Machine Learning‐Guided Discovery of Novel Inhibitors of Escherichia coli MurB
- 1School of Pharmacy Jiangsu Ocean University Lianyungang China
Abstract
MurB, a key enzyme involved in bacterial cell wall biosynthesis, represents a promising target for antibacterial drug discovery. In this study, a machine learning assay was employed to identify potential inhibitors targeting E. coli MurB. Five machine learning models were constructed using AutoMolDesigner, of which the RDKit_2D‐based Model_2 was selected as the optimal activity‐prediction model for virtual screening of an in‐house compound library, yielding 238 compounds. These compounds were subsequently subjected to FRED molecular docking, and 16 candidate compounds were selected based on their hydrogen‐bond interactions with the key active‐site residue ASN A:233. Preliminary enzyme inhibition assays conducted at 50 μM identified 4 compounds (Q9, Q11, Q12, and Q13) with inhibition rates exceeding 50%. Further IC₅₀ determination revealed values of 34.44, 31.45, 25.08, and 25.94 μM, respectively. Molecular dynamics simulations combined with principal component analysis were performed to investigate the dynamic behaviors and binding stability of the MurB‐ligand complexes. In addition, a CCK‐8 assay was conducted in HepG2 cells to evaluate the cytotoxicity of the active compounds preliminarily. Collectively, 4 low‐cytotoxic micromolar MurB inhibitors were identified in this study, offering promising scaffolds for further mechanistic investigation and structural optimization.
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BibTeX
@article{song2026machine,
title = {{Machine Learning‐Guided Discovery of Novel Inhibitors of Escherichia coli MurB}},
author = {Shuhua Song and Qingxin Liu and Aoqi Luo and Xiao Shi and Xinxin Si},
journal = {Chemical Biology \& Drug Design},
year = {2026},
month = nov,
doi = {10.1111/cbdd.70422},
url = {https://doi.org/10.1111/cbdd.70422},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Machine Learning‐Guided Discovery of Novel Inhibitors of Escherichia coli MurB
AU - Shuhua Song
AU - Qingxin Liu
AU - Aoqi Luo
AU - Xiao Shi
AU - Xinxin Si
PY - 2026
DA - 2026/11/05
JO - Chemical Biology & Drug Design
DO - 10.1111/cbdd.70422
UR - https://doi.org/10.1111/cbdd.70422
AB - MurB, a key enzyme involved in bacterial cell wall biosynthesis, represents a promising target for antibacterial drug discovery. In this study, a machine learning assay was employed to identify potential inhibitors targeting E. coli MurB. Five machine learning models were constructed using AutoMolDesigner, of which the RDKit_2D‐based Model_2 was selected as the optimal activity‐prediction model for virtual screening of an in‐house compound library, yielding 238 compounds. These compounds were subsequently subjected to FRED molecular docking, and 16 candidate compounds were selected based on their hydrogen‐bond interactions with the key active‐site residue ASN A:233. Preliminary enzyme inhibition assays conducted at 50 μM identified 4 compounds (Q9, Q11, Q12, and Q13) with inhibition rates exceeding 50%. Further IC₅₀ determination revealed values of 34.44, 31.45, 25.08, and 25.94 μM, respectively. Molecular dynamics simulations combined with principal component analysis were performed to investigate the dynamic behaviors and binding stability of the MurB‐ligand complexes. In addition, a CCK‐8 assay was conducted in HepG2 cells to evaluate the cytotoxicity of the active compounds preliminarily. Collectively, 4 low‐cytotoxic micromolar MurB inhibitors were identified in this study, offering promising scaffolds for further mechanistic investigation and structural optimization.
SN - 1747-0277
SN - 1747-0285
ER -