML) and deep learning (DL).
The rest of this article is structured as follows: In Section 2, we describe the VS process,
its implementation models, and the steps that compose it. Then, in Section 3, we describe
the ML algorithms that are used with VS and the characteristics that make them suitable
for the composition of CAAD models aimed at similarity evaluation. In Section 4, some
guidelines for in silico models for CADD are presented and discussed. Finally, in Section 4,
we present the conclusions of the exploited and presented strategies.
2. Virtual Screening
VS is an in silico technique used in the drug discovery process [
12
–
16
]. During VS,
large databases of molecular structures are automatically evaluated using computational
methods. With the use of VS, it is expected to identify molecules more susceptible to
binding to the molecular target, typically a protein or enzyme receptor.
VS works like a filter (Figure 1), eliminating more molecules so that the number of
final candidate molecules that may become a drug is much smaller than the initial number.
In Figure 1, molecules that can become a good drug are initially selected because they
have properties that favor their action or are similar to drugs with known functioning.
Candidate ligands are then analyzed, and molecules in which pharmacophoric groups that
indicate the likelihood of toxicity are identified are eliminated. In the next step, the best
poses for candidate ligands are identified. During this VS process, the candidate ligands
can have their compositions and structures readjusted to enhance their properties; mainly,
their pharmacokinetic characteristics (absorption, distribution, metabolism, excretion, and
toxicity (ADMET)). It may require new cycles of optimizations in the composition and
structure of candidate ligands. This way, the ligands are prepared for biological assays
after this process.

Drugs Drug Candidates 2023,2313
Drug-like and Lead-like
Toxicity
Identification of the
best pose
ADME
Figure 1. Virtual screening process.
Furthermore, the VS allows for the selection of compounds in a database of structures
with a greater probability of presenting biological activity against a target of interest.
VS can also eliminate compounds that may be toxic or that have unfavorable specific
pharmacodynamic and pharmacokinetic properties. In this sense, biological assays should
be performed only with the most promising molecules, leading to a lower cost and shorter
development time [13].
There are essentially three main approaches of VS: structure-based VS (SBVS), ligand-
based VS (LBVS), and fragment-based VS (FBVS). The following sections detail these
approaches.
2.1. Structure-Based Virtual Screening
SBVS, also known as target-based VS (TBVS), attempts to predict the best binding
orientation of two molecules to form a stable complex. The SBVS technique encompasses
methods that explore the molecular target’s 3D structure. SBVS is the preferred method
when the 3D structure of the molecular target has been experimentally characterized [
14
].
SBVS tries to predict the likelihood of coupling between candidate ligands and target
protein, considering the binding strength of the complex. The most used SBVS technique is
molecular anchoring due to the low computational cost and the satisfactory results [
15
,
16
].
Molecular docking emerged in the 1980s when Kuntz et al. [
17
] developed an algo-
rithm that explored the geometrically feasible docking of a ligand and target. They showed
that structures close to the correct ones could be obtained after docking. This technique
tries to predict the best position and orientation of a ligand in a binding site of a molecular
target so that both form a stable complex. To this end, it uses the structural and chemical
complementarity resulting from the interaction between a ligand and a molecular target
through scoring functions, often complemented with pharmacophoric restrictions [
18
].
SBVS is often used with proteins and enzymes as receptors, but it can also be used in other
structures, such as carbohydrates and DNA. However, although the approach was promis-
ing, it was only in the 1990s that it became widely used, when there was an improvement
in the techniques used in conjunction with an increase in computational power and greater
access to the structural data of target molecules.
The use of SBVS has advantages and disadvantages. Among the benefits are the
following: (a) there is a decrease in the time and cost of screening millions of small
molecules; (b) there is no need for the physical existence of the molecule, so it can be
tested in silico before being synthesized, (c) there are several tools available to assist SBVS.
As a disadvantage of using SBVS, it can be highlighted that (a) some tools work
best in specific cases but not in more general cases [
19
]; (b) it is difficult to accurately
predict the correct binding position and classification of compounds due to the difficulty of

Drugs Drug Candidates 2023,2314
parameterizing the complexity of ligand–receptor binding interactions; (c) it can generate
false positives and false negatives.
Despite the disadvantages noted above, many studies using SBVS have been devel-
oped in recent years [
12
,
20
–
25
], which shows that although SBVS has weaknesses, it is still
widely used for designing new drugs due to the reduction in time and cost.
2.2. Ligand-Based Virtual Screening
LBVS uses ligands with known biological activity aiming to identify molecules with
similar structures using virtual libraries of compounds. In this approach, the molecular
target structure is not considered. Instead, it follows the principle that structurally similar
molecules may exhibit comparable biological activity. Thus, LBVS tries to identify com-
pounds with similar molecular scaffolds or pharmacophore moieties, increasing the chance
of finding biologically active compounds.
LBVS is performed by comparing the descriptors or characteristics of known molecules
derived from reference compounds and compared with the descriptors of the molecules
of the databases. For this purpose, similarity measures are used. There are several ways
to verify the similarity between two sets of molecular characteristics, but the Tanimoto
coefficient [26] is one of the most used to perform this task [27].
One of the most common LBVS classifications techniques is the number of dimensions
of the represented descriptor (1D, 2D, or 3D):
•
1D descriptors cover molecular properties such as weight, number of hydrogen-bond
donor and acceptor groups, number of rotatable bonds, number and atoms type,
and computed physicochemical properties, such as logP and water solubility (log D),
degree of ionization, and others;
•
2D descriptors are those based on molecular topology. Therefore, they are built
based on the molecular connectivity of the compounds. Two-dimensional descriptors
can exist as structural descriptors and topological indices [
28
]; structural descriptors
are those characterizing the molecule by its chemical substructure. They can be
represented in 2D graphs or binary vectors. For example, the number of aromatic
rings in a molecule, the connectivity index, and the Carbo index (which calculates
molecular similarity) are 2D descriptors. Topological indices define the molecule’s
structure according to its shape and size. Simpler indices characterize the molecules
according to their size, shape, and degree of branching, and more complex indices
consider both the properties of the atoms and their connectivity;
•
3D descriptors provide molecular information in the context of the spatial distribution
of atoms, molecular properties, or chemical groups. 3D descriptors need to consider
molecular conformations, meaning they need to consider the spatial distribution of
particles, molecular properties, or chemical groups [
29
]. Studies have demonstrated
that a single descriptor does not perform better than another for all VS [
30
]. Therefore,
the molecules are, in most cases, described as a set of descriptors. Examples of 3D
descriptors are the electrostatic potential and van der Walls.
LBVS is more suitable for use in the following situations: (a) whenever there is little
information about the structure of the molecular target. In addition, it is used to enrich
the database for SBVS experiments; (b) for targets with large amounts of experimental
data available or where the drug-binding site is not well defined, LBVS methods are
generally superior to SBVS methods [
31
]; (c) the simultaneous use of the LBVS and SBVS
approaches can increase the accuracy of the VS, as the LBVS can eliminate some false-
positive compounds identified as promising by the SBVS technique, increasing the chances
of obtaining good results [11].
The LBVS technique has the following limitations: (a) the currently available LBVS
techniques have not yet shown the desired performance, but the prediction accuracy is
expected to increase rapidly in the coming years [
14
,
20
]; (b) minor chemical modifications
in similar molecules can either potentiate an activity or make it inactive [
32
]. Wermuth [
33
]
shows how relatively modest changes in a molecule with known biological activity can

Drugs Drug Candidates 2023,2315
lead to compounds with very different activity profiles. This is known as activity cliffs [
34
].
In this way, a molecule identified by the LBVS technique as similar to another with known
activity may be inactive, leading to a false-positive result.
2.3. Fragment-Based Virtual Screening
FBVS has also been a powerful approach to finding early hits that can lead devel-
opment [
35
]. FBVS aims to test fragments of low-molecular-weight molecules against
macromolecular targets of interest [
36
]. FBVS usually generates a candidate compound
from a chemical fragment with low molecular weight (generally less than 300 Da), low
binding affinity, and simple chemical structures [
37
]. These compounds are then used
as starting points for drug development. However, due to their low molecular weight,
fragment hits are generally weak binders and need to be made into larger molecules that
bind more tightly to the target to be made into a lead. Therefore, the methods used in FBVS
and the fragment binding modes are critical in fragment-based screening [35,37].
Over the last 20 years, FBVS using nuclear magnetic resonance (NMR) has been a
prominent approach. FVBS has made it possible to obtain a high success rate with increased
development speed and decreased cost of producing new drugs [
36
]. Moreover, there is
no need for intervention by medicinal chemists in the early stages of increasing fragment
molecules and their transformation into molecules with more significant biological activity.
Several FBVS approaches have been used today, and the development of the ligand from a
fragment using ML has gained prominence [38].
The main advantage of using FBVS is the low complexity of the fragments used in the
simulation [
35
], which allows for the use of different techniques (which have pros and cons
that must be analyzed according to the situation) for the development of new compounds
and cost reduction in drug development [35].
3. Virtual Screening Algorithms
VS has become a widely used strategy for drug discovery, and as seen in the previous
section, it can be classified into SBVS, FBVS, and LBVS. Moreover, VS techniques can be
divided according to the algorithms used to perform its pipeline.
In the literature, several taxonomies are used to classify the algorithms, such as those
presented in [
39
–
44
]. However, due to the diversity of the algorithms described in this
work, we chose to use a new taxonomy, as shown in Figure 2, in which the algorithms are
divided into similarity, quantitative, machine learning, meta-heuristic, and others. In the
following sections, we explain the algorithms, describe how they are used, and how they
contribute to the drug discovery process.
3.1. Similarity-Based Algorithms
In similarity-based approaches, algorithms assume that a molecule with a similar
structure to another whose biological activity is known may share similar activity [
16
]. In a
similarity search, a structure with a known biological activity relating to the target is used as
a reference. These reference structures are then compared to a database of known molecules
so that the similarity between them is determined. This similarity can be determined by
a coefficient of similarity applied to all molecules in the database. However, two highly
similar compounds may have different biological activities. Thus, small changes in a
molecule can either generate an active or an inactive compound [
27
]. The methods based
on similarity tend to only require a few pieces of reference information to create predictive
models [
45
]. Therefore, their simplicity and effectiveness have made them widely accepted
by the scientific community. Additionally, the similarity-based algorithms can be combined
with other methods such as ML, meta-heuristics, and others to improve the results.

Drugs Drug Candidates 2023,2316
Figure 2. VS algorithms taxonomy.
The similarity-based algorithms can be divided into 2D, 3D, quantum, and hybrid ap-
proaches. The 2D approaches are the most intuitive VS technique based on the 2D similarity
of substructures compared to various 2D descriptors using a similarity coefficient [
46
–
48
].
Then, in 3D approaches, the similarity search can also be performed through 3D molecular
representations, such as in pharmacochemical [
27
,
32
,
33
,
49
], overlapping volumes [
50
,
51
],
and molecular interaction fields (MIFs) [
16
]. Next, the quantum approach mainly focuses
on quantum descriptors that produce good results but depend on conformation [
52
–
55
].
Finally, this could still be achieved with a hybrid approach [
56
–
61
] using more than one of
the options above.
3.2. Quantitative Algorithms
QSAR modeling is one of the well-developed LBDD methods. QSAR models have
attempted to predict the property and/or activity of interest of a given molecule.
A QSAR model is formed by a mathematical equation that compares the characteristics
of the investigated molecules with their biological activities. Mathematically, according
to Faulon and Bender [
62
], any QSAR method can usually be defined as the application
of mathematical and statistical methods to the problem of finding empirical equations
in the form Yi = F (X
1
, X
2
,
. . .
, X
n
), where the variable Yi is the approximate biological
activity (or another property of interest) of the molecules and X
1
, X
2
,
. . .
, X
n
are structural
experimental or calculated properties (molecular descriptors such as molar weight, logP,
fragments, number of atoms and/or bonds). F is some mathematical relationship empiri-
cally determined that must be applied to descriptors to calculate property values for all
molecules.

Drugs Drug Candidates 2023,2317
At first, QSAR modeling was only used with simple regression methods and limited
congener compounds. However, QSAR models have grown, diversified, and evolved to
modeling and VS in large databases using various ML techniques [63].
Benedetti and Fanelli [
64
] show several commonly used QSAR models (local,
pharmacophore-based, and global QSAR models). Fan et al. [
65
] used a ligand-based
3D QSAR model based on
in vitro
antagonistic activity data against adenosine receptor
2A (A2A). The resulting models, obtained from 268 chemically diverse compounds, were
used to test 1897 chemically distinct drugs, simulating the real-world challenge of a safety
screening when presented with novel chemistry and a limited training set. Additionally, it
presented an in-depth analysis of the appropriate use and interpretation of pharmacophore-
based 3D QSAR models for safety.
The objective of a QSAR model is to establish a trend of molecular descriptor values
that correlate with the values of biological activity [
17
]. Therefore, it is essential to minimize
the prediction error, for example, by using the sum of squared differences or the sum of
absolute values of the differences between the expected and observed activities in Yi for a
training set. However, before constructing the model, the data need to be pre-processed
and adequately prepared to build and validate the models [62].
3.3. Machine Learning-Based Algorithms
The availability of large databases of molecules storing information such as the struc-
ture and biological activity makes it possible to use predictive models based on ML, which
require a large amount of data from active and inactive compounds to make the prediction.
Therefore, ML techniques have been increasingly used in VS due to their accuracy, expan-
sion of chemical libraries, new molecular descriptors, and similarity search techniques [
45
].
ML is a field of artificial intelligence and computer science devoted to understanding
and building algorithms that learn based on data, analogy, and experience [
37
]. The ML
algorithms use datasets to learn and, based on knowledge acquired in the learning process,
make decisions, make previsions, and recognize patterns. In addition, it is expected that the
overall performance of the ML system will improve over time and the system will adapt to
changes [41].
ML techniques stand out due to their ability to learn from data (examples). Further-
more, the learning methods can be divided into supervised, unsupervised, or reinforcement,
depending on the algorithm [
66
]. Supervised learning requires a training set in which
each sample is composed of the input and its desired output, i.e., the learning algorithm
uses labeled data [
67
]. In supervised learning, the system parameters are adjusted by the
error between the system output and the desired output [
39
]. In unsupervised learning,
the training set is composed only of the inputs. The desired output is not available or
does not exist at all [
67
]. The objective of unsupervised learning is to construct the system
knowledge based on the spatial organization of the inputs. In other words, unsupervised
learning aims to discover patterns using unlabeled data. Reinforcement learning uses a
mechanism based on trial-and-error and reward, in which the correct action awards the
learner and the wrong actions punish them [
68
]. The learner decides on actions based on
the current environmental state and the feedback from previous actions [
69
]. The learner
has no prior knowledge of what action to take, and the aim is to find the actions that
maximize long-term reinforcement. Reinforcement learning is often used in intelligent
agents [41].
The power of VS is increased with ML because it makes it possible, instead of per-
forming computationally costly simulations or exhaustive similarity searches, to track
predicted hits much faster and more accurately [
23
,
70
,
71
]. ML is used to find new drugs,
repositioning compounds, predict interactions between ligands and protein, discover drug
efficacy, ensure safety biomarkers, and optimize the bioactivity of molecules, to mention a
few [
72
]. We can see in Figure 2that the ML algorithms can be divided into artificial neural
networks (ANNs), support vector machines (SVMs), Bayesian techniques, decision trees
(DTs), k-nearest neighbors (k-NN), Kohonen self-organized maps (SOMs), deep learning

Drugs Drug Candidates 2023,2318
(DL), and ensemble methods. The following sub-sections explain the ML algorithms, show
how they can be used, and contribute to the drug discovery process.
3.3.1. Artificial Neural Networks
ANNs are mathematical models inspired by the processing capacity of the human
brain. A neural network is composed of a set of computational units called neurons, which
are a simple mathematical model of biological neurons [
67
]. The neurons are connected
through synaptic weights, which simulate the strengths of synaptic connections of the
biological brain [
73
]. The synaptic weights represent the knowledge in the ANNs, and the
learning is performed by changing these weights. Generally, the ANNs use a supervised
learning algorithm to update the weights based on errors obtained between the network
output and the desired output. The neural network structure can be defined by the number
of layers, the number and type of neurons, and the learning algorithm [
74
]. For example,
Figure 3illustrates the structure of a neural network with two inputs, four neurons in the
first layer (hidden layer), one neuron in the second layer (output layer), and the synaptic
weights [75].
Figure 3.
An example of a simple neural network model. The model was formed by two inputs (i1
and i2), four neurons (h1, h2, h3, and h4) in the first layer, one neuron (o) in the output layer, and the
synaptic weights.
Different efforts introduce ANNs in medicinal chemistry to classify compounds, prin-
cipally in studies with QSAR, to identify potential targets and the localization of structural
and functional characteristics of biopolymers. In such a context, Lobanov [
76
] introduces a
review to argue how ANNs can be used in the VS pipeline from combinatorial libraries,
showing how selecting descriptors is essential to evaluate the ANNs. Furthermore, in
such a review it is emphasized that descriptors can be found in one-dimensional (1D),
two-dimensional (2D), and three-dimensional (3D) forms and can be used to create a model
for ADMET properties profiles.
Tayarani et al. [
77
] propose an ANN model based on molecular descriptors to obtain
the binding energy using the physical and chemical descriptions of the selected drugs. The
authors showed a high correlation between the observed and calculated binding energies
by the ANN since the average error ratio was less than 1%. The experimental results
suggested that ANN is a powerful tool for predicting the binding energy in the drug design

Drugs Drug Candidates 2023,2319
process. Recently, Mandlik, Bejugam, and Singh [
78
] wrote a book chapter to show the
possibilities of using ANNs to aid in developing new drugs.
3.3.2. Support Vector Machine (SVM)-Based Techniques
SVM is a supervised ML algorithm for classification and regression tasks [
41
,
79
]. In a
D-dimensional data space, an SVM creates a hyperplane (h) or a set of hyperplanes to cluster
input data elements according to the similarity expressed in the descriptors’ characteristics.
Such hyperplanes employ linear and nonlinear functions to identify margins that separate
a set of classes (x). In general, the greater the operating margin, the lower the classifier’s
generalization error. Intuitively, a good separation is achieved using the hyperplane with
the most significant distance looked at in the closest training data of any class. For example,
Figure 4shows a classification instance where x1 and x2 denote a pair of classes, with h1,
h2, and h3 as operating margins in which h1 does not separate the classes, h2 separates the
classes, and h3 separates them with the highest operating margin.
Figure 4.
Classification example using SVM. The red and blue squares represent objects of each class
to be classified. h1 does not separate classes. h2 does, but only by a small margin. h3 manages to
separate them with the maximum margin.
SVMs have recently been cited as a promising technique for VS [
79
–
82
]. Rodrígues-
Pérez et al. [
77
] describe how the algorithm can be used in VS and create a comparison with
others in ML presenting its advantages and disadvantages. As an example, it is mentioned
that SVM has evolved as one of the “premier” ML approaches and has been used as a
approach of choice, given its typically high performance in compound classification and
property predictions on the basis of limited training data. This is possible thanks to the
adaptability and versatility of SVM for specialized applications.
Silva et al. [
83
] used Autodock Vina in the docking process, but they proposed an
alternative SVM-based scoring function. They showed that while Autodock Vina offers
a prediction of acceptable accuracy for most targets, classification using SVM was better,
which illustrates the potential of using SVM-based protocols in VS. Finally, Li et al. [
84
]
describe the ID-Score, a new scoring function based on a set of descriptors related to
2278 protein–ligand complexes extracted from Protein Data Bank (PDB). The results indi-
cated that ID-Score performed consistently, implying that it can be applied across a wide
range of biological target types.

Drugs Drug Candidates 2023,2320
3.3.3. Bayesian Techniques
Bayesian techniques are based on Bayes’ theorem and describe the probability of an
event occurring due to two or more causes. This approach combines previous beliefs with
new evidence to compose a hypothesis and describe a model. The main implementation of
this model was algorithms such as the naïve Bayes classifier and Bayesian networks [85].
In repositioning and drug discovery scenarios, the Bayesian algorithms identify the
probability of a compound possessing biological activity for a specific target. Therefore,
Bayesian techniques can be used, for instance, to find novel active scaffolds against the
same target or to find compounds that are more active or that possess improved ADMET
properties when compared to the main structure [
86
]. However, Bayesian networks were
not explicitly superior to much simpler approaches, based, for example, on the Tanimoto
coefficient [87].
3.3.4. Decision Trees
DTs are a supervised ML algorithm that describes functions to take data attributes
as input and return a single output, sometimes called a “decision” by some authors [
69
].
DTs make decisions by performing a sequence of tests, starting in the first tree node called
“root” and proceeding in a top–down strategy until reaching the last level of the structure,
with nodes typically named leaves.
To compose the aforementioned structure as a tree, firstly, a test value that reports
some relationship with each input attribute is associated with each internal node, except
the root. Then, such nodes are labeled with the attribute’s decision possibilities, and leaves
specify the final results that the function should return according to the characterizations
exploited. Figure 5shows how a DT works. In Figure 5diagram, we introduce a toy
example to identify whether the weather is good for playing sports outdoors. The decision
process starts in the root node where weather (i.e., sunny, cloudy, or rainy) is observed.
Then, according to weather conditions, the decision function estimates a novel specific
node (at a lower level), repeating this process until some leaf is achieved, which is used to
extract and return the result.
Figure 5.
Classification example using a decision tree to determine whether the weather is good
enough to go outside. The first level is determined by how the weather is. Depending on the answer, a
new question is selected, or the answer is found. For example, if the weather is sunny, it is questioned
about the humidity depending on whether it is high or normal to determine whether the final answer
is yes or no.

Drugs Drug Candidates 2023,2321
Lavecchia [
76
], argue that DTs are a useful alternative to creating combinatorial li-
braries to predict the similarities between drugs or specific biological activities. However,
to extract high performance using DTs, a single tree structure is not enough. In this context,
some efforts, such as those presented in [
88
], propose applying a set of DTs to increase
the forecast variation. These proposals are called random decision forests (RDFs). This
generalization of DTs is employed by Lavecchia to demonstrate improving the LBVS perfor-
mance, the predictive ability of quantitative models, and unique decision tree algorithms.
Considering our paper’s focus, decision-making forests can be considered as a choice to
determine protein-binding affinity, in addition to being widely used in other coupling
applications. In DT, molecules can be classified from the input data employed to design
their structure during the training stage [70].
Considering VS, is possible to observe DTs employed as a support tool and classifier
functions. An alternative method to construct DT-based classifiers with higher precision
for repositioning and discovery of drugs, improving accuracy as its structure grows in
complexity, has been described by Ho and collaborators. The proposed method uses a
pseudorandom procedure to choose a member of a resource vector. According to the choice
made, DTs are generated using only the components that have the selected characteris-
tics [
89
]. DTs have been applied to investigate interactions among biological activities,
chemical substructures, and phenotypic effects [
88
]. This study assumes chemical struc-
ture fingerprints provided by the PubChem system, divided across four assays according
to biological activity data in a 10-fold cross-validation (CV) sensitivity, specificity, and
Matthews correlation coefficient (MCC) with results of 57.2~80.5%, 97.3~99.0%, and 0.4~0.5,
respectively.
3.3.5. K-Nearest Neighbors
KNN is the most popular unsupervised learning method used for classification and
regression, whose strategy boils down to looking for, from some prefixed data input
element, the k-closest neighbors. In scenarios where input data elements are fashioned by
the same descriptors and drawn in the same n-Dimensional space, some metrics can be
chosen to compare them, including those based on distances such as Euclidean, cosine,
and Manhattan [
90
]. In a correlation analysis context, Pearson [
91
] and Kendall [
92
] may
be sufficient. For input data elements represented by a particular structure such as trees,
Resnik [93], Jiang [94], and Lin [95] should be considered.
Once the adequate metric is found, the param K in the KNN algorithm should be
defined before execution, which matches the number of neighborhood points extracted
as results. For example, if k = 1, only the training element closest to the instance to be
classified is selected. If k = 5, the five elements closest to the instance are selected, and
based on the classes of the five elements, the class of the test element is inferred. The idea
of this algorithm is that the class to which a compound belongs can be defined by the class
to which the closest neighbors belong (more similar compounds, for example), considering
weighted similarities between the compound and its closest neighbors [96].
In VS, Shen et al. [
97
] created a new drug design strategy that uses KNN to validate
developed QSAR models. They identified and synthesized nine potential anticonvulsant
drugs by evaluating a library of more than 250,000 molecules. Of these, seven were
confirmed as bioactive (a success rate of 77%). Peterson et al. [
98
] used KNN in QSAR
and VS studies and found 47 types I geranyl transferase inhibitors in a database with
9,500,000 chemicals. Seven were successfully tested
in vitro
and presented activity at the
micromolar level. These new hits could not be identified using traditional similarity search
methods that demonstrate that KNN models are promising and can be used as an efficient
VS method.
3.3.6. Kohonen’s SOMs
Kohonen’s self-organizing maps (SOMs) [
99
] are unsupervised algorithms that use
competitive networks formed by a two-layered structure of neurons. The first layer is called

Drugs Drug Candidates 2023,2322
the input layer, with its neurons completely interconnected to the neurons of the second
layer, which is organized in a dependent arrangement of the object to be mapped. Kohonen
maps are therefore formed by connected nodes with an associated vector corresponding
to the map’s input data (e.g., molecular descriptors). Kohonen algorithms can analyze
grouped data to discover multidimensional structures and patterns. The Kohonen networks
can also be considered an unsupervised neural network since having a known target vector
is not necessary. It is considered a self-organized algorithm since it can decrease the size of
a data group while maintaining the real representation concerning the relevant properties
of the input vectors, resulting in a set of characteristics of the input space.
The Kohonen self-organizing maps are defined by Ferreira et al. [
100
] as competitive
neural networks with a high degree of interconnection between their neurons that can form
mappings, preserving the topology between the input and output spaces. For example, in
VS, Kohonen’s SOMs could be employed extensively in drug repositioning and scaffold
hopping [
101
]. Noeske et al. [
102
] used Kohonen’s SOMs to discover new targets for
metabotropic glutamate receptor antagonists (mGluR). Experiments have revealed distinct
subclusters of mGluR antagonists, and localization overlaps with ligands known to bind
to histamine (H1R), dopamine (D2R), and various other targets. These interactions were
later confirmed experimentally and exhibited significant binding affinities between the
predicted mGluR antagonists for the targets. This led to a mGluR1 antagonist and selective
subtype (Ki = 24 nM) based on a coumarin scaffold. This compound was later developed
into a series of leads.
In another study, Noeske et al. [
103
] used Kohonen’s SOMs to map the drug-like
chemical space using pharmacophore descriptors. The experiments demonstrated that
other G protein-coupled receptors (GPCRs) could interact with mGluR ligands (mGluR1:
dopamine D2 and D3 receptors, histamine H1 receptor, mACh receptor; mGluR5: histamine
H1 receptor). Lastly, the results were experimentally confirmed, and their IC50 ranged from
5 to 100
µ
M. Hristozov et al. [
104
] use Kohonen’s SOMs in LBVS to rule out compounds
with a low probability of having biological activity. The proposed idea can be used,
according to the authors, (1) to improve the recovery of potentially active compounds;
(2) to discard compounds that are unlikely to have a specific biological activity; and
(3) to
select potentially active compounds from a large dataset. In a recent paper, Palos
et al. [
105
] applied Kohonen’s SOMs in ligand clustering to perform drug repositioning
in FDA-approved drugs. This research suggests that four FDA drugs could be used for
Trypanosoma cruzi infections.
3.3.7. Ensemble Methods
Ensemble methods combine multiple models rather than employing a single model to
increase model accuracy. The results are significantly more accurate when the combined
models are used. This has increased the popularity of ensemble methods in ML [106].
Sequential and parallel ensemble techniques are the two main types of ensemble
methods. Adaptive boosting (AdaBoost) is an example of a sequential ensemble technique
that generates base learners in a sequence. The sequential generation of base learners
facilitates the base learners’ dependence on one another. The model’s performance is then
improved by giving previously misrepresented learners more weight.
Base learners are generated in parallel ensemble techniques, such as a random forest.
To foster independence among base learners, parallel methods use the parallel generation
of base learners. The application of averages results in a significant reduction in error due
to the independence of base learners.
Most ensemble methods employ a single algorithm in base learning, resulting in
uniformity among all base learners. Homogeneous base learners are base learners of the
same type and share many of the same characteristics. Heterogeneous ensembles are
created when heterogeneous base learners are used in other approaches. Different types of
learners make up heterogeneous base learners.

Drugs Drug Candidates 2023,2323
In a recently published paper, Helguera [
107
] reported that using ensemble techniques,
such as bagging [
108
] and boosting [
109
], provides better predictions than any of the above
techniques in isolation in ML. Therefore, Helguera proposed an ensemble method based
on genetic algorithms (GAs). To investigate potential Parkinson’s disease therapeutics,
Helguera applied his proposed method for the LBVS of dual-target A2A adenosine re-
ceptor antagonists and MAO-B inhibitors. Finally, he showed that the ensemble method
outperformed individual models.
A combination of VS and MD with Ensemble methods to evaluate the
∆
T
m
is described
in [110], which used Ensemble methods to estimate the ∆Tmexperimental values of DNA
intercalating agents. These authors also evaluated three docking methodologies, and the
best five molecules were submitted to MD simulations.
3.3.8. Deep Learning
DL is a large class of ML techniques based on neural networks in which inputs are
successively transformed into alternative representations that better allow for pattern
extraction [
39
,
41
–
44
,
67
,
69
,
73
,
111
]. The word “deep” refers to the fact that circuits are
typically organized in many layers, meaning that the computation paths from inputs to
outputs have many steps. DL is currently the most widely used approach for applications
such as visual object recognition, machine translation, speech recognition, speech synthesis,
and image synthesis; it also plays a significant role in reinforcement learning applications.
Figure 6shows the main difference between ANNs and the DL approach, namely the
number of hidden layers. In ANNs, one or two hidden layers are usually used, while in DL,
more than five layers are used, leading to more meaningful processing and better results.
Figure 6.
Differences between ANN and DL architectures. In a simple neural network, one or two
hidden layers are used, while in DL, more than two layers are always used. With this, the required
processing is more significant; however, in most cases, the results are better.
Several studies with DL have been applied in VS. Multiple reviews [
63
,
112
–
115
] have
presented a detailed explanation of DL and how it can be used in VS and described methods
and techniques along with problems and challenges in the area. Bahi et al. [
116
] present a
compound classification method based on a deep neural network for VS (DNN-VS) using
the Spark-H2O platform to label small molecules from large databases. Experimental
results have shown that the proposed approach outperforms state-of-the-art ML techniques
with an overall accuracy of more than 99%.
Joshi et al. [
117
] used DL to create a predictive model for VS, and the molecular
dynamics (MD) of natural compounds against the severe acute respiratory syndrome
coronavirus 2 (SARS-CoV-2) were deployed on the Selleck database containing 1611 natural
compounds for VS. From 1611 compounds, only four compounds were found with drug-
like properties, and three were non-toxic. The results of MD simulations showed that

Drugs Drug Candidates 2023,2324
two compounds, Palmatine and Sauchinone, formed a stable complex with the molecular
target. This study suggests that the identified natural compounds may be considered for
therapeutic development against SARS-CoV-2.
More recently, Schneider et al. [
118
] presented DL for VS in antibodies to predict
antibody–antigen binding for antigens with no known antibody binders. As a result, the
DL approach for antibody screening (DLAB) can improve antibody–antigen docking and
structure-based VS of antibody drug candidates. Additionally, DLAB enables improved
pose ranking for antibody docking experiments and the selection of antibody–antigen
pairings for which accurate poses are generated and correctly ranked.
3.4. Meta-Heuristic Algorithms
Meta-heuristic approaches are widely applied in searching for and finding approxi-
mate optimum solutions. Meta-heuristic can be classified as single-solution or population-
based [
119
]. Single-solution meta-heuristics aim to change and improve the performance
of a single solution. Tabu search (Section 3.4.1.) and simulated annealing (Section 3.4.2.)
are examples of single-solution meta-heuristics used in drug discovery. On the other hand,
population-based methods maintain and improve multiple solutions, each corresponding to
a unique point in the search space [
120
]. Population-based meta-heuristics applied in drug
discovery include genetic algorithms (Section 3.4.3.), differential evolution (Section 3.4.4.),
conformational space annealing (Section 3.4.5.), ant colony optimization (Section 3.4.6.),
and particle swarm optimization (Section 3.4.7.).
3.4.1. Tabu Search
The TS algorithm performs the search using a memory structure that accepts the
investigation of movements that do not improve the current solution [
121
]. In TS, the
memory is used to create a Tabu list consisting of solutions that will not be revisited,
preventing cycles, making it possible to explore unvisited regions, and improving the
decision-making process through past experiences.
PRO_LEADS [
122
] is one of the most popular algorithms using TS in VS. It proposes
using a TS algorithm to explore the large search space and uses an empirical scoring
function that estimates the binding affinities to order the possible solutions. PRO_LEADS
was tested on 50 protein–ligand complexes and accurately predicted the binding mode of
86% of the complexes.
3.4.2. Simulated Annealing (SA)
Simulated annealing, a general probabilistic meta-heuristic, is a method that can
approximate the global optimum of a given function given ample search space [
123
]. In
summary, SA is an approach for resolving bound-constrained and unconstrained optimiza-
tion issues. This method simulates the actual physical procedure of heating a material and
gradually lowering the temperature to reduce defects and reduce system energy.
A new point is generated randomly during each iteration of the simulated annealing
algorithm. A probability distribution with a temperature-dependent scale is the foundation
for the new point’s distance from the current point or the scope of the search. All new
points that lower the objective are accepted by the algorithm, as are points that raise the
objective with a certain probability. The algorithm can look globally for more potential
solutions rather than becoming stuck in local minima by accepting points that raise the goal.
An annealing schedule is chosen to lower the temperature as the algorithm progresses. The
algorithm reduces the scope of its search to a minimum to converge as the temperature
drops.
Within the MD context, simulated annealing molecular dynamics (SAMD) involves
heating a particular crystallographic (ligand–receptor) complex to high temperature, fol-
lowed by gradual slow cooling to unveil conformers of reasonable energy minima [
124
].
The process is usually repeated over multiple cycles (10–100) [
124
,
125
]. One use of SAMD is
calculating the optimal docked pose/conformation for a particular ligand within a specific

Drugs Drug Candidates 2023,2325
binding pocket, as in the CDOCKER docking engine. Hatmal and Taha [
126
] use a method-
ology implemented to develop pharmacophores for acetylcholinesterase and protein kinase
C-
θ
. The resulting models were validated by receiver-operating characteristic analysis and
in vitro
bioassay. Thus, in this research, four new protein kinase C-
θ
inhibitors among
captured hits, two of which exhibited nanomolar potencies, were identified.
3.4.3. Genetic Algorithms
GAs are a technique for tackling both obliged and unconstrained improvement issues
that depend on regular choice, the interaction that drives natural development [
127
]. They
are implemented as a computer simulation where a population undergoes mutations to
find better solutions. Each new individual represents a possible solution to the problem.
For each new generation, the most suitable individuals are evaluated. This process selects
some individuals for the next generation, and they are recombined and mutated to generate
new individuals. Although random, they are targeted to a better solution by exploring
available information to find new search points where better performance is expected. This
process is repeated until it is finalized. The problem with GAs is that they typically have a
poor processing time. Therefore, they are mostly used in challenging problems.
GAs are widely used in VS. For example, Bhaskar et al. [
128
] used a genetic algorithm
in their docking process to uncover 14 lead candidates that selectively inhibit the NorA
efflux pump competing with its substrates. In addition, Rohilla, Khare, and Tyagi [
129
]
evaluated, using a genetic algorithm, the ability of the IdeR transcription factor (essential in
regulating the intracellular levels of iron) to bind to the DNA after being exposed to some
compounds. The most potent inhibitors showed IC50 values of 9.4 mM and 6.9 mM. After
analyzing the inhibition results, it was possible to find an important scaffold in inhibiting
the protein (benzo-thiazol benzene sulfonic).
Xia et al. [
130
] applied a genetic algorithm to investigate a potential therapy to treat
neurodegenerative disorders (e.g., Parkinson’s disease) and stroke. They performed a
VS experiment based on the X-ray structure of the Pgk1/terazosin complex and selected
13 potential
anti-apoptotic agents selected from the Specs chemical library.
In vitro
ex-
periments performed by the authors demonstrated a high chance of three of the selected
compounds binding to hPgk1-like terazosin. These results indicate that they can act by
activating hPgk1 and as apoptosis inhibitors.
3.4.4. Differential Evolution
DE is a heuristic strategy for globally optimizing nonlinear and non-differentiable
continuous space functions. A broader group of evolutionary computing algorithms in-
cludes the DE algorithm. The DE algorithm begins with an initial population of candidate
solutions, just like other popular direct search strategies such as GAs and evolution strate-
gies. Then, by introducing mutations into the population, these candidate solutions are
iteratively improved, keeping the fittest candidate solutions with lower objective function
values. The basic idea of this operation is to add a randomly selected individual to the
difference between two other randomly selected individuals [131].
MolDock [
132
] combines DE with a cavity prediction algorithm. In a VS test of
ligands against target proteins, MolDock identified the correct binding mode of 87% of the
complexes. This result was better than Glide [
133
], Gold [
134
], Surflex [
135
], and Flexx [
136
]
in similar databases.
3.4.5. Conformational Space Annealing (CSA)
CSA is a stochastic global search optimization algorithm that uses randomness in
the search process. Due to this, this method works well for nonlinear objective functions
where other local search algorithms do not. It utilizes global optimization to find the
optimal solution for a given objective function satisfying for any. CSA can be considered
a type of GA because it performs genetic operations while maintaining a conformational

Drugs Drug Candidates 2023,2326
population [
137
]. Here, the terminologies of “conformation” and “solution” are used
interchangeably. CSA contains two essential ingredients of Monte Carlo and SA.
As in Monte Carlo, CSA only works with locally minimized conformations. However,
in SA, annealing is performed in terms of temperature, while in CSA, annealing is per-
formed in an abstract conformational space while the diversity of the population is actively
controlled.
The CSA performance is described in Shin [
138
], which evaluates two existing docking
programs, GOLD [139] and AutoDock [140], on the Astex diverse set.
3.4.6. Ant Colony Optimization
ACO is an algorithm inspired by the behavior of ants in the search of food. One of the
central ideas of ACO, proposed by Dorigo et al. [
141
], is the indirect communication based
on trails (or paths) of pheromones between a colony of agents called ants. In nature, ants
search for food by walking on random tracks until they find it. Then, they return to the
colony and leave behind a trail of pheromones. When other ants find this trail, they stop
following random tracks and follow the trail to obtain food and return to the colony. Over
time, the pheromone evaporates, reducing the ants’ attraction to a certain path. The more
ants go through a path, the more time it takes for the pheromone to evaporate. Pheromone
evaporation is important because it prevents an optimal local solution from becoming
excessively attractive over time.
The Protein–Ligand ANT System (PLANTS) [
142
] is used in protein–ligand docking.
In this algorithm, an artificial colony of ants is used to find the conformation of the least
energized ligand at the binding site. In this artificial colony, the behavior of real ants is
imitated by marking the low-energy conformations with pheromone trails. This infor-
mation of the pheromone track is then modified in the subsequent iterations, aiming to
increase the probability of generating conformations with lower energy. According to
Reddy et al. [
143
], this algorithm behaves well when tested with GOLD in experimentally
developed structures.
3.4.7. Particle Swarm Optimization
Particle swarm optimization (PSO) optimizes a problem iteratively by improving a
candidate solution respecting a certain quality measure. It has many similarities to GAs.
In PSO, the system is initialized with an individual (population) representing random
solutions, and it searches for the best solution by updating the generations. In the PSO,
the possible solutions are called particles and fly through the problem space following the
current optimal particles. Comparing it with GA, PSO is easier to implement because it
has fewer parameters to calibrate. Although classified as an evolutionary algorithm, PSO
does not have the survival characteristic of the fittest or the use of genetic operators, such
as crossing and mutation [144].
Liu, Li, and Ma [
145
] compared a PSO algorithm against four state-of-the-art docking
tools (GOLD, DOCK, FlexX, and AutoDock with Lamarckian GA) and it performed better
in the evaluated database. Gowthaman, Lyskov, Karanicolas [
146
], and PSOVina [
147
] are
other examples of particle swarming applied in VS.
3.5. Other Techniques-Based Algorithms
In the literature, algorithms are also found based on other techniques applied in the
drug development process. Among these, we can highlight Local Search (Section 3.5.1), Ex-
haustive search (Section 3.5.2), Simplex (Section 3.5.3), Incremental Construction
(Section 3.5.4), and Monte Carlo (Section 3.5.5).
3.5.1. Local Search
Local search is a method to solve computationally complex optimization problems.
Local search algorithms test all solutions in the candidate solution space, applying only
local changes until a solution considered optimal is found or a specific time limit has

Drugs Drug Candidates 2023,2327
elapsed. These algorithms look for reasonable solutions in large or infinite-state spaces.
Autodock Vina [
148
], SwissDock/EADock [
149
], and GlamDock [
150
] are examples of tools
that use the local search for VS.
3.5.2. Exhaustive Search
For problems with no efficient solution method, it could be interesting to test each
possibility sequentially to determine the best solution. This exhaustive examination of all
possibilities is known as exhaustive search, direct research, or the “brute force” method. An
exhaustive search is typically used when the problem’s size is limited, or problem-specific
heuristics can be used to reduce the number of possible solutions to a manageable level.
This method is also used when implementation simplicity is more important than speed.
In VS, eHiTS [
151
] software offers the first genuinely exhaustive systematic search
algorithm that considers all poses without a severe steric clash of all ligands.
3.5.3. Simplex Algorithm
The Simplex algorithm is an iterative procedure for solving linear problems in a
finite number of steps [
152
]. It consists of (i) knowing an initial basic feasible solution,
(ii) testing
whether the solution is optimal, (iii) improving the solution from a set of rules,
and repeating the process until an optimal solution is obtained. For example, the Simplex
method was used with GA to create rDock [153].
3.5.4. Incremental Construction
This is an algorithm for incremental construction in which the ligand is gradually
incorporated into the binding site [
58
–
60
]. The chemical structure is initially broken up
into several pieces with this strategy. Next, one of these pieces is chosen to serve as an
anchor fragment, and it is docked in a complementary area of the binding site while the
other pieces are added one at a time. The procedure goes on until the entire ligand is made.
Finally, the calculation executes the conformational search just for the additional pieces,
lessening the levels of opportunity to be investigated, and consequently staying away from
the combinatorial blast.
3.5.5. Monte Carlo
A Monte Carlo method uses a statistical methodology based on a large set of random
samples to obtain results that approximate reality [
154
]. Thus, Monte Carlo methods
perform a sufficiently high number of successive simulations to allow for probabilities to
be calculated heuristically.
When used as docking methods, Monte Carlo methods randomly generate an initial
conformation of the ligand and calculate its binding energy. Based on this initial confor-
mation, a new configuration is generated. Let us suppose that the binding energy for the
new configuration is lesser (i.e., more negative) than for the initial conformation. In that
case, it is automatically accepted as the reference for the next iteration. Otherwise, another
evaluation is performed to verify whether it should be used as the reference. This process
is repeated until the desired number of iterations is reached.
4. Guidelines for In Silico Models for CADD
Although some in silico models have been created and used for years to evaluate
chemicals in some countries, without a transparent validation process and an objective
determination of the reliability of the models it is essential to increase their regulatory
acceptance.
In 2004, the Organization for Economic Co-operation and Development (OECD)
showed an initiative for (Q)SAR models [
155
] and created principles for the validation of
such models based on the following concepts: (i) (Q)SRAR models should be fashioned
toward a well-defined aim, (ii) aid algorithms should be focused only on the aims of (Q)SAR
models, (iii) a well-defined application domain should be presented, and (iv) appropriate

Drugs Drug Candidates 2023,2328
measures of goodness of fit, robustness and predictivity, and a mechanistic interpretation,
should be applied if it is possible. Later in 2004 was published a full report describing all
these points [156].
In 2007, the “Guidance Document on the Validation of (Q)SAR Models” was published
by the OECD to provide advice on how to evaluate specific (Q)SAR models in light of
the OECD’s principles [
157
]. Attached as annexes are a validation checklist, a validation
reporting format, and validation case studies.
In November 2011, the Government of Canada published the third edition of the PSPC
National CADD Standard. A concerted effort was made to simplify the standard, and
PSPC is aware of the emerging technology and processes related to building information
modeling (BIM). However, as BIM represents a significant change, a new BIM standard, by
necessity, will be created, facilitating the transition in the architecture, engineering, and
construction (AEC) industries. In addition, some regions have developed a regional CADD
standard, which is to be used as a complement to this national standard. Additionally, the
Government of the USA and the European Chemicals Agency have already published their
standards. Therefore, the recommendation is to create or follow a protocol with the best
recommendations of some standards and updates them as needed.
Recently, Czub et al. [
158
] published a paper that evaluated whether an ML-based
model could meet the OECD principles’ regulation for the 5-HT1A receptor box. The model
was developed based on a database with close to 9500 molecules by using an automatic ML
tool (AutoML). First, the model selection was based on the Akaike information criterion
value and 10-fold cross-validation routine. Later, good predictive ability was confirmed
with an additional external validation dataset with over 700 molecules. Moreover, the multi-
start technique was applied to test whether an automatic model development procedure
results in reliable results. The information provided indicates that our final model leads to
affinity predictions within the error range indicated by the FDA.
5. Final Considerations
Several in silico methods and techniques have been used to improve drug development.
CADD allowed for the development of new compounds with a decrease in time and cost.
As a result, VS has emerged as one of the most promising in silico drug design methods.
This review focused on the most used algorithms in the CADD process and described
how they can be used to contribute to the various stages of drug development, focusing
primarily on the use of VS.
VS techniques can be divided according to algorithms based on similarity, quantitative,
ML, meta-heuristics, and others. Each category was explained, and use cases were demon-
strated. Among these techniques, the one that has been highlighted most recently is ML.
ML methods have successfully been used to screen employees and aided in drug discovery.
In addition, DL has been producing even more accurate models in the last
5 years
. As a
result, several studies and innovations benefit from the application of DL in CADD.
However, CADD tools have required a wide variety of knowledge from researchers,
which is necessary for their integration in computation, chemistry, and biology to select and
prepare the targets and ligands, generate the models, and analyze the results. As a result,
the formation of multidisciplinary teams and researcher training is increasingly important
for the selection of new hits and for optimizing HTS experiments.
Author Contributions:
Conceptualization, A.G.T. and A.M.d.S.; methodology, A.G.T.; validation,
A.G.T. and A.M.d.S.; formal analysis, T.A.d.O., M.P.d.S. and E.H.B.M.; investigation, T.A.d.O., M.P.d.S.
and E.H.B.M.; resources, A.G.T. and A.M.d.S.; data curation, A.G.T. and A.M.d.S.; writing—original
draft preparation, T.A.d.O.; writing—review and editing, A.G.T., A.M.d.S., E.H.B.M. and M.P.d.S.;
visualization, T.A.d.O.; supervision, A.G.T.; project administration, A.G.T.; funding acquisition, A.G.T.
and A.M.d.S. All authors have read and agreed to the published version of the manuscript.

Drugs Drug Candidates 2023,2329
Funding:
This research was funded by PPBE and PPGCF/UFSJ; Fundação de Amparo àPesquisa
do Estado de Minas Gerais—FAPEMIG, grants APQ-02741-17, APQ-00855-19, APQ-01733-21, and
APQ-04559-22; and Conselho Nacional de Desenvolvimento Científico e Tecnológico—CNPq-Brazil,
grants 305117/2017-3, 426261/2018-6; and by the fellowship of 2021 (grant 310108/2020-9).
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: Not applicable.
Acknowledgments:
The authors would like to thank the Federal University of São João del-Rei
(UFSJ), Programa de Pós-graduação em Bioengenharia (PPBE/UFSJ), and the Federal Center for
Technological Education of Minas Gerais (CEFET-MG) for providing the physical infrastructure and
human resources.
Conflicts of Interest: The authors declare no conflict of interest.
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