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7 Molecular Docking: State-of-the-Art Scoring Functions and Search Algorithms 183
• Prospective validation: in this step, the goal is to apply the docking method to
systems not yet experimentally studied and compare predictions with subsequent
experimental results.
• Blind docking: in this type of validation, the aim is to perform docking simula-
tions without prior knowledge of the location of the binding site on the target
protein. This tests the method’s ability to predict the location of known binding
sites and estimate unknown sites.
• Comparative validation: here, the focus is on comparing the performance of the
docking method with other protein– ligand affinity prediction methods, such as
force-field-based docking, molecular dynamics, or approaches employing
machine learning techniques.
It is important to note that there is no single validation method universally
applicable in docking studies, as the choice of validation method depends on the
context, research objectives, and availa ble data. In many cases, a combination of
validation methods is used to obtain a comprehensive assessment of docking
technique performance. Additionally, collaboration among researchers and the
availability of well-defined experimental datasets play a fundamental role in the
successful validation of docking predictions [94].
6.1 Success in Docking Predictions and the Consensus
Technique
Using a consensus employing multiple dockin g algorithms is a common and valuable strategy in research involving drug discovery and studies of protein–ligand
interactions. This approach involves conducting multiple independent docking simulations for a protein–ligand complex and then combining the results to obtain a
more reliable and accurate prediction of the binding conformation and affinity of the
compound at the receptor site.
Docking simulations can be sensitive to various variables and parameters, and
this diversity may influence individual results. By achieving a consensus of results
obtained from different docking algorithms, it is possible to reduce the impact of
outliers or inaccuracies, obtaining a more accurate estimate. The consensus takes
into account the inherent uncertainty in docking simulations. Di fferent simulations
may produce slightly different results, but the consensus provides a robust measure
reflecting the intrinsic variability of docking algorithms [86, 95].
The consensus process can help identify binding conformations consistent across
multiple simulations, increasing confidence that these conformations are more likely
to occur in reality. By verifying if various simulations produce consistent results, the
consensus helps assess the reliability of docking predictions. Conformations or
interactions that are consistent in multiple simulations are more likely to be reliable.
184 R. M. de Angelo et al.
The consensus allows researchers to analyze protein–ligand interactions better by
considering a variety of conformations, and possible Consensus helps reduce false
positives (ligands that appear to bind but are not relevant) and false negatives
(ligands that are mistakenly discarded) in dockin g predic tions [86].
In drug discovery research, decisions regarding selecting potential bioactive
compounds and designing new molecules can be guided by consensus-based
docking results, providing a solid foundation for decision-making with lower error
chances. It is important to note that consensus from results via different docking
algorithms is not a definitive solution to all chall enges associated with molecular
docking, and its application should be carefully planned and validated. Additionally,
how to obtain the consensus and the number of simulations to be performed may
vary depending on the system of interest and research objectives. However, in many
cases, a consensus from various docking programs is a practical approach to improve
the reliability of predictions and the quality of results.
7 Inappropriate Use of Validation Methods in Docking
The inappropriate use of validation methods is a significant concern when applying
molecular docking techniques. Validation is essential to determine the reliability of
predictions generated in docking simulations. Improper use can lead to erroneous
conclusions and excessive confidence in results that are not genuinely predictive
[95]. Several factors may contribute to less accurate conclusions about docking
results, such as:
• Lack of experimental data: Initial validation of docking algorithms relies on the
availability of relevant experimental data. Validation becomes compromised if
high-quality experimental data are unavailable for comparison with docking
results.
• Validation on biased datasets: Researchers sometimes validate their results on
datasets previously used to develop or tune scoring functions. This can
overestimate the method’s accuracy, as training data may be “ biased” to fit the
algorithms used.
• Lack of diversity in molecular targets: Some docking studies may focus only on a
specific type of target protein, limiting the generalization of results. Proper
validation should include a variety of molecular targets to determine applicability
in different contexts.
• Failure to consider system flexibility: If the docking algorithm does not account
for the flexibility of the protein and/or ligand, validation may be inaccurate,
especially in highly flexible systems.
7 Molecular Docking: State-of-the-Art Scoring Functions and Search Algorithms 185
• Neglecting entropic terms: The exclusion of entropic terms in estimating binding
energy (terms describing changes in entropy during the binding process) can lead
to inaccurate validation, as these changes are critical for estimating protein–
ligand affinity [2].
• Validation limited to single metrics: Validation often focuses on a single metric,
such as binding affinity. However, other metrics, such as ligand specificity and
correct binding conformation, are also essential and should be considered.
• Ignoring solvent influence: Validation may be inadequate if the solvent is not
adequately treated in docking simulations, as solvation is a critical factor in
protein–ligand interactions.
• Failure to conduct repeatability studies: Docking results may vary depending on
execution conditions and parameters. Repeating studies is vital to assess result
robustness.
A comprehensive approach should be adopted to overcome the previously cited
issues and appropriately use validation methods in the molecular docking process.
This includes selecting independent validation datasets, considering different performance metrics, evaluating flexibility and solvation, and repeating experiments to
ensure result consistency. Collaboration within the scientific community is crucial
for sharing data and knowledge and promoting rigorous validation practices [85, 86,
95].
8 Evaluation from an Expert’s Perspective
Expert evaluation is essential when analyzing results obtained through molecular
docking simulations, and this is due to a series of fundamental reasons. The results of
these simulations can be incredibly complex, involving various metrics, binding
energy data, conformations, and structural information. In this context, the expertise
of a specialist is necessary, enabling the precise interpretation of these complex data.
Experts can assess these results in light of the biological and chemical context,
considering the target protein’s function and the specific application. They can
discern whether the identified conformations are relevant in the biological and
chemical context [81].
Experimental validation is critical to confirming predictions made through
docking. In this regard, a specialist can conceive and conduct experiments that
confirm or refute predictions, becoming a crucial component in the validation
process. Assessing the accuracy of docking simulations is a task that requires
technical and scientific knowledge. A specialist can identify potential sources of
error, such as the quality of protein and ligand structures, the effectiveness of scoring
functions, and the appropriateness of simulated conditions.
186 R. M. de Angelo et al.
In areas such as drug design and discovery, the analysis of docking results often
directly affects strategic decisions, such as the selection of drug candidates and the
design of potential bioactive compounds. In this context, the guidance of a specialist
is of utmost importance. As mentioned earlier, docking simulations have their
limitations and uncertainties. An expert can identify and communicate these limitations clearly, providing a balanced and realistic view of the conclusions. As proteins
and ligands can exhibit flexibility, specialized analys is is necessary to understand
how this flexibility affects docking predictions and consider how dynamic conformations may influence binding. Experts can assess whether docking results align
with current knowledge about the target protein’s biology, its function in the
organism, and its relationship with the overall biological context [82].
In this way, based on their knowledge, an expert can draw solid conclusions and
provide recommendations on the next steps of research, such as additional validation
experiments, ligand optimization, or structural modifications. Expert evaluation is an
essential step in trans lating raw data into scientific knowledge, experimental validation, and guiding strategic decisions in research related to drug discovery, molecular
biology, and chemistry.
9 Use of Machi ne Learning in Molecular Docking
Machine learning and molecular docking have an increasingly close relationship in
the field of drug discovery and studies of protein–ligand interactions. The application of machine learning techniques throu ghout molecular docking simulations has
the potential to enhance the accuracy and efficiency of predictions. Below are listed
some ways in which these two areas relate [96–98]:
• Improvement of scoring functions: Scoring functions used in docking simulations
are crucial for estimating the binding affinity between protein and ligand.
Machine learning techniques can be used to develop more accurate and specific
scoring functions, incorporating a broader range of molecular features and
interactions [30].
• Drug candidate selection: Machine learning algorithms can analyze large librar-
ies of chemical compounds and predict which ones are more likely to be prom-
ising ligands for a specific target protein. This can save time and resources in
virtual screening. For example, a study [74] on COVID-19 utilized about 2.178
million unique protein/ligand binding site combinations available in databases to
optimize the search for potent inhibitors, applying ML techniques to facilitate and
guide in silico studies.
• Classification of active and inactive ligands: Machine learning techniques can be
applied to classify ligands as active or inactive based on their chemical and
structural properties. This is useful for the virtual screening of compound
7 Molecular Docking: State-of-the-Art Scoring Functions and Search Algorithms 187
libraries. In a study by Salimi et al. [99], a screening pipeline was built using
machine learning algorithms integrated with checks for similarity to approved
drugs to find new inhibitors for the vascular endothelial growth factor receptor-2
signaling pathway.
• Prediction of protein–ligand interactions: Machine learning algorithms can pre-
dict specific interactions between proteins and ligands, identifying critical amino
acid residues for binding or preferred binding sites [97].
• Modeling molecular flexibi lity: Molecular flexibility is a significant challenge in
docking simulations. Machine learning methods can be applied to model the
flexibility of proteins and ligands, allowing more realistic predictions. Harmalkar
and Gray [100] studied the difficulties in predicting protein interactions and ways
to apply machine learning to optimize these simulations. The study showed that
docking simulations in the CAPRI [101] challenge included a wide variety of
target types, with 11 out of 28 “easy” targets achieving high-quality structure.
However, for the 17 “difficult” targets, the intrinsic flexibility of biomolecules
remains a challenge, with only 2 achieving high quality.
• Enhancement of validation: Machine learning techniques can be employed to
enhance the validation of docking results, identifying more reliable and effective
metrics to assess the method’s performance. In this case, a study by Zhang et al.
[102] presented an internal and external dataset used to cross-validate eight
machine learning methods. The results showed that the extremely random tree
model performed better and was adopted as the first step in virtual screening.
• Increase in computational efficiency: Machine learning methods can be used to
accelerate the processing of large volumes of data in docking simulations, making
the process more efficient [98].
• Discovery of structure–activity relationships: Machine learning can help discover
complex relationships between the chemical structure of ligands and their bio-
logical activity, aiding in compound optimization. For example, in Hermansyah’ s
study [103], selective DPP-4 inhibitors against DPP-8 and DPP-9 were identified
using an AI-based quantitative structure–activity relationship (QSAR) workflow,
enabling faster screening of millions of molecules for the DPP-4 target compared
to other screening methods.
• Integration of diverse data: Machine learning techniques enable the integration of
data from various sources, such as high-throughput screening data, structural
biology information, and gene expression data, for a more comprehensive system
analysis and optimization of simulations [ 104].
To assess the evolution in the use of machi ne learning in docking simulations,
publications indexed in the “Web of Science” database between 2012 and 2022 were
compiled, relating to the use of molecular docking with machine learning (Fig. 7.5).
188 R. M. de Angelo et al.
Fig. 7.5 Number of studies (2012–2022) considering the use of machine learning techniques and
docking simulations for the design and discovery of drug candidates. Research conducted on the
“Web of Science” platform on October 15, 2023, with the combination of the following keywords:
“machine learning” AND “molecular docking”
A significant increase, approximately 14 times, in the number of publications using
ML techniques to enhance docking studies can be observed.
In summary, integrating machine learning techniques in molecular docking
studies can significantly enhance the accuracy and efficiency of predictions, accelerating the discovery of drug candidates and enabling a deeper understanding of
protein–ligand interactions. Collaboration among computer scientists, structural
biologists, and chemists is crucial for the success of this approach.
10 Advancements and Improvements in Computational
Resources
Recent advancements in computational resources have led to significant progress in
molecular dockin g, playing a crucial role in drug discovery. A rising technique is
structure-based virtual screening, which bene fits from the increasing availability of
high-resolution target structures and large-scale virtual compound libraries, such as
REAL combinatorial libraries. A REAL combinatorial library collects virtually
generated chemical compounds representing a wide structural diversity. The term
“REAL” stems from “Readily Available for Synthesis,” indicating their readiness for
laboratory synthesis. These libraries are designed to offer a vast array of molecules
that can be synthesized efficiently and economically in the laboratory [105].
However, new approaches are necessary to keep pace with these libraries’
exponential growth. In this regard, based on a modular approach, the
V-SYNTHES method emerges as a promising solution. This method conducts
7 Molecular Docking: State-of-the-Art Scoring Functions and Search Algorithms 189
hierarchical screening of a REAL library containing over 11 billion compounds.
Initially, V-SYNTHES identifies the most promising scaffold–synthon combinations as seeds for iterative growth, refining them to select complete molecules with
the best docking scores. This approach enables efficient detection of high-scoring
compounds in an extensive chemical space while docking only a min imal fraction of
the library. This is highly advantageous in terms of computational and economic
efficiency. Furthermore, the method has been experimentally validated in synthesizing and testing cannabinoid antagonists, demonstrating significant improvement
over conventional virtual screenings [105].
Another notable advancement lies in incorporating molecular strain as an additional parameter in evaluating ligand scores during the docking process. A recent
approach utilizes information on relative torsional populations from the Cambridge
Structural Database to precalculate these strain energies, resulting in a more accurate
and efficient evaluation. Retrospective studies have shown that including these strain
energies significantl y improves success rates by preferentially excluding false highscoring molecules. This approach, independent of the scoring function used, stands
out for its speed and practicality, and it is applicable even in large-scale compound
libraries [106].
Moreover, careful selection of small molecule libraries is crucial to this process.
Libraries capable of synthesizing billions of compounds, such as the Examine
library, are available. Finally, despite the advancements represented by AlphaFold2
in predicting protein structures with high precision, studies have shown that the
quality of predicted structures does not always translate into satisfactory performance during the docking process. Removing low-confidence regions from the
predicted structure and flexibilizing side chains are promising strategies to optimize
docking results, underscoring the importance of refined adjustments for successfully
applying these models in drug discovery. It is also important to mention that recent
approaches have been developed to consider the refolding around the ligand. New
models like AlphaFold3 and Neuraplex have showed excellent performance in this
area [ 107].
11 Challenges
Improving molecular docking techniques faces various technical and scientific
challenges as the pursuit of greater accuracy and applicability is ongoing. Some of
the main challenges include enhancing the accuracy of scoring functions and
adequately considering the flexibility of the systems under consideration. Cur rent
functions may not accurately capture all ligand–receptor interactions, especially in
highly flexible systems or those with weak interactions. Integrating the flexibility of
both the protein and the ligand in simulations is a complex task. Developing
effective methods to handle flexibility, including molecular dynamics coupled
with docking, is a significant challenge [2]. Considering entropic terms in scoring
functions is another complicating factor but essential for accurately predicting
190 R. M. de Angelo et al.
protein–ligand affinity. Correctly modeling changes in entropy during the binding
process is equally challenging. The influence of solvent on protein–ligand interactions must be accurately addressed as well. Developing more precise solvation
methods is a considerable challenge. Additionally, experimental validation is crucial
for refining docking techniques. However, it is not always easy to conduct due to the
complexity of protein–ligand interactions and the availability of high-quality experimental data [4].
Extending the applicability of docking to more diverse targets, such as membrane
proteins and protein–protein complexes, is another challenge, as these systems can
be highly complex and require specific approaches. Integrating the docking process
with other computational techniques, such as molecular dynamics, machine learning, and quantum simulations, is an evolving approach to improving the accuracy
and comprehensiveness of predictions. It is worth noting that docking simulations on
multiple targets are complex and challenging, as they require considering the
competition between different ligands at a single or multiple binding sites [71 ].
Other factors that make docking simulations somewhat challenging involve the
following factors: validation and prediction of weak molecular interactions, such as
those involved in allosteric inhibitors or protein modulators; docking simulations
can be computational ly intensive, requiring significant resources; the need for the
development of efficient algorithms to reduce computation time; better understanding of the complexities of biology involved in protein–ligand interactions may be
essential to enhance docking techniques and make them more realistic; availability
of high-quality protein and ligand structures is essential for the quality of docking
results; use of high-quality reference experimental datasets to validate and improve
docking techniques.
Overcoming the challenges mentioned above and others not described here
regarding docking simulations requires multidisciplinary collaboration among computer scientists, structural biologists, chemists, and pharmacologists. Continuous
research and development of more advanced methods will improve molecular
docking techniques and their application in various areas, including drug candidate
discovery.
12 Conclusions
Molecular docking techniques play a crucial role in drug discovery and candidate
drug design worldwide and have undergone significant technological advances.
Table 7.5 presents the key reasons why molecular docking algorithms are essential
in the drug discovery and design process and how various improvements have
contributed to the advancement and qua lity of docking analyses.
Molecular docking techniques are vital in global drug discovery and design. They
help expedite the entire process, save resources, and enhance accuracy in identifying
drug candidates with lower error rates. Ongoing technological advancements in
molecular docking furt her broaden its impact and potential in pharmaceutical and
biological researches.
7 Molecular Docking: State-of-the-Art Scoring Functions and Search Algorithms 191
Table 7.5 Importance and challenges/advancements in docking algorithms
Fact Importance
Accelerates drug candidate
discovery
Resource savings The use of docking techniques helps reduce the number of com-
Expands research scope Molecular docking algorithms make it possible to investigate a
Assists in selectivity studies Docking techniques aid in designing compounds that can selec-
Understanding molecular
mechanisms
Personalization of therapies Docking can also be used to develop drugs for personalized ther-
Increased accuracy of scoring functions
Inclusion of flexibility Improved docking software allows for considering certain flexi-
Integration with Machine
Learning
Use of supercomputers Supercomputers and high-performance clusters allow the execu-
Quality data related to
structural biology
More accurate experimental
validation
The design of drug candidates is lengthy, costly, and risky.
Molecular docking enables the virtual screening of compounds,
expediting the identification of drug candidates with higher success
rates. This saves time and resources
pounds to be synthesized and experimentally tested, saving financial and laboratory resources
wide variety of compounds, including those not easily accessible
through experimental approaches
tively bind to specific targets, minimizing unwanted side effects
Analyzing protein–ligand interactions through docking contributes
to understanding the molecular mechanisms underlying diseases,
leading to the development of more effective treatments
apies tailored to individual patient needs
Challenge/Advancement
Scoring functions used in docking simulations have been
enhanced, becoming more accurate and representative of ligand–
receptor interactions
bility in the protein and the ligand, enhancing prediction accuracy
Integrating machine learning techniques and docking algorithms
improves the prediction of protein–ligand interactions, making
simulations more effective
tion of more complex docking simulations
The availability of high-quality structural biology data, such as
protein structures determined by crystallography or nuclear magnetic resonance, enhances the accuracy of docking simulations
Technological advances in experimental validation, such as highresolution mass spectrometry and cryo-electron microscopy, assist
in confirming predictions from docking simulations
192 R. M. de Angelo et al.
Questions to Answer When Planning a Docking Experiment
What is the main objective of the docking experiment?
What are the features and limitations of the biological receptor’s structure?
What are the main molecular properties of the ligands under study?
Have the three-dimensional structures been verified and optimized?
Are there structural or conformational errors in the selected molecules?
Which docking software will be used? Is it suitable for the system under study?
What is the best scoring function to use for the study in question?
Are there any licensing limitations for the tools being used?
What positive and negative controls will be used to validate the docking protocol?
How will the results be analyzed? What criteria will be used to evaluate the estimated binding
energy and molecular interactions?
What recent advancements in scoring functions and docking flexibility can be leveraged?
Is there a possibility to integrate machine learning techniques to improve the prediction of
protein–ligand interactions?
Is high-quality structural biology data available to enhance the accuracy of docking simulations?
What experimental methods will be used to validate the results of the docking simulations?
What are the limitations of molecular docking simulations?
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