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Related Experiment Video

Updated: Jan 14, 2026

Line Shape Analysis of Dynamic NMR Spectra for Characterizing Coordination Sphere Rearrangements at a Chiral Rhenium Polyhydride Complex

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Published on: July 27, 2022

Machine Learning-Guided Design of Rhenium Tricarbonyl Complexes for Next-Generation Antibiotics.

Miroslava Nedyalkova^1,2, Gozde Demirci¹, Youri Cortat¹

¹Department of Chemistry, Fribourg University, Chemin Du Musée 9, Fribourg 1700, Switzerland.

ACS Bio & Med Chem Au

|October 20, 2025

Summary

This summary is machine-generated.

This study introduces a novel computational method using machine learning to design new rhenium-based antibiotics. This approach effectively predicts antibacterial potency against resistant bacteria, aiding in the development of next-generation antimicrobial agents.

Keywords:

antimicrobial antimicrobial resistance machine learning metal complex rhenium complexes

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Area of Science:

Medicinal Chemistry
Computational Chemistry
Infectious Diseases

Background:

Rising antibiotic resistance necessitates novel antibacterial agents.
Metal-based compounds, particularly rhenium complexes, show promise as antibiotics.
Current machine learning (ML) methods often rely on structural similarity, limiting innovation.

Purpose of the Study:

To develop a computational strategy for the rational design of novel rhenium-based antibiotics.
To utilize machine learning models for predicting the antibacterial potency of rhenium complexes.
To explore structurally diverse rhenium complexes against antibiotic-resistant bacteria.

Main Methods:

Developed predictive ML models (MLP and RF) using structural descriptors.
Estimated minimum inhibitory concentration (MIC) against MRSA and MSSA.
Employed SHAP analysis for interpretability of model predictions.

Main Results:

ML models demonstrated strong predictive performance for antibacterial activity.
Successfully evaluated 26 novel rhenium complexes.
Identified key structural features influencing antibacterial potency.

Conclusions:

The ML-guided approach facilitates the *de novo* design of potent rhenium-based antibiotics.
This strategy is effective for combating antibiotic-resistant bacterial infections.
Offers a promising pathway for discovering next-generation antimicrobial agents.