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5.4 Significance of computational methods
in optimization of chiral compounds
5.4.1 Quantum mechanical studies
Quantum mechanical calculations, such as DFT or ab initio methods, are widely used to
investigate the electronic structure and properties of chiral compounds. These calcula-
tions provide information about molecular energies, geometries, electronic transitions,
and spectroscopic properties. Quantum mechanical calculations can be used to predict
the stereochemistry and chiral properties of compounds, as well as to optimize their
structures. The conformational features of a molecule critically influence its physical
and chemical properties. Therefore, an accurate conformational analysis must be car-
ried out to arrive at stable conformers of the target compounds. [5] synthesized a target
compound for which they performed conformational search by Spartan 14 using Monte
Carlo, searching together with Merck molecular force field molecular mechanics calcula-
tions. Then, full geometry optimizations of these obtained structures were employed by
the DFT/B3LYP/6–311++G(2d,p) method. Finally, four stable conformers were obtained for
the target compounds. Gibbs free energies, relative Gibbs free energies, and Boltzmann
weighting factors of the conformers were calculated. In order to analyze the structural
differences between conformations, Newman projection was utilized to visualize the
conformation of noncyclical molecules. Different stereogenic centers in the target com-
pounds may lead to two different absolute configurations. Electronic circular dichroism
was performed to get reliable stereochemical alignment of the target compounds. Elec-
tronic circular dichroism is a spectroscopic technique used to study the differential ab-
sorption of circularly polarized light by chiral molecules. It provides information about
the electronic structure and the spatial arrangement of chromophores within a molecule
[5]. For attaining stable conformations of their target inhibitor, Farrokh zadeh et al.,
2020, performed geometrical optimization at the B3LYP/6–311++G(d,p) theoretical level
using Gaussian16 [4]. Similarly [6] designed and synthesized antiangiogenetic TX agent
and performed conformational analysis, and the initial structure of TX agent was con-
structed using CAChe and then, Global minimum analysis was performed using CON-
FLEX with an MM2 force field and they also calculated the solvation free energy (dGW)
using MOPAC. Energy calculations were performed with the PM3 Hamiltonian using
MOPAC [6].
5.4.2 Molecular dynamics
MD simulations simulate the movement and behavior of atoms and molecules over
time. They can be used to study the conformational dynamics and stability of chiral
compounds in solution or when interacting with biological targets. MD simulations
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provide insights into the interactions between the compound and its environment
and can guide the optimization process. Geng et al. [5] conducted MD simulation
using the Thinker minimizer in order to exclude bad implausible van der Waals con-
tacts and to obtain the most advantageous conformation of chiral compo unds. They
can be used to study the stability of chiral compounds in solution or when interacting
with biological targets. MD simulations provide insights into the interactions between
the compound and its environment and can guide the optimization process [5]. Far-
rokh zadeh et al., 2020, conducted MD simulation run for (R,R) and (R,S) conformation
of their target compound using AMBER18 software. Parametrization of the protein
molecules was carried out using the FF14SB force field while the ANTECHAMBER
module was used to parameterize the ligand molecules. Generation of topology and
parameter files for the complexes was then performed using the LEAP module, which
was also used to solvate the systems explicitly in a TIP3P water box of size of 10 Å. All
hydrogen bonds were constrained during the MD production run using the SHAKE
algorithm, after which the resulting coordinates and trajectories saved at every 1 ps
were analyzed using the CPPTRAJ and PTRAJ modules of AMBER18. Microcal origin
software was used for data plots, whereas UCSF Chimera and Biovia discovery studio
were used for graphical analyses and visualization [4]. Similarly, Ohkura et al. [6] per-
formed MD simulations with an InsightII-Discover under a consistent valence force
field (CVFF) on a silicon graphics octane workstation. Brahmachary et al. [11] per-
formed MD after molecular mechanics studies for conformation generation by using
Particle Mesh Ewald (PME) to deal with the long-range electrostatic interactions with
SHAKE procedure [7].
5.4.3 Molecular mechanics
Molecular mechanics is a computational approach used to study the behavior and
properties of molecules at the atomic level. It is a simplified representation of molecu-
lar systems, where the interactions between atoms are approximated using mathe-
matical functions and parameters. Molecular mechanics-generalized Born surface
area (MM/GBSA) is the most popular endpoint free energy approach in the Computer
aided drug design field, which helps in identifying crucial binding site residues and
their interaction patterns. Farrokh zadeh et al., 2020, used the MM/GBSA method to
estimate the differential binding affinities of (R,R) and (R,S) of their target diastereom-
ers toward HBVC dimer [4, 11]. They conducted research and tried to found out that
why the inhibitory potency of compound 1r (the benzylic substituent at the pro-R posi-
tion) is 30 times over that of compound 1s (the benzylic substituent at the pro-S posi-
tion) against Aurora A by optimizing the compounds through molecular mechanics by
the sander program in AMBER9.0 to relax the system [7].
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5.4.4 Molecular docking
Molecular docking is a computational method used to predict the binding orientation
and affinity of a chiral compound with a target receptor or enzyme. By simulating the
docking process, researchers can identify the optimal binding conformation of the com-
pound, which is crucial for its activity. Docking studies can help optimize the stereo-
chemistry of the compound to enhance its binding affinity and selectivity. Brooks et al.
[2] conducted docking studies of sertraline and the conformations were docked to the
leucine transporter structure with which they were originally co-crystalized. The pose
of the enantiomer was similar to that of the co-crystalized compound (RMSD = 2.64) and
the estimated free energy of binding was also not very different (−7.25 kcal/mol versus
− 6.29 kcal/mol) – the ranking within the library is dramatically changed. R, S-Sertraline
comes in at rank 67 (out of 3,879 docked structures) while the S, R enantiomer ranks
407. This demonstrates that a moderate difference in binding pose due to chirality can
result in dramatic ranking shifts within a library [2]. R, S-Nutlin, and the enantiomer S,
R-nutlin were seeded into the NCI Diversity Set II structure library file and docked to
the MDM2 structure, with which they were originally co-crystalized. After docking, con-
sistent with earlier observations, the R,Snutlin (the co-crystalized form) docked at rank
51, whereas the enantiomer docked at rank 3,482 out of 3,915 structures. Likewise, the
estimated binding energy worsened from −6.70 kcal/mol to −3.95 kcal/mol. The RMSD
between the two docking poses was 7.07. This coincides with what was previously re-
ported and shows that when chirality has a strong influence on the geometry of the
molecule, the docking pose will vary dramatically between enantiomers [2]. Molecular
docking involves fitting a protein and ligand together in a favorable conformation to
form a complex. Structural information from such a complex may help to clarify the
inhibitory nature between the inhibitor and the enzyme [5]. Similarly, Patel et al. [8]
performed molecular docking for predicting the desired binding conformations on a
Glide 4.0 and it was found that the (R)-enantiomer was more potent than the (S)-
enantiomer of their target compound [8].
5.4.5 NMR studies
NMR techniques have proven to be highly versatile in the detection and analysis of
atropisomer mixtures. Variable-temperature NMR studies have emerged as a valuable
tool for assessing the activation energy barrier (ΔG⧧) associated with atropisomer in-
terconversio n. When the interconversion barriers are low (ΔG⧧), elevated tempera-
tures cause spectral line, broadening or merging, enabling the direct calculation of
ΔG⧧, ΔH⧧, and ΔS⧧, based on the exchange rate constants (k) or half-lives (t
1/2
) of the
interconversion processes [3]. For compounds with faster interconversion rates (t
1/2
<
1 s), line–shape analysis can be employed to determine the rate constants (k) by simu-
lating exchange processes using dynamic NMR models. In cases where an intermedi-
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ate exchange regime is observed (k ≈ frequency difference (Δν
0
) of monitored nuclei),
the rate constants (k) can be calculated from the Δν
0
values of diastereotopic nuclei at
the coalescence temperature (T
c
) of the interconverting atropisomers. When the sys-
tem exhibits slow-to-intermediateexchangeregime(k < Δν
0
), two-dimensional ex-
change spectroscopy (EXSY) can be utilized. EXSY measures the intensity of chemical
exchange cross peaks, as a function of mixing time, providing enhanced sensitivity
for determining the rate constant (k) [3]. For systems with higher rotational barriers,
kinetic analysis (i.e., time-course NMR) can be employed to monitor the isomerization
of purified atropisomers over time and at different temperatures. By studying the con-
version of isomers through first-order reaction kinetics, the exchange rate (k) and re-
action half-life (t
1/2
) can be derived [3].
5.4.6 Virtual screening
In drug discovery programs, virtual screening is maturing into a powerful tool. It is
also useful when physical screening is difficult, expensive, and/or inefficient for large
libraries. Several software applications are available that will perform virtual screen-
ing, including: GLIDE, Auto dock, and Gold. Essentially, virtual screening examines
the geometric and charge “fit” of a small molecule for a designated binding site on a
target protein. Once bound, an approximate free energy of binding can be calculated.
Through virtual screening, researchers can rapidly explore a vast chemical space,
considering different stereoisomers, conformations, and substitutions. This computa-
tional approach allows for the prediction of binding affinity and selectivity of chiral
compounds toward target receptors. By screening large compound libraries in silico,
virtual screening enables the identification of promising candidates for further exper-
imental validation. One such example was the screening for S-adenosyl methionine
decarboxylase (AdoMetDC) inhibitors.
Physical screening of AdoMetDC requires a radioactive assay, measuring the re-
lease of radiolabeled CO
2
as SAM (AdoMet) is converted to decarboxylated SAM. By
using virtual screening, only a subset (133 selected from the top scoring 300 com-
pounds) of the 1,990 that comprised the NCI Diversity set required physical screening,
and the results of this screen yielded an active compound. Virtual screening projects,
however, can provide misleading results if a library contains errors or does not fully
represent the chemical space occupied by the individual molecules [2].
For example, Nutlin, SDF file from the NCI Diversity Set II (http://dtpsearch.ncifcrf.
gov/FTP/divii.sdf) database. This compound possesses a single chiral carbon (denoted
with * on the 2D structure). The file segment shows the X, Y, Z coordinates of the atoms
and their types. In particular, the atom stereo parity column can have one of four val-
ues: (i) 0 indicating not stereo; (ii) 1, odd R configuration; (iii) 2, even S configuration; or
(iv) 3, indicating either odd or even or unmarked. Furthermore, when chiral informa-
tion is present, it is important to note that this information is not necessarily absolute
5 Design of computational chiral compounds for drug discovery and development 87
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chirality but should be considered relative chirality, particularly in cases where a com-
pound has multiple chiral centers [2].
5.5 Chiral switch
A chiral switch refers to the process of developing and marketing a drug as a single
enantiomer (a specific mirror image form of a molecule) when the previously avail-
able drug was marketed as a racemic mixture (a mixture of both enantiomers). Chiral
switches are primarily driven by the need to enhance the safety, efficacy, and/or intel-
lectual property rights of a drug. Based on these new guidelines in the 1990s, most
drug companies and research institutes have begun focusing on single enantiomers
early on when they identify a chiral drug candidate [9]. For those drugs that had been
marketed as racemates, the owners have been switching the drugs to the active enan-
tiomer [10]. This strategy is referred to as chiral switching [2]; examples include the
racemate omeprazole (Prilosec), first launched in 1988 but was switched to esomepra-
zole (Nexium) in 1999 in Europe and in 2001 in the United States as the original pat-
ents expired [2].
Many of the top selling drugs have been marketed as single enantiomer drugs,
such as Fluticasone (GlaxoSmithKline) for respiratory therapeutics and Pravastatin
(BristolMyers Squibb) for cardiovascular therapeutics [10]. As the authors demon-
strated, there was a very definite change toward single enantiomer drugs [2].
5.6 Limitations
Lacking stereochemical information and alternative stereoisomers is not a shortcom-
ing of these databases. To include every possible member of a set of stereoisomers, it
is required to thoroughly explore the potential chiral space and increase the size of
the library per molecule at a rate of 2n, where n is the number of stereocenters pres-
ent in the molecule. In some cases, this can result in a tremendous increase in the size
of the structure library. For example, the drug fluticasone furorate possesses nine chi-
ral carbons [11]. Add ing all possible stereoisomers for this molecule could increase
the number of structures to 512. Although not every stereoisomer is necessarily chem-
ically feasible, this “brute force” approach to exploration of chiral space can result in
dramatic increases in library size. If this library is prepped for docking (i.e., using
Schrödinger’s Ligprep application) and every chemically feasible structure is gener-
ated for every molecule, the final structure count rises to 17,571. In terms of data stor-
age, a 7.0 megabyte file increases to 28.1 megabytes. In the same vein, preparing the
fully enumerated file required 14 h, whereas the simple enumeration (no additional
stereoisomers generated) requ ired 23 min. Clearly, in larger libraries, this problem
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will be more pronounced even if the specific increases in time required for prepara-
tion varies between computers [2].
5.7 Conclusions
Based on their findings, researchers now intend to focus on the development of pharma-
ceutical agents with single atropisomers, specifically those possessing axial chirality.
This work highlights the significance of atropisomerism in the design and optimization
of chiral pharmaceutical compounds, offering potential avenues for the development of
novel therapeutics. Virtual screening and computational approaches play a crucial role
in designing chiral compounds with the desired biological activity. The chirality of a
compound, determined by its three-dimensional arrangement of atoms, can significantly
impact its interactions with biological targets. Computational methods, such as quantum
mechanical calculations and MD simulations, provide valuable insights into the stereo-
chemistry, properties, and binding modes of chiral compounds. By differentiating the
binding modes of diastereomers and predicting the stereochemistry of compounds,
these methods aid in optimizing chiral structures and predicting their biological activity.
Virtual screening techniques further enhance the process by efficiently identifying po-
tential chiral compounds for further investigation, reducing the number of compounds
that need to be physically screened. Overall, the integration of computational methods
in the design of chiral compounds enables a more efficient and targeted approach to
drug discovery and development, enhancing our understanding of the relationship be-
tween chirality and biological activity. However, to make the screening relevant in ad-
dressing stereochemical issues that lie ahead in the process, each chiral compound
should be properly represented in the 3D compound database used for virtual screening
so that the drug discovery team knows which stereoisomer of the compound to pursue.
References
[1] Mariaule, G., De Cesco, S., Airaghi, F., Kurian, J., Schiavini, P., Rocheleau, S., Huskić, I., Auclair, K.,
Mittermaier, A., & Moitessier, N. 3-oxo-hexahydro-1H-isoindole-4-carboxylic acid as a drug chiral
bicyclic scaffold: structure-based design and preparation of conformationally constrained covalent
and noncovalent prolyl oligopeptidase inhibitors. Journal of Medicinal Chemistry, 2016 May 12,
59(9), 4221–4234.
[2] Brooks, W. H., Guida, W. C., & Daniel, K. G. The significance of chirality in drug design and
development. Current Topics in Medicinal Chemistry, 2011, 11(7), 760–770.
[3] Lanman, B. A., Parsons, A. T., & Zech, S. G. Addressing atropisomerism in the development of
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Milardi, D. Inhibition of Aβ amyloid growth and toxicity by silybins: The crucial role of
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[7] Cheng, Y., Cui, W., Chen, Q., Tung, C. H., Ji, M., & Zhang, F. The molecular mechanism studies of
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[8] Patel, P. D., Patel, M. R., Kaushik-Basu, N., & Talele, T. T. 3D QSAR and molecular docking studies of
benzimidazole derivatives as hepatitis C virus NS5B polymerase inhibitors. Journal of Chemical
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[9] Tucker, G. T. Chiral switches. Lancet, 2000 Mar 25, 355(9209), 1085–1087.
[10] Marrero-Ponce, Y., & Castillo-Garit, J. A. 3D-chiral atom, atom-type, and total non-stochastic and
stochastic molecular linear indices and their applications to central chirality codification. Journal of
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[11] Brahmachary, E., Ling, F. H., Svec, F., & Fréchet, J. M. Chiral recognition: Design and preparation of
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Biswa Mohan Sahoo
✶
, Pooja Chawla, Subas Chandra Dinda,
Narahari Narayan Palei, Bhupendra Singh,
and Bibhas Chandra Mohanta
6 Role of integrated bioinformatics
in structure-based drug design
Abstract: Bioinformatics refers to the interdisciplinary approach that involves the ap-
plication of computational tools to collect, store, analyze, and interpret biochemical
and biological information. It combines biology, computer science, mathematics, and
statistics to provide a forum for new drug discovery processes. Several studies on ge-
nomics and proteomics provide opportunities to design new targets for drug discov-
ery. The interactions of drugs with therapeutic targets are of prime importance for
the development of potential drug candidates. Hence, the structure-based drug design
is considered as an essential tool for faster and more cost-efficient lead discovery as
compared to the traditional method. Further, integrated bioinformatics plays a vital
role in accurately identifying potential molecular biomarkers for diagnosis, prognosis,
and therapies for several disease conditions. This technique reduces time and cost as
compared to the wet-lab-based experimental procedures.
Keywords: Bioinformatics, tools, structure, drug, design, discovery
6.1 Introduction
Drug discovery and development is a multistep process that involves the identifica-
tion and characterization of potential drug molecules to treat the disease condition
safely. This process is a lengthy, expensive, and resource-intensive process [1]. The
drug discovery cycle may take around 12–14 years with the cost ranging from 0.8 to
1.0 billion dollars. High cost, lengthy time duration, high level of risk, uncertainty in
✶
Corresponding author: Biswa Mohan Sahoo, School of Pharmacy and Life Sciences, Centurion
University of Technology & Management, Bhubaneswar, Khurda-752050, Odisha, India
Pooja Chawla, Department of Pharmaceutical Chemistry, University Institute of Pharmaceutical
Sciences and Research, Baba Farid University of Health Sciences, Faridkot 151203, Punjab, India
Subas Chandra Dinda, School of Pharmacy, The Neotia University, Jhinga 743368, West Bengal, India
Narahari Narayan Palei, Amity Institute of Pharmacy, Amity University, Lucknow Campus, Lucknow
226010, Uttar Pradesh, India
Bhupendra Singh, School of Pharmacy, Graphic Era Hill University, Dehradun 248002, India;
Department of Pharmacy, S.N. Medical College, Agra 282002, Uttar Pradesh, India
Bibhas Chandra Mohanta, Department of Pharmacy, Central University of South Bihar, Gaya 824236,
Bihar, India
https://doi.org/10.1515/9783111207117-006
https://t.me/med1917
the results, and highly complex procedures are the main challenges in the develop-
ment of new drug candidates. To overcome these problems, it is necessary to employ
new and more cost-effective methods for drug designing and development. There are
several methods involved in the drug discovery process including random screening,
molecular manipulation, computational approach, and serendipitous research [2].
Among these approaches, the computational methods are considered an efficient
strategy to accelerate and economize the drug discovery and development process.
The key features of the drug discovery process include target selection, target valida-
tion, lead identification, lead optimization, preclinical, toxicity profile study, formula-
tion, clinical studies, drug approval process, and marketing (Figure 6.1) [3].
6.1.1 Computational approaches in drug discovery
and development process
Due to an increase in the availability of information regarding several biological targets
of different disease conditions, several computational approaches such as molecular
docking, de novo design, molecular similarity calculation, virtual screening, pharmaco-
phore-based modeling, and pharmacophore mapping have been applied extensively [4].
Targets are the naturally existing cellular components responsible for the various patho-
logical conditions of different diseases. Targets may be receptors, enzymes, nucleic acids,
hormones, ion channels, and so on. Drug discovery processes is mainly based on two
computational approaches: structure-based drug design (SBDD) and ligand-based drug de-
sign (LBDD) [5]. Computer-aided drug design (CADD) methods have a few limitations such
as lead molecules derived from the virtual screening process that still need validation
through preclinical and clinical assessments before market approval. CADD emerges as a
fast and reliable technique in pharmaceutical and medicinal research since it not only
saves time but also helps cut costs of designing therapeutic agents (Figure 6.2).
Drug Discovery and Development
Bioinformatics Virtual Screening In silico ADMET Prediction
Docking De novo Design Pharmacokinetic Simulation
Computational Biology Focused Library Design Computational System Biology
Computational Tools
Disease related
Genomics
Target
Identification
Target
Validation
Lead
Identification
Lead
Optimizaion
Preclinical
Study
Clinical
Study
Figure 6.1: Drug discovery and development process.
92 Biswa Mohan Sahoo et al.
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6.1.1.1 Structure-based drug design (SBDD)
SBDD is a target-based approach that involves molecular docking, de novo design, and
molecular dynamics (MD). SBDD is the most efficient approach to the drug develop-
ment process in w hich the 3D structural information of various biological targets is
employed for designing its inhibitors [6]. This approach is mainly based on the hy-
pothesis that the ability of a drug molecule to interact with a specific protein and ex-
hibit a desired biological effect depends on its ability to interact with a particular
binding site on a target protein [7]. The efficiency, accuracy, and speed of the compu-
tational methods mainly depend on several factors such as conformation, generation,
sampling, scoring functions, optimization of algorithms, and calculation of molecular
similarity [8]. The optimization of the drug discovery process is of great importance
for the pharmaceutical industry because the identification and selection of suitable
lead molecules have intense impact on the cost and profitability of new drug substan-
ces. In the new drug discovery and development process, several software available
for determining pharmacokinetic parameters include DDDPlus, GastroPlus, and Map-
Check. Similarly, computer-based programs used for ligand interaction study are Au-
toDock, GLIDE, GOLD, and BioSu ite. For molecular modeling and structure–activity
Computer Aided Drug Design
(CADD)
Structure Based Drug Design
(SBDD)
Ligand Based Drug Design
(LBDD)
Molecular Docking
and Scoring
Quantitative structure
activity relationship
De novo design
Molecular
Dynamics
Pharmacophore
modelling
Virtual
Screening
Lead
Identification
Lead
Optimization
Drug
Candidate
Figure 6.2: Steps involved in computational approaches.
6 Role of integrated bioinformatics in structure-based drug design 93
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