QSAR modelling and molecular docking studies of 3-(Pyridine-3-yl)-2-oxazolidinone derivatives as potential antibacterial agents against gram-positive pathogens

Due to the high prevalence of multidrug-resistant bacterial strains, there is a need to create new effective antibacterial substances. Linezolid shows effective antibacterial activity and sufficient therapeutic effects. However, due to frequent use, the formation of bacterial resistance, as well as myelotoxicity, its usage is not widespread. New derivatives with better efficiency and low toxicity should be studied. This article describes the analysis of various derivatives of 3-(Pyridine−3-yl)− 2-oxazolidinone by their antibacterial action through Quantitative Structure–Activity Relationship (QSAR) modelling and molecular docking. For this purpose, several QSAR models were developed by the GA-MLR method against four gram-positive strains – S. aureus (ATCC 25923), S. pneumoniae (ATCC 49619), E. faecalis (ATCC 29212), and S. xylosus (ATCC 35924). The models are reliable and have been validated with good parameters: the value of R 2 was within 0.7819 to 0.9230; Q 2 LOO varied from 0.6847 to 0.8704; R 2 adj was in the range from 0.7213 to 0.9049. In addition, molecular docking experiments were conducted using the protein receptor from H. marismortui (50S ribosome subunit, PDB code: 3CPW). The analyzed derivatives had excellent values of binding energy varying from − 9.89 ± 0.06 to − 11.21 ± 0.04 kcal/mol. It exceeded the binding energy of the reference drug linezolid (− 9.84 ± 0.01 kcal/mol), thereby paving the way for the development of novel drug candidates.

Authors

Institutions

Publication Details

Journal
Discover Chemistry.
Published
2026-09-25
DOI
https://doi.org/10.1007/s44371-026-00987-w
Primary Topic
Computational Drug Discovery Methods
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
article

QSAR modelling and molecular docking studies of 3-(Pyridine-3-yl)-2-oxazolidinone derivatives as potential antibacterial agents against gram-positive pathogens

Rupinder Preet Kaur, Sanjana Manjh
Discover Chemistry.
Computational Drug Discovery Methods
article

QSAR modelling and molecular docking studies of 3-(Pyridine-3-yl)-2-oxazolidinone derivatives as potential antibacterial agents against gram-positive pathogens

Rupinder Preet Kaur, Sanjana Manjh
article en

Abstract

Due to the high prevalence of multidrug-resistant bacterial strains, there is a need to create new effective antibacterial substances. Linezolid shows effective antibacterial activity and sufficient therapeutic effects. However, due to frequent use, the formation of bacterial resistance, as well as myelotoxicity, its usage is not widespread. New derivatives with better efficiency and low toxicity should be studied. This article describes the analysis of various derivatives of 3-(Pyridine−3-yl)− 2-oxazolidinone by their antibacterial action through Quantitative Structure–Activity Relationship (QSAR) modelling and molecular docking. For this purpose, several QSAR models were developed by the GA-MLR method against four gram-positive strains – S. aureus (ATCC 25923), S. pneumoniae (ATCC 49619), E. faecalis (ATCC 29212), and S. xylosus (ATCC 35924). The models are reliable and have been validated with good parameters: the value of R 2 was within 0.7819 to 0.9230; Q 2 LOO varied from 0.6847 to 0.8704; R 2 adj was in the range from 0.7213 to 0.9049. In addition, molecular docking experiments were conducted using the protein receptor from H. marismortui (50S ribosome subunit, PDB code: 3CPW). The analyzed derivatives had excellent values of binding energy varying from − 9.89 ± 0.06 to − 11.21 ± 0.04 kcal/mol. It exceeded the binding energy of the reference drug linezolid (− 9.84 ± 0.01 kcal/mol), thereby paving the way for the development of novel drug candidates.

Discover Chemistry.Vol. 3(1)
Guru Nanak Dev University (IN), Guru Nanak Dev University College, Verka (IN)
Openalex Percentile: Top 9%
Computational Drug Discovery Methods
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.