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Ligand- and Structure-Based Virtual Screening in Drug Discovery

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Biophysical and Computational Tools in Drug Discovery

Part of the book series: Topics in Medicinal Chemistry ((TMC,volume 37))

Abstract

The virtual screening (VS) is an important tool used in the modern drug discovery process to identify new leads and drug-like molecules for therapeutic interventions. With the rapid advancements in the computational hardware supported by advanced algorithmic progress and comprehensive data, the VS has gained fast momentum in drug discovery paradigm. The VS protocol is performed predominantly by using the ligand-based (LB) and structure-based (SB) VS methods with each one having its own merits and demerits. The LBVS method works on the similarity approach based on the physicochemical parameters, chemical functionality, and shape similarity of the ligands. The SBVS method works on the complementarity of the ligand with the target binding site and is more preferred than LBVS due to the consideration of both ligand and target information. Further in the SBVS, the available conformational information of the active chemical space helps in making reasonable decisions for the ligand selection. However, the limitations in scoring functions and incorrect pose prediction may result in the inaccurate SB models of limited performance in VS experiments. Various factors interplay in the development of successful VS models and the fine tuning of these factors leads to the efficient VS models with high sensitivity. In this chapter, some of the recent ligand- and structure-based approaches for virtual screening in drug discovery are described.

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Abbreviations

5-HT3AR:

Serotonin receptor

AAC:

Amino acid composition

AChE:

Acetylcholinesterase

AChEI:

Acetylcholinesterase inhibitors

AMBER:

Assisted model building with energy refinement

AMP:

Antimicrobial peptides

AmpC:

AmpC β-lactamase

AR:

Androgen receptor

BACE:

β-secretase

BEAR:

Binding estimation after refinement

BEDROC:

Boltzmann-enhanced discrimination of ROC

BRD4:

Bromodomain-containing protein 4

BuChE:

Butyl choline esterase

CADM:

Conventional autodock docking method

CASF:

Comparative assessment of scoring functions

CDK2:

Cyclin dependent kinase 2

CmaA1:

Mycobacterial cyclopropane synthase

CoMFA:

Comparative molecular field analysis

CoMSIA:

Comparative molecular similarity indices analysis

CRF:

Corticotropin-releasing factor receptor-1

DHFR:

Dihydrofolate reductase

DprE1 :

Decaprenylphosphoryl-beta-D-ribose oxidase

DUD-E :

Directory of useful decoys enhanced

EF:

Enrichment factor

EGFR :

Epidermal growth factor receptor

FGFr1:

Fibroblast growth factor receptor 1

FN:

False negatives

FP:

False positives

FXa:

Factor Xa

Gal-3:

Galectin-3

Glide SP:

Grid-based ligand docking with energetics Standard precision

Glide XP:

Grid-based ligand docking with energetics Extra precision

GnRH:

Gonadotropin releasing hormone receptor

GOLD:

Genetic optimization for ligand docking

GPCR:

G-protein coupled receptor

GR:

Glucocorticoid receptor

GSK-3:

Glycogen synthase kinase-3

H1R:

Histamine H1 receptor

H4R:

Histamine H4 receptor

HASL:

Hypothetical active site lattice

HBA:

Hydrogen bond acceptor

HBD:

Hydrogen bond donor

HDACis:

Histone deacetylase enzyme inhibitors

HIVpr:

Human immunodeficiency virus type 1 protease

HM:

Homology model

HTS:

High-throughput screening

HY:

Hydrophobic

IFD:

Induced fit docking

IFP:

Induced fit protocol

LBDD:

Ligand-based drug design

LBVS:

Ligand-based virtual screening

M. Tb :

Mycobacterium tuberculosis

MACCS:

Molecular ACCess System

MC4:

Melanocortin receptor-4

MCH:

Melanin concentrating hormone receptor

MDS:

Molecular dynamics simulation

MixMD:

Mixed molecular dynamics

MLR:

Multivariate linear regression

MM-GBSA :

Molecular mechanics-generalized born surface area

MM-PBSA :

Molecular mechanics-Poisson–Boltzmann surface area

MOE:

Molecular operating environment

NAMD:

Nanoscale molecular dynamics

NMR:

Nuclear magnetic resonance

PAINS:

Pan assay interference

PARP:

Poly (ADP-ribose) polymerase

PDB:

Protein Data Bank

PDE4B:

Phosphodiesterase 4B

PDE5A:

Phosphodiesterase 5A

PDGFrb:

Platelet derived growth factor receptor kinase

PET:

Positron emission tomography

PFP:

Probe finding probability

PLANTS:

Protein–ligand ANT system

PLIF:

Protein–ligand interaction fingerprint

PPARγ:

Peroxisome proliferator activated receptor gamma

PR:

Progesterone receptor

QSAR:

Quantitative structure–activity relationships

RBF:

Radial basis function

RF:

Random forest

rms:

Root mean square

RMSD:

Root mean square deviation

rmse:

Root mean square error

RNA:

Ribonucleic acid

ROC-AUC:

Area under the ROC curve

ROCS:

Rapid overlay of chemical structures

RXRα:

Retinoid X receptor alpha

SBDD:

Structure-based drug design

SBVS:

Structure-based virtual screening

siSASA:

Site solvent accessible surface area

STAMP:

Structural alignment of multiple proteins

SVM:

Support vector machine

SVMGen:

Support vector machine general

TN:

True negatives

TP:

True positives

VS:

Virtual screening

β2R:

β2-Adrenoceptor

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Authors and Affiliations

  1. School of Health Sciences, Kaziranga University, Jorhat, Assam, India

    Shome S. Bhunia

  2. Amity University, Lucknow, India

    Mridula Saxena

  3. Global Institute of Pharmaceutical Education and Research, Kashipur, Uttarakhand, India

    Anil K. Saxena

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  1. Shome S. Bhunia
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  2. Mridula Saxena
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  1. GIPER, Kashipur, India

    Anil Kumar Saxena

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Bhunia, S.S., Saxena, M., Saxena, A.K. (2021). Ligand- and Structure-Based Virtual Screening in Drug Discovery. In: Saxena, A.K. (eds) Biophysical and Computational Tools in Drug Discovery. Topics in Medicinal Chemistry, vol 37. Springer, Cham. https://doi.org/10.1007/7355_2021_130

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