Exploring New Small Molecule Inhibitors for SARS‐CoV‐2 3CLpro: A Comprehensive Computational Study

Abstract The SARS‐CoV‐2 3C‐like protease (3CLpro) has emerged as a key target for drug development owing to its crucial role in viral replication of SARS‐CoV‐2 virus. In this study, we employed a ligand‐based virtual screening strategy to identify potential small molecules capable of binding to SARS‐CoV‐2 3CLpro. Using GC376 as a reference compound, we conducted a comprehensive search in a chemical database and identified 178 candidate molecules (CPLs) with structural similarity to GC376. Molecular docking analyses revealed that several CPLs exhibited superior binding affinities to SARS‐CoV‐2 3CLpro compared to GC376. Notably, CPL‐390, CPL‐374, and CPL‐306 displayed the highest binding energies, indicating their potential as inhibitors of SARS‐CoV‐2 3CLpro. Detailed analysis of noncovalent interactions and molecular mechanics generalized Born surface area (MMGBSA) calculations further supported the binding affinity of CPLs to SARS‐CoV‐2 3CLpro. Key interactions with catalytic residues and steric hindrance within the binding pocket highlighted potential inhibition mechanisms for CPL‐306, CPL‐374, and CPL‐390. Moreover, pharmacokinetic analysis indicated that CPL‐306, CPL‐374, and CPL‐390 possessed favorable absorption, distribution, metabolism, and excretion (ADME) properties, positioning them as promising candidates for further drug development. In contrast, GC376 exhibited less favorable pharmacological characteristics, emphasizing the need for novel compounds with improved profiles.

Authors

Institutions

Publication Details

Journal
ChemistrySelect
Published
2024-09-27
DOI
https://doi.org/10.1002/slct.202403533
Citations
2
Primary Topic
Computational Drug Discovery Methods
Type
article
Field-Weighted Citation Impact
0.72

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Exploring New Small Molecule Inhibitors for SARS‐CoV‐2 3CLpro: A Comprehensive Computational Study

J. Manuel Hernández‐Hernández, Rodolfo Daniel Ávila‐Avilés
2 citations
ChemistrySelect
Computational Drug Discovery Methods
0.72
article

Exploring New Small Molecule Inhibitors for SARS‐CoV‐2 3CLpro: A Comprehensive Computational Study

J. Manuel Hernández‐Hernández, Rodolfo Daniel Ávila‐Avilés
article en
2 citations

Abstract

Abstract The SARS‐CoV‐2 3C‐like protease (3CLpro) has emerged as a key target for drug development owing to its crucial role in viral replication of SARS‐CoV‐2 virus. In this study, we employed a ligand‐based virtual screening strategy to identify potential small molecules capable of binding to SARS‐CoV‐2 3CLpro. Using GC376 as a reference compound, we conducted a comprehensive search in a chemical database and identified 178 candidate molecules (CPLs) with structural similarity to GC376. Molecular docking analyses revealed that several CPLs exhibited superior binding affinities to SARS‐CoV‐2 3CLpro compared to GC376. Notably, CPL‐390, CPL‐374, and CPL‐306 displayed the highest binding energies, indicating their potential as inhibitors of SARS‐CoV‐2 3CLpro. Detailed analysis of noncovalent interactions and molecular mechanics generalized Born surface area (MMGBSA) calculations further supported the binding affinity of CPLs to SARS‐CoV‐2 3CLpro. Key interactions with catalytic residues and steric hindrance within the binding pocket highlighted potential inhibition mechanisms for CPL‐306, CPL‐374, and CPL‐390. Moreover, pharmacokinetic analysis indicated that CPL‐306, CPL‐374, and CPL‐390 possessed favorable absorption, distribution, metabolism, and excretion (ADME) properties, positioning them as promising candidates for further drug development. In contrast, GC376 exhibited less favorable pharmacological characteristics, emphasizing the need for novel compounds with improved profiles.

ChemistrySelectVol. 9(37)
Universidad Autónoma del Estado de México (MX), Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional (MX)
Consejo Mexiquense de Ciencia y Tecnología
Openalex Percentile: Top 26%
Computational Drug Discovery Methods
0.72
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.