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4.16 GOLD
The GOLD (Genetic Optimization for Ligand Docking) docking tool stands as a pivotal
software in the field of molecular modeling and drug discovery with the maximum
number of manuscripts, with a search of more than 24,000 over the NCBI PubMed cen-
tral. The huge popularity of AutoDock in recent years pushed GOLD to the second most
used docking program (Figure 4.2). The program was developed by the Cambridge Crys-
tallographic Data Centre (CCDC). Like AutoDock, GOLD also use GA to explore the con-
formational space of ligands within protein binding sites, facilitating the prediction of
optimal binding modes [44, 45]. The GA mimics the process of natural selection, itera-
tively refining ligand poses to identify energetically favorable binding configurations.
Its versatility, accuracy, and user-friendly interface makes GOLD a valuable asset for
medicinal chemists and structural biologists engaged in rational drug design, enabling
the identification of promising compounds for further development.
4.17 DOCK
DOCK (acronym for “DOCKing”) is an automated procedure for docking a molecule
into a receptor site. It is another popular docking tool with about 20,000 searches in
the NCBI PubMed central. It characterizes the ligand and receptor assets of spheres
that could be overlaid through a clique detection procedure. Historical ly, the DOCK
algorithm addressed rigid body docking using a geometric matching algorithm [31].
Over the years, scoring based on on-the-fly optimization, exhaustive search, an im-
proved geometrical and chemical matching algorithms for rigid body docking, and
flexible ligand docking were added to improve the algorithm’s ability to find the low-
est energy binding mode. The force-field scoring function include van der Waals, elec-
trostatics, desolvation, and hydrogen bonding. The latest version of DOCK (DOCK6) is
written in C++ and uses GAs and de novo design for fragment-based ligand searching
for molecular docking [56]. The ligand–receptor complex is scored by means of steric
fit, chemical complementation, or pharmacophore similarity. Specifically, it focuses
on predicting the preferred orientation and conformation of a ligand when bound to
a target receptor.
4.18 AutoDock Vina
AutoDock Vina is a widely used molecular docking software with 12,644 search results
over NCBI PubMed central. It was developed by the Molecular Graphics Laboratory at
The Scripps Research Institute using advanced algorithms for faster and more accu-
rate ligand–receptor binding predictions than its predecessor, AutoDock [48]. This
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open-source software employs a combination of empirical scoring functions and a sto-
chastic global optimization algorithm to explore the conformational space of ligands
and efficiently search for energetically favorable binding modes. AutoDock Vina has
gained popularity in the field of computational biology and drug discovery due to its
high-performance docking capabilities, and the ability to handle various types of bio-
molecular targets. Researchers and drug developers leverage AutoDock Vina to gain
insights into molecular interactions, identify potential drug candidates, and optimize
lead compounds in the pursuit of novel therapeutic solutions.
4.19 Glide
Glide docking tools are innovative software applications designed to facilitate the
seamless integration and docking of molecular structures in the field of computa-
tional chemistry and drug discovery [61, 26]. It is among the widely used docking tool,
with about 8,000 search results over NCBI PubMed central, for automated screening
of huge libraries. It designs a series of hierarchical filters to search for possible poses
and orientations of the ligand within the binding site of the receptor. Ligand flexibil-
ity is handled by an exhaustive search of the ligand torsion angle space. Initial ligand
conformations are selected based on torsion energies and docked into rece ptor-
binding sites with soft potentials. Then, a rotamer exploration is used to further
model receptor flexibility. It also utilizes molecular dynamics simulations and scoring
functions to assess the energetics and geometry of potential binding poses. By provid-
ing accurate predictions of ligand–receptor interactions, these tools empower scien-
tists to streamline the drug discovery process, identify potential lead compounds, and
optimize molecular structures for enhanced therapeutic efficacy. Glide docking tools
have become indispensable in the quest for novel drug candidates, offering powerful
and efficient means to explore the vast chemical sp ace and accelerate the develop-
ment of new pharmaceutical agents.
4.20 FlexX
FlexX is a molecular docking tool utilized in computational drug discovery with about
2000 search results in NCBI PubMed central. It was developed by BioSolveIT, and em-
ploys an efficient and flexible ligand docking algorithm to predict the binding modes
and affinities of small molecules within the active sites of protein targets. It uses an
incremental construction algorithm to sample various ligand conformations, resulting
in optimal binding [25]. The ligand-based fragment is first docked into the active site
by matching hydrogen bond pairs and metal and aromatic ring interactions between
the ligand and the protein. Then, the remaining components are incrementally built
4 State-of-the-art modeling techniques in performing docking algorithms 75
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up under a set of predefined rotatable torsion angles to account for ligand flexibility.
The FlexX scoring function is based on Böhm’s work. Its current version employs an
empirical scoring function based on electrostatic interactions, directional hydrogen
bonds, rotational entropy, and aromatic and lipophilic interactions. The interactions
between functional groups are also taken into account by assigning the type and ge-
ometry for groups.
4.21 Conclusion
Molecular docking stands as a foundation stone in the field of structural bioinformatics
and computer-aided drug discovery. Its ability to predict and analyze ligand–protein
interactions has revolutionized the way researchers approach drug development, allow-
ing for more efficient and targeted drug discovery processes. As computational methods
continue to advance and our understanding of biological systems deepens, molecular
docking is poised to play an even more significant role in shaping the future of drug
discovery and structural biology. The ongoing integration of experimental data and the
development of advanced algorithms will further refine the accuracy and reliability of
molecular docking, ensuring its continued relevance in the ever-evolving landscape of
biomedical research.
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https://t.me/med1917
Yash Chauhan, Ajay Sharma, Arya Lakshmi Marisatti, Neha Singh,
Sahil Kumar, and Kalicharan Sharma
✶
5 Design of computational chiral compounds
for drug discovery and development
Abstract: The chirality of a compound, which refers to its three-dimensional arrange-
ment of atoms, can have a significant impact on its biological activity. Chirality arises
when a molecule contains an asymmetric carbon atom, also known as a chiral center,
which has four different substituents attached to it. Spatial arrangement of substitu-
ents having two mirror image (enantiomers) forms can exhibit different interactions
with biological targets, leading to variations in their activity.
Computational approaches differentiate the binding modes of two diastereom-
ers of a target compound. Change in chiral ity of a compound from R, S to R, and R
significantly affects its biological activity. Computational methods play a crucial
role in optimizing chiral compounds. Quantum mechanical calculations, such as
density functional theory or ab initio methods, can provide valuable insights into
the electroni c structure, properties, and stereochemistry of chiral compounds.
These calculations aid in predicting the stereochemistry and chiral properties of
compounds and optimizing their structures. Computational methods, such as mo-
lecula r dynamics, quantum mec hanical calculations, molecular mechanics, virtual
screening, molecular docking, and nuclear magnetic resonance (NMR). NMR techniques,
such as variable-temperature NMR and time-course NMR, are valuable tools for detecting
and analyzing atropisomer mixtures. Virtual screening techniques can help identify po-
tentialchiralcompoundsforfurther investigation, reducing the number of compounds
that need to be physically screened. In conclusion, the chirality of a compound has a pro-
found effect on its biological activity. These techniques provide information about the sta-
bility and interconversion rates of atropisomers, optimizing chiral compounds and
understanding their interactions with biological targets. These methods provide valuable
insights into the stereochemistry, conformational dynamics, stability, and binding proper-
ties of chiral compounds, facilitating the design.
✶
Corresponding author: Kalicharan Sharma, Department of Pharmaceutical Chemistry, ISF College of
Pharmacy, Moga 142001, Punjab, India, e-mail: sharmakcpt@gmail.com
Yash Chauhan, Neha Singh, Sahil Kumar, Department of Pharmaceutical Chemistry, DIPSAR, DPSRU
110017, New Delhi, India
Ajay Sharma, Arya Lakshmi Marisatti, Department of Pharmacognosy and Phytochemistry, SPS,
DPSRU 110017, New Delhi, India
https://doi.org/10.1515/9783111207117-005
https://t.me/med1917
Keywords: Chiral compounds, enantiomers, thalidomide tragedy, stereochemistry, so-
torasib case study, quantum mechani cs, molecular dynamics, NMR studies, virtual
screening
5.1 Introduction
Chiral compounds are crucial in the pharmaceutical industry because many drugs
and biologically active molecules are chiral [1]. This means that they exist in two mir-
ror image forms, or enantiomers, which can have significantly different pharmacolog-
ical properties, such as potency, e fficacy, and toxicity. Therefore, it is important to
identify the active enantiomer and develop it into a drug or therapeutic agent. A chi-
ral compound is a molecule that is nonsuperimposable on its mirror image. This
means that the molecule and its mirror image, known as enantiomers, have identical
physical and chemical properties except for their interactions with other chiral mole-
cules. Chiral modifications, such as the introduction of a chiral center or the resolu-
tion of a racemic mixture, can be a useful tool in this process, as they can lead to the
discovery of more potent and selective compounds with improved pharmacokinetic
properties. One notable example of stereoisomerism is found in the drug thalidomide,
which was prescribed in the 1950s to alleviate morning sickness in pregnant women.
The drug was initially thought to be safe and was widely prescribed, but it was later
discovered that R-enantiomer of thalidomide was responsible for causing severe birth
defects, while the S-enantiomer was safe. This tragedy highlights the importance of
understanding stereochemistry and the potential dangers of using racemic mixtures
in drug development. The development of hit series often involves a process called
lead optimization through various computational methods such as conformational
analysis, density functional theory (DFT) or ab initio methods, molecular docking, mo-
lecular dynamics (MD) simulations, high-throughput screening, Quantitative struc-
ture–activity relationship/Quantitative structure–property relationship modeling, etc.
where modifications are made to the initial hit compound to improve its activity, se-
lectivity, and pharmacokinetic properties. In an increasing number of drug discovery
projects, virtual 3D compound libraries are employed for computer-based screening
against an X-ray or nuclear magnetic resonance (NMR) structure of a target protein
[2]. Chiral compounds can be used to probe biological systems and investigate the
mechanisms of action of drugs and other biologically active molecules. By synthesiz-
ing and studying both enantiomers, researchers can gain insights into the stereochem-
ical requirements for biological activity and better understand the interactions
between the drug and its target.
In summary, chiral compounds are crucial for the development of hit series be-
cause they can lead to the discovery of more potent and selective compounds with
improved pharmacokinetic properties, as well as provide insights into the mecha-
82 Yash Chauhan et al.
https://t.me/med1917
nisms of action of drugs and other biologically active molecules, and computational
methods can greatly speedup the discovery process, while reducing costs, by reducing
the amount of “wet lab” experimental work required.
5.2 Background
To date, only four United States Food and Drug Administration (FDA)-approved drugs
incorporate such a stereochemical feature: the natural products vancomycin and col-
chicine, whose axial chirality is enforced as a result of conventional stereocenters in
rings bridging their biaryl motifs; 8,11 lesinurad, whose configurational stability was
only recognized postlaunch (through separation of the racemic marketed product); 12
and sotorasib (LUMAKRAS/ LUMYKRAS), which represent the first FDA-approved ther-
apy to be manufactured and marketed as a configurationally stable, atropisomerically
pure compound [3].
5.3 Effect of chirality on biological activity
In a recent study by Lanman et al. [3], the focus was on investigating atropisomerism
in the development of Sotorasib, a covalent inhibitor of KRAS G12C, which has poten-
tial therapeutic applications in the treatment of non-small lung cancer. Researchers
discovered that an axially chiral biaryl moiety played a crucial role in effectively en-
gaging a previously hidden protein-binding pocket, leading to enhanced inhibitor po-
tency. This axially chiral biaryl structure exhibited restricted rotation, resulting in
the formation of stable atropisomers with significantly different potency, showing a
10-fold difference. To assess the stability of these atropisomers, researchers em-
ployed a combination of NMR spectroscopy, HPLC (high- performance liquid chroma-
tography), and computational methods. They utilized various techniques such as
variable-temperature NMR, time-course NMR, and chiral HPLC to evaluate the configu-
rational stability of axially chiral bonds with different rotational barriers. One of the
key design elements of Sotorasib was the axially chiral biaryl linkage, which contrib-
uted to the atropisomerism phenomenon, and ultimately optimized its binding to the
KRAS G12C target [3]. Similarly multiple computational approaches have been applied
to explore, validate, and differentiate the binding modes of (R,R) and (R,S) of their target
compound and revealed that chirality change from R,S to R,R exhibited higher potency
inhibition against hepatitis B virus capsid (HBVC) [4].
5 Design of computational chiral compounds for drug discovery and development 83
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