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In Silico Approach for Antibacterial Discovery: PTML Modeling of Virtual Multi-Strain Inhibitors Against

Valeria V Kleandrova¹, M Natália D S Cordeiro¹, Alejandro Speck-Planche¹

¹LAQV@REQUIMTE/Department of Chemistry and Biochemistry, Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal.

Pharmaceuticals (Basel, Switzerland)

|February 26, 2025

Summary

Researchers developed a machine learning model to predict and design new antibacterial compounds targeting drug-resistant Staphylococcus aureus strains. This approach accelerates the discovery of novel antimicrobials to combat rising infectious disease threats.

Keywords:

PTML antibacterial fragment fragment-based topological design multilayer perceptron subgraph topological indices

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Area of Science:

Computational chemistry
Machine learning
Drug discovery

Background:

Multidrug-resistant Staphylococcus aureus infections pose a significant global health challenge.
In silico methods offer a pathway to rapidly identify and design novel antibacterial agents.
Targeting multiple S. aureus strains with varying resistance is crucial for effective treatment.

Purpose of the Study:

To develop a perturbation theory machine learning model (PTML-MLP) for predicting and designing inhibitors against Staphylococcus aureus.
To create a versatile virtual inhibitor model capable of targeting diverse S. aureus strains.
To accelerate the early stages of antibacterial drug discovery.

Main Methods:

Utilized chemical and biological data from the ChEMBL database.
Employed the Box-Jenkins approach to convert topological indices into graph-theoretical indices.
Developed a multilayer perceptron neural network (PTML-MLP) model using these indices.

Main Results:

The PTML-MLP model achieved over 80% accuracy in both training and testing.
Fragment-based topological design (FBTD) enabled interpretation of molecular fragments contributing to activity.
Four novel drug-like molecules were designed as potential multi-strain S. aureus inhibitors.

Conclusions:

Perturbation theory machine learning modeling shows promise for early antibacterial drug discovery.
The designed molecules represent promising chemotypes for future synthesis and testing.
This approach can aid in developing versatile anti-S. aureus agents.