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Conflict of interest: There is no conflict of interest.
Author’s contribution: Anchal Sharma: Data curation, Writing original draft, Concep-
tualization, and Methodology; Nitish Kumar:Writing– Review and editing and Formal
analysis; Jyoti:Writing– Original draft preparation, Aanchal Khanna:Writing– Origi-
nal draft preparation, Preet Mohinder Singh Bedi:SupervisionandInvestigation
Abbreviations used
BCS biopharmaceutics classification system
SAR structure–activity relationship
DDI drug-drug interactions
CYP450 cytochrome450
FDA Food and Drug Administration
NAPQI N-acetyl-p-benzoquinoneimine
CADD computer-aided drug design
ProTox-II prediction of toxicities of chemicals
eMolTox prediction of molecular toxicity with confidence
TOXNET Toxicology Data Network
ToxiM toxicity prediction tool for small molecules
ToxCast toxicity forecasting
Tox21 toxicology in the twenty-first century
AI artificial intelligence
ML machine learning
EPA Environmental Protection Agency
MW molecular weight
QSAR quantitative structure–activity relationship
NCGC National Institutes of Health Chemical Genomics Centre
AUROC area under the receiver operating characteristic curve
ADMET absorption, distribution, metabolism, elimination and toxicity
LOO CV leave-one-out cross-validation
DNN deep neural networks
CNN convolutional neural network
MetaboGen metabolite generator
BoM bond of metabolism
GCNN graph convolutional neural networks
EFL English as a foreign language
NCE novel chemical entities
logP lipophilicity
SR solubilization ratio
SCbs Solubilization capacity of the bile salt
SC aq Solubilization capacity of water
LEFR linear solvation energy relationship
FaSSIF fasted state simulated intestinal fluids
204 Anchal Sharma et al.
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QSPR Quantitative structure property relationships
NRTL non-random two liquid
ANNs artificial neural networks
COSMO-RS Conductor-like screening model for real solvents
ANFIS Adaptive neuro fuzzy inference system
APIs active pharmaceutical ingredient
GBRT gradient tree boosting
ET extra tree
SVR support vector regression
BRR Bayesian ridge regression
RMSE root mean square error
LR linear regression
DT decision tree
GRNN generative regression neural network
ADA-DT Adaboost algorithm decision tree
ADA-LR Adaboost algorithm-linear regression
ADA-GRNN generative regression neural network
GCN graph convolutional network
GCM group contribution method
COMPASS condensed-phase optimized molecular potentials for atomistic simulation studies
MLPNN multi-layer perceptron neural network
GCM group contribution method
GPR Gaussian process regression
AChE acetylcholinesterase
DCC dicyclohexylcarbodiimide
MAO-B monoamine oxidase inhibitors
BA Bat optimization algorithm
TPI test-particle insertion
MD molecular dynamics
NVP nevirapine
HSP Hansen solubility parameters
ANOVA analysis of variance
DNN-PP deep neural network properties predictor
CMD carboxy muconolactone decarboxylase
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Abhimannu Shome, Chahat, Keshav Taruneshwar Jha,
Pooja A. Chawla
✶
, and Muralikrishnan Dhanasekaran
10 Recent advancement in binding
free-energy calculation
Abstract: Binding free energy calculation is a crucial method utilized in studying the in-
teractions between ligands and proteins, providing valuable insights into thermodynam-
ics and facilitating rational drug design. Recent progress in computational techniques,
force fields, and enhanced sampling approaches has significantly enhanced the accuracy
and dependability of these calculations. Furthermore, the integration of machine learn-
ing and data-driven methods has further improved predictive capabilities. Quantum me-
chanical approaches have also been integrated to more precisely account for electronic
and quantum effects. These advancements have revolutionized the field, leading to a
deeper comprehension of molecular recognition and the development of more effica-
cious drugs. Ongoing advancements in this area hold immense potential for accelerating
the drug discovery process and advancing our understanding of ligand–protein interac-
tions. This chapter explores various types of binding free energy calculation methods in-
cluding molecular dynamic simulation, Monte Carlo simulation, molecular mechanics/
generalized Born surface area, and molecular mechanics/Poisson–Boltzmann surface
area.
Keywords: Binding free energy, Gibbs free energy, molecular dynamics, MMGBSA,
MMPBSA
10.1 Introduction
Over the last decade, there has been a surge in enthusiasm for the use of quantum me-
chanical approaches in lead drug identification and optimization. This is due to an urge
for a better understanding of connections throughout biomolecular frameworks and
is enabled by an ongoing rise in computational resources [1]. Until recently, the drug
✶
Corresponding author: Pooja A. Chawla, University Institute of Pharmaceutical Sciences and
Research, Baba Farid University of Health Sciences, Faridkot 151203, Punjab, India,
e-mail: pvchawla@gmail.com
Abhimannu Shome, Chahat, Keshav Taruneshwar Jha, Department of Pharmaceutical Chemistry,
ISF College of Pharmacy, Ghal Kalan, G. T. Road, Moga 142001, Punjab, India
Muralikrishnan Dhanasekaran, Department of Drug Discovery and Development, Harrison College of
Pharmacy, Auburn University, 3306B Walker building, Auburn, AL 36849, USA
https://doi.org/10.1515/9783111207117-010
https://t.me/med1917
lead design method was dependent on the practical high-throughput screening (HTS) of
chemical collections; despite several attempts to increase its effectiveness and efficacy,
HTS remains a lengthy and costly technology [2]. Protein-ligand three-dimensional (3D)
architectures have been utilized in the optimization of pharmacological lead com-
pounds to increase their overall strength and specificity [3], opening the door for a
more objective strategy in drug discovery roughly 40 years earlier. This was supported
by conceptual advances, enhanced computational algorithms, as well as more rapid
computing resources, allowing computational techniques to be used to characterize pro-
tein-ligand interactions in silico and acquire approximate values of small-molecule con-
necting free energy, along with large-screen chemical collections via structure-based
strategies. Nowadays, computer-aided chemistry serves as a well-established and im-
portant technique in the drug development process. The binding free energy (ΔG
bind
)
plays a fundamenta l role in the interaction between proteins and smal l molecules,
which is of crucial importance in pharmaceutical chemistry and holds significant value
for the pharmaceutical industry. There is no endeavor sufficient to compute it accu-
rately while being computationally profitable. Precise estimation of receptor-smal l-
molecule interactions in the beginning phases of the medication development workflow
would help to systematically create novel, more powerful, and less hazardous medica-
tions, conserving valuable time, labor, and resources. The precise estimation of ΔG
bind
in the setting of protein-small-molecule connection is dependent on (1) the system’s
power capacity surface, (2) amino acids mobility, (3) incorporating the existenc e of
water atoms inside the binding region, and (4) the solvation model implemented. The
previous two topics have lately been explored. Over the past 25 years, there certainly
has been an incredible advancement in both conceptual composition and algorithmic
advances for determining the amount of binding free energies [4–6], which range from
prompt techniques for application in HTS as well as scoring to slower – but in theory
better accurate – calculations employing free energy perturbation (FEP) or thermody-
namics collaboration [7], focused at directing chemical synthesis over hit-to-lead optimi-
zation [8, 9]:
1. Despitethemajorityofthesepurposesrelying on traditional mechanics-based
fields of force, the introduction of ever-stronger computers has enabled the rapid
growth and utilization of quantum mechanical (QM) devices to si mulate macro-
molecular networks with the help of drug lead identification and creation in re-
cent years. The drug design data resource (D3R) blind obstacles in 2015, 2016, and
2018 demonstrated the necessity of technique creation and comparison for pos-
ture evaluation and binding tendency evaluation of ligands [10].
2. It must be pointed out that QM composition, getting mathematically reliable, in-
corporates every element that contributes to power as well as considering con-
nections or consequences that are typically overlooked in the study of molecular
force fields: electronic polarization, transfer of charges, halogen connections, and
the formation of covalent bond; additionally, it provides the option of addressing
every component and connections on the same basis, preventing the necessity for
212 Abhimannu Shome et al.
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system-dependent parameterization. Grimme and Schreiner [11, 12] just wrote an
intriguing article on the modifications that they envision for the coming 25 years
in computer-aided chemistry, one of which is how inexpensive QM approaches
will prevail over force fields.
3. It ought to be emphasized that the theory of QM has played an essential part in the
computational designing of drugs from the beginning of time (cf. W.G. Richards’ pio-
neering research on the pharmacology of quantum [13]).
4. Furthermore, QM equations are frequently employed for generating force field
torsional capacity to a high degree ab initio information and partial atomic
charges via combining electrostatic surface prospective [14], in quantitative
structure–activity relationship (QSAR) techniques [15, 16], for investigating the
mechanisms of reaction, to examine small-molecule tension [17], as well as molec-
ular docking, across additional uses. The objective of this chapter is to showcase
the increasing significance of QM methods in computing binding interactions
within the context of structure-based drug lead optimization. Additionally, it aims
to illustrate various functions that employ diverse methodologies currently avail-
able in this field [18, 19].
5. If the person looking for information is more curious about the creation than
utilization of QM equipment’s for binding affinity estimations, a great up-to-
date examination is accessible; an outstanding work by Yilmaz and Korth [20]
provides a comprehensive overview of the creation of semiempirical QM and
density operational techniques evolved via the use of hydrogen bonds and dis-
persion modifications.
10.2 Thermodynamic aspect behind the binding
free energy
In biochemistry, binding free energy refers to the ability of a molecule to bind to an-
other molecule through various weak or noncovalent interactions. This binding is cru-
cial to many biological processes including protein-protein interactions, enzyme-
substrate interactions, and the binding of ligands to receptors.
10.2.1 Gibbs free energy
Gibbs free energy, represented as ΔG, is a thermodynamic principle utilized to mea-
sure the maximum potential work obtainable from a chemical reaction or physical
process under constant temperature and pressure conditions. It is named after the
American scientist Josiah Willard Gibbs [21, 22]. The Gibbs free energy (ΔG) is a mea-
10 Recent advancement in binding free-energy calculation 213
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