Antimicrobial Rhenium Tricarbonyl Complexes: Accelerating their Discovery by Leveraging Machine Learning Models

Invited talks

Miroslava Nedyalkova1, Gozde Demirci1, Youri Cortat1, Kevin Schindler1, Fatlinda Rhamani1, Justine Horner2, Aurelien Crochet1, Aleksandar Pavić3, Olimpia Mamula Steiner2, Fabio Zobi1* and Marco Lattuada1

1 University of Fribourg, Fribourg, Switzerland

2 University of Applied Sciences Western Switzerland HES- SO, Fribourg, Switzerland

3 Institute of Molecular Genetics and Genetic Engineering, University of Belgrade, Belgrade, Serbia

fabio.zobi [at] unifr.ch

Abstract

The expanded prevalence of resistant bacteria and the inherent challenges of complicated infections highlight the urgent need to develop alternative antibiotic options. Through conventional screening approaches, the discovery of new antibiotics has proven to be challenging. Anti-infective drugs, including antibacterials, antivirals, antifungals, and antiparasitics, have become less effective due to the spread of drug resistance. In this work, we helped define the design of next-generation antibiotic analogs based on metal complexes. For this purpose, we used artificial intelligence (AI) methods, demonstrating superior ability to tackle resistance in Gram-positive and Gram-negative bacteria, including multidrug-resistant strains. The existing AI approaches’ bottleneck relies on the current antibiotics’ structural similarities. Herein, we developed a machine learning approach that predicts the minimum inhibitory concentration (MIC) of Re-complexes towards two S. aureus strains (ATCC 43300 – MRSA and ATCC 25923 – MSSA). A Multi-layer Perceptron (MLP) was tailored with the structural features of the Re-complexes to develop the prediction model. Although our approach is demonstrated with a specific example of rhenium carbonyl complexes, the predictive model can be readily adjusted to other candidate metal complexes. The work shows the application of the developed approach in the de novo design of a metal-based antibiotic with targeted activity against a challenging pathogen.

Keywords: antibiotic, rhenium complexes, machine learning

Acknowledgement: M.L. and M.N. acknowledge financial support from the Swiss National Science Foundation through the NCCR Bio-inspired materials. M.L., F.Z., G.D., Y.C., A.C. acknowledge financial support from the University of Fribourg. K.S. and F.Z. acknowledge financial support from the Swiss National Science Foundation grant number 200021_196967. O.M.S. and J.H. acknowledge financial support from the Haute Ecole Spécialisée de Suisse Occidentale.