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Updated: May 20, 2025

A Method to Assess Bacteriocin Effects on the Gut Microbiota of Mice

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Published on: July 25, 2017

Seeking Correlation Among Porin Permeabilities and Minimum Inhibitory Concentrations Through Machine Learning: A

Sara Boi¹, Silvia Puxeddu², Ilenia Delogu²

¹Department of Chemical and Geological Sciences, University of Cagliari, S.P. 8 km 0,700, I-09042 Monserrato, CA, Italy.

Molecules (Basel, Switzerland)

|March 27, 2025

Summary

Developing antibiotics against Gram-negative bacteria is hard due to their outer membrane. This study shows increased antibiotic permeation through the OmpF porin enhances antimicrobial activity, aiding drug discovery.

Keywords:

antibiotics artificial intelligence drug design in silico screening infectious diseases liposomes molecular dynamics

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Published on: June 28, 2014

Area of Science:

Microbiology
Medicinal Chemistry
Computational Biology

Background:

Gram-negative bacteria possess a challenging outer membrane barrier for antibiotic penetration.
Developing novel antibiotics requires understanding factors influencing outer membrane permeation and efficacy.

Purpose of the Study:

To investigate the relationship between antibiotic permeation through the OmpF porin of *Escherichia coli* and antimicrobial efficacy.
To develop a machine learning model correlating permeation data with antimicrobial activity.

Main Methods:

Measured relative permeability coefficients (RPCs) using liposome swelling assays.
Determined minimum inhibitory concentrations (MICs) against *E. coli*.
Developed a machine learning approach combining classification and regression models.

Main Results:

Quantified a negative correlation between RPC and MIC, indicating higher permeation generally improves antimicrobial activity.
Observed this correlation is significant mainly for compounds with substantial permeability.
Identified that other factors dominate antimicrobial potency when permeation is low.

Conclusions:

Antibiotic permeation through the OmpF porin is a key factor for antimicrobial efficacy against *E. coli*.
The developed machine learning approach provides a valuable *in silico* tool for early-stage drug discovery.
This strategy can accelerate the identification of promising antibiotic candidates by reducing synthesis and testing efforts.