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Chapter 7
Molecular Docking: State-of-the-Art
Scoring Functions and Search Algorithms
Rafaela M. de Angelo, Daniel S. de Sousa, Aldineia P. da Silva,
Laise P. A. Chiari, Albérico B. F. da Silva, and Kathia M. Honorio
Abstract This study addresses the technique of molecular docking in drug design,
emphasizing the importance of advanced scoring functions and search algorithms. It
explores the generation of detailed models involving protein targets and ligand
molecules and the evaluation of multiple ligand conformations to identify favorable
molecular interactions. Additionally, it underscores the prediction of the ligand’s
most stable and energetically favorable conformation to interact with the target
protein. Molecular docking plays a fundamental role in the early stages of drug
discovery and development, assisting in selecting promising drug candidates and
estimating the affinity between candidate molecules and target proteins. The key
contributions of this chapter include the exploration of the importance of molecular
docking in drug design and the analysis of advanced scoring functions and search
algorithms. These topics are essential components for successful molecular docking.
In addition, this chapter will discuss the relevance of result validation and the need
for accuracy in the predictions based on docking simulations. Furthermore, it
evaluates the challenges faced in molecular docking and reflects on using machine
learning techniques in this context. It provides insights into the interaction between
molecules and target proteins, which is essential for understanding the mechanisms
of action of bioactive substances. It also emphasizes the acceleration in drug
discovery and design, resource management, and the enhancement of understanding
of molecular interactions as significant outcomes. These contributions underscore
R. M. de Angelo
University Federal of ABC (UFABC), Center for Natural and Human Sciences (CCNH), Santo
André, SP, Brazil
D. S. de Sousa · L. P. A. Chiari · A. B. F. da Silva
University of São Paulo (USP), São Carlos Institute of Chemistry (IQSC), São Carlos, SP,
Brazil
A. P. da Silva · K. M. Honorio (
University of Sao Paulo (USP), School of Arts, Sciences and Humanities (EACH), Sao Paulo,
Sao Paulo, Brazil
e-mail: KMHONORIO@USP.BR
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024
V. G. Maltarollo (ed.), Computer-Aided and Machine Learning-Driven Drug
Design, Computer-Aided Drug Discovery and Design 3,
https://doi.org/10.1007/978-3-031-76718-0_7
✉)
163
164 R. M. de Angelo et al.
the importance of molecular docking as a fundamental tool in pharmaceutical
research and drug development.
Keywords Molecular docking · Scoring functions · Search algorithms · Drug
design
1 Molecular Docking
Molecular docking is a sophisticated computational technique extensively utilized
across various scientific domains, including chemistry, pharmacy, and molecular
biology. Its primary objective is to simulate the interaction between a molecule and
the three-dimensional structure of a specific target. The docking procedure goal is to
generate de tailed models of the target macromolecule (e.g., protein) together with
the small (ligand) molecule. It often starts from multiple ligand conformations are
assessed to pinpoint the position and orientation that yield favorable molecular
interactions, which are quantified using a scoring function (SF). These interactions
encompass hydrogen bonds, van der Waals forces at the protein’s active site, and
other relevant interactions crucial for the binding. Regardless of the scoring function
employed, these interactions play a significant role in determining the docking
outcomes [1].
In a nutshell, the primary objective of the docking is to predict a favorable targetligand conformation, maximizing the number of interactions and minimizing the
predicted binding energy (derived from the SF). This information is crucial in the
search for drug candidates, as it helps select promising bioactive substances, optimize structures of existing molecules, and estimate the affinity between the candidate molecule and the target protein. In summary, the docking technique is crucial in
the early stages of drug discovery and development [2].
Kuntz and colleagues pioneeringly introduced the docking methodology in 1982,
with the article titled “A Geometric Approach to Macromolecule-Ligand Interactions.” [3] In this work, the authors proposed that molecular recognition between
molecules, based on chemical and geometric aspects, could be explored through
three-dimensional models of both the ligand and the target. Since then, molecular
docking has been widely applied as a rapid approach to estimate how a particular
compound binds to a biological target and to predict/estimate the affinity of this
interaction.
In this chapter, we will discuss the relevance of docking in drug design and
discovery, advances in scoring functions and search algorithms (including machine
learning, ML), calculations involved in docking simulations, essential components
of effective docking programs, limitations of the technique, and validation of
docking results (including the consensus technique). We end the chapte r discussing
perspectives from different experts, the ML integration in molecular docking, and
giving a perspective on the most recent challenges encountered in the field.
7 Molecular Docking: State-of-the-Art Scoring Functions and Search Algorithms 165
1.1 Importance/Relevance of Docking in the Drug Design
and Discovery
Traditionally, the search for new drugs starting from the initial hits involves the
synthesis and testing of many compounds; this process is error prone and resource
intensive. Docking can help to deprioritize compounds with a low likelihood of
success, saving valuable time and resources. Docking results can also be used to
optimize the structure of existing hit molecules, during the hit-to-lead phase,
improving their effectiveness and selectivity to the target protein [2].
Molecular docking provides detailed insights into the protein–ligand interactions
at the atomic level. The early identification of hits as drug candidates with a high
probability of success can save significant resources in the long term by avoiding
investments in compounds that will not work or h ave severe side effects. It is worth
mentioning that the docking technique is also relevant in the era of personalized
medicine, where drugs are tailored to individual patient characteristics, helping
design medications that precisely fit variant or mutant proteins [4].
When one employs molecular docking and faces some of its complexities, it
becomes crucial to recognize the pivotal role played by the choice of scoring
functions and search algorithms. These elements constitute the essential foundations
of docking tools, guiding the investigation of molecular interactions and determining
optimal binding conformations. Before delving into recent advancements in scoring
functions and search algorithms, it is relevant to briefly contextualize these components’ importance within the molecular docking realm.
2 Advances in Scoring Functions and Search Algorithms
Molecular docking is an ever-evolving field and state-of-the-art scoring function and
search algorithms continue to advance rapidly. It is crucial to highlight significant
trends and recent advancements within these domains, delineated through the
presentation of three principal categories of scoring functions: force-field-based,
empirical, and knowledge-based [5]. Figure 7.1 summarizes the main classes of
scoring functions used to estimate protein–ligand interactions, categorizing them
into three principal topics: types of functions, datasets (concerning already obtained
data), and applications for different functions.
In recent years, several studies related to docking simulations have focused on the
development and refinement of scoring functions [6], such as:
• Machine learning-based scoring functions: Machine learning techniques to
develop more accurate scoring functions have gained prominence in recent
years. Deep learning algorithms are being applied to model complex protein–
ligand interactions [7]. Figure 7.2 illustrates some machine learning-based algo-
rithms that can be applied to scoring functions and optimizing protein–ligand
166 R. M. de Angelo et al.
Fig. 7.1 Diagram of categories, datasets, and applications of scoring functions employed during
the protein–ligand docking. (1) Datasets can be used to obtain scoring function (SF), which can
estimate binding affinities, for example. (2) Regarding function types, SF can be classified into three
categories: knowledge-based, force field-based, and empirical; below we find the databases that
derive the SF. (3) SF can be applied to estimate interactions between a large number of compounds
and proteins, optimizing the search for molecules of interest
7 Molecular Docking: State-of-the-Art Scoring Functions and Search Algorithms 167
Fig. 7.2 Machine learning-based algorithms used in scoring functions, which are present in
docking programs. Each acronym represents a unique approach in the field of machine learning,
tailored to different types of data and specific tasks. RF (Random Forest); XGB (XGBoost); GBT
(Gradient Boosting Trees); NN (Neural Network); CNN (Convolutional Neural Network); GNN
(Graph Neural Network)
interaction calculations. The limitations of generic ML scoring functions include
the possibility of not capturing specific nuances of protein–ligand interaction,
resulting in less precise predictions. Additionally, the reliance on broad datasets
can make models less sensitive to the unique features of specific interactions. On
the other hand, the advantages of generic ML scoring functions lie in their ability
to generalize and computational efficiency. These functions can be trained on
large datasets to capture general protein–ligand interaction patterns, making them
applicable to various biological systems. Furthermore, using ML algorithms may
enable the discovery of complex relationships between molecule features and
their interactions, which traditional approaches may not readily identify.
• Multiscale scoring functions: Scoring functions that consider multiple scales of
interactions, such as local and global interactions, are being developed to improve
the accuracy of binding predictions. An example of global interactions cited in
this work is related to the long-range electrostatic interactions between charged
residues on the protein surface and polar or charged groups at the ligand mole-
cule. These can play a significant role in determining the overall stability of the
interactions and the binding affinity of the protein–ligand complex [8].
• Integration of structural and energetic data: The integration of structural infor-
mation, such as structural biology data, with energetic data in scoring functions is
becoming more common, enhancing the accuracy of docking simulations. While
168 R. M. de Angelo et al.
this integration improves docking accuracy, it is noteworthy that many original
scoring functions like ALP/ChemScore were actually based on energy results
from pharmaceutical companies [9–11].
• Scoring functions and solvation effect: Scoring functions that take into account
the influence of solvent on protein–ligand interactions are being developed and
refined to improve accuracy in solvated systems [12, 13].
Taking into account the search algorithms, it is important to mention that some of
the original algorithms focused on just sampling the ligand conformation, while the
more advanced ones expand on the protein–ligand sampling as a single process. So,
the search algorithms integrated into molecular docking platforms serve as robust
computational tools for exploring the conformational space of ligands for protein
active sites. Each algorithm embodies distinct methodologies tailored to optimize
ligand-binding interactions. Below, we can cite some algorithms:
Random Forest (RF): An ensemble learning technique predicated on decision trees,
RF excels in classification and regression tasks. Its adaptability renders it condu-
cive to identifying promising ligand poses within docking studies [14].
XG-Boost (XGB): An optimized implementation of gradient boosting, XGB is
proficient in handling classification and regression challenges. While powerful,
vigilant parameter regularization is essential to mitigate overfitting risks [15].
Gradient Boosted Trees (GBT): Analogous to XGB but with a more generalized
boosting methodology, GBT offers efficacy in classification and regression
endeavors. Prudent parameter tuning is requisite to circumvent overfitting
concerns [16].
Neural Networks (NN): NNs, comprising interconnected layers of neurons, exhibit
prowess in capturing intricate data representations. In docking, NNs are instru-
mental in discerning the intricate relationship between ligand structure and
protein binding affinity [17 ].
Convolutional Neural Networks (CNN): Specialized in grid-structured data
processing, such as images, CNNs are adept at extracting local patterns pertinent
to ligand–protein interactions. Their application in molecular docking facilitates
comprehensive structural analysis [18].
Graph Neural Networks (GNN): Tailored for data represented as graphs, GNNs
excel in modeling complex atomistic interactions within molecular systems.
Leveraging their capabilities, GNNs offer versatile representations of molecular
structures, facilitating spatial relationship elucidation [19].
These algorithms, either employed individually or synergistically, augment the
precision and efficiency of molecular docking endeavors. The selection of an
appropriate algorithm hinges upon the specific attributes of the docking problem
and the nature of the data under investigation [20].
About the search algorithm s implemented in docking programs, the main characteristics of these algorithms include:
7 Molecular Docking: State-of-the-Art Scoring Functions and Search Algorithms 169
• Enhancement of global search algorithms: Global search algorithms, such as
genetic algorithms and particle swarm optimization (heuristic), are being
improved to explore ligand conformation spaces more effect ively [21]. It is
worth noting that the diversity of available search functions has increased,
underscoring the importance of selecting the appropriate algorithm for molecular
docking, as it is not always a one-size-fits-all problem [22].
• Hardware acceleration: The use of high-performance hardware, such as GPUs
(Graphics Processing Unit) and TPUs (Tensor Processing Unit), accelerates the
search for binding conformations, making the docking process faster and more
efficient [23].
• Intelligent local search methods: Local search algorithms are being enhanced
with intelligent strategies to increase the probability of finding high-affinity
binding conformations [24–26].
• Integration of molecular dynamics: Integrating molecular dynamics with docking
is becoming more common, allowing for a more realistic consideration of molec-
ular flexibility during the search [26, 27].
• Flexible conformation simulations: Algorithm s that enable simulations of flexible
conformation are being developed, allowing ligands and proteins to change their
conformations during the docking process [28, 29].
• Rescoring approaches: Processes in which ligands are re-ranked after the initial
search can help improve the accuracy of predictions [4, 5].
• Integration of experimental information: Algorithms that integrate docking and
experimental information, such as mass spectrometry or electron microscopy
data, are being develo ped to enhance validation and prediction accuracy [6].
It is important to note that docking simulations continue to benefit from
multidisciplinary collaboration among computer scientists, structural biologists,
chemists, pharmacologists, and others. Furthermore, applying machine learning
techniques and using high-performance computing resources significantly drives
the state-of-the-art in these areas. As new technologies and approaches are developed, research in molecular docking continues to advance, providing new insights
into drug candidate discovery/planning and understanding protein–ligand
interactions.
170 R. M. de Angelo et al.
2.1 Search Algorithms, Scoring Functions, and Machine
Learning
Machine learning algorithms can be used to optimize parameters in molecular
docking techniques, enhancing their ability to predict more accurate molecular
interactions. In most of the cases, ML-based scoring functions represent a methodology based on supervised learning, employing structural data with labels reflecting
experimentally measured binding affinities. Initially, this ML-integration relies on
traditional approaches such as Support Vector Machines (SVM), Random Fo rest
(RF), and Gradient Boosting Trees (GBT), aiming to improve scoring performance
on reference test sets. The inputs for these models included manually designed
descriptors such as molecular fingerprints, ligand features, atom pair terms, and
force field terms [30, 31]. Currently, there are other approaches to incorporate
machine learning techniques into scoring functions, which can improve simulation
performance and/or optimize the types of result analyses. Below, we will describe
some machine learning-based scoring functions presented in Table 7.1.
2.2 Critical Characteristics of Search Algorithms
Search algorithms are classified into three main groups, according to the methodology applied to explore ligand flexibility: systematic, deterministic, and stochastic
[47]. Table 7.2 presents the critical characteristics of each algorithm.
As expected, an essential factor in the molecular docking process is the choice of
the search algorithm that will position the compound in the active site/binding site of
the target. Among the primary algorithms, Genetic Algorithms (GA) and Lamarckian Genetic Algorithms (LGA) stand out as stochastic methods and explore the
conformations of the receptor–ligand complex by allowing for random conformational modifications of the ligand and, occasionally, specific protein residues [53]. It
is noteworthy to mention the primary reference for the GOLD program, which
employs GA considering the ligand flexibility and the flexibility of some residue
side chains.
GA is based on Darwin’s theory of evolution, considering the ligand’s degrees of
freedom as “genes,” composing “chromosomes” referring to ligand poses. Changes
leading to the generation of new conformations are determined by mutations, which
randomly alter the “genes,” and by “crossover,” allowing the exchange between two
“genes” that will be evaluated by the scoring function, considering the new generations those with the highest estimated affinity [54].
Still, in line with evolutionary ideas from biology, the LGA algorithm incorporates the same biological concepts but is applied to computational processes. This
approach starts with a population of ligand conformations, evaluates their fitness
based on interaction energy, and selects the fittest ligands as parents for the next
generation. The crossover and mutation processes introduce diversity while the
7 Molecular Docking: State-of-the-Art Scoring Functions and Search Algorithms 171
Table 7.1 Description of some machine learning-based scoring functions
Algorithm Name Input feature Dataset Reference
RF-score Counting pairs of protein–ligand
atom types within a predefined
PDBbind
v2007
[31]
distance cut-off
RF | Random
Forest
SFCscore
Descriptors related to ligandspecific interactions and surface
PDBbind
v2007
[32]
RF
area
ΔVinaRF20 Vina empirical and surface area
terms
PDBbind
v2014
[33]
CSAR
dataset
XGB |
eXtreme Gradient Boosting
ΔVinaXGB Vina empirical factors, surface
area components, ligand stability
factors, and bridge water
considerations
ΔLinF9XGB A sequence of Gaussian terms
defining protein–ligand interactions, surface area components,
ligand characteristics, bridge water
PDBbind
v2016
CSAR
dataset
PDBbind
CSAR
dataset
BindingDB
[34]
[35]
considerations, and pocket
attributes
AGL-Score Features of protein–ligand com-
PDBbind [36]
plexes based on algebraic graph
theory
GBT | Gradient Boosting
Tree
ECIF-GBT Counting pairs of protein–ligand
atom types while taking into
account the connectivity of each
PDBbind
v2016
[37]
atom
NN | Nearest
Neighbors
NNScore 1.0 Descriptors of specific interactions
and ligand-dependent
NNScore 2.0 Vina empirical factors, counts of
protein–ligand atom-type pairs
MOAD
PDBbind
MOAD
PDBbind
[38]
[39]
within a predefined distance
threshold
AtomNet Local structure-based 3D grid
DUD-E [40]
from protein–ligand structures
CNN |
Convolutional
Neural
Pafnucy Atom property-based 3D grid from
protein–ligand structures
Kdeep Atom type-based 3D grid from
protein–ligand structures
PDBbind
v2016
PDBbind
v2016
[41]
[42]
[43]
Network
OnionNet Element-specific contacts between
protein and ligand atoms without
PDBbind
v2016
the need for rotation, categorized
by different distance ranges
PotentialNet Atom node feature and distance
matrix
PDBbind
v2007
[44]
graphDelta [45]
(continued)
172 R. M. de Angelo et al.
Table 7.1 (continued)
Algorithm Name Input feature Dataset Reference
GNN | Graph
Neural
Network
SIGN Distance matrix of atom nodes and
Table 7.2 Key characteristics of the main categories of search algorithms
Systematic The systematic approach is the only one mentioned earlier that guarantees
convergence to the global minimum. It systematically and exhaustively explores
all ligand degrees of freedom, traversing the energy surface. The main problem
related to systematic methods is that computational cost grows explosively with
the addition of degrees of freedom in the system. This approach becomes
prohibitive for larger ligands (i.e., with many degrees of freedom). This approach
is not always suitable for applications that rely on more agile methods [48]
Deterministic The deterministic approach uses energy minimization and molecular dynamics
methods. These methods employ the gradient (i.e., the direction of the steepest
ascent on a surface) and system movement over time, respectively.
Therefore, they always ensure convergence to an energy minimum. However,
such a minimum cannot be guaranteed to correspond to the global minimum of
the energy surface. These methods are strictly dependent on the initial conditions
of the system and often get trapped in local minima with high energy barriers [49]
Stochastic This approach involves heuristic methods in exploring the energy surface. Sto-
chastic methods randomly vary all ligand degrees of freedom (translational,
rotational, and conformational) at each step, generating a wide diversity of
solutions. These solutions are evaluated based on a probabilistic criterion to
decide whether they will be rejected. Examples of this approach include Monte
Carlo and Evolutionary Algorithm [50, 51]. The main disadvantage of this
methodology is that convergence to the global minimum is not guaranteed,
requiring multiple independent runs of the algorithm to maximize the probability
of finding an optimal result [52]
Atom node features considering
local environment and distance
matrix
angle matrix of bond edges
PDBbind
v2018
PDBbind
v2016
[46]
fitness of the descendants is re-evaluated. The process is repeated for several
generations until stop criteria are met. The method is called “Lamarckian” because
it allows information learned in previous generations to be passed on to future
generations, leading to convergence in favorable ligand conformations. In this
case, an individual (ligand) with ideal interaction characteristics would pass this
information to the new individual or population (group of ligands) [54]. It is essential
to highlight that the evolution and the widespread use of molecular docking techniques have allowed this computational tool to be used to screen new potential
ligands against a large variety of biological targets [55]. Figure 7.3 summarizes the
search methodologies that can be explored to understand and improve ligand
flexibility in a dockin g simulation.