Machine Learning and Deep Learning Models for Predicting Noncovalent Inhibitors of AmpC β-Lactamase

Youcef Bagdad; Marion Sisquellas; Michel Arthur; Maria A Miteva

doi:10.1021/acsomega.4c03834

Machine Learning and Deep Learning Models for Predicting Noncovalent Inhibitors of AmpC β-Lactamase

ACS Omega. 2024 Sep 24;9(40):41334-41342. doi: 10.1021/acsomega.4c03834. eCollection 2024 Oct 8.

Authors

Youcef Bagdad¹, Marion Sisquellas¹, Michel Arthur², Maria A Miteva¹

Affiliations

¹ Université Paris Cité, CNRS UMR 8038 CiTCoM, Inserm U1268 MCTR, 75006 Paris, France.
² INSERM, Sorbonne Université, Université Paris Cité, Centre de Recherche des Cordeliers (CRC), 75006 Paris, France.

Abstract

Continuous use of antibiotics leads to the ability of bacteria to adapt by developing complex antibiotic resistance (AR) mechanisms. The synthesis of β-lactamases is a widely observed AR mechanism. The class C β-lactamase (AmpC) causes significant resistance toward β-lactam antibiotics, and new treatments are urgently needed. Noncovalent inhibitors have been developed against a broad spectrum of β-lactamases. In this study, we developed robust and accurate models for predicting noncovalent inhibitors of AmpC using large compound data sets and machine/deep learning modeling. We created support vector machine (SVM), random forest (RF), and feed-forward neural network (FFNN) classification models. The cross-validation (CV) accuracies varied between 80 and 82%, as combined models reached an accuracy of 83%. We analyzed the physicochemical characteristics of the noncovalent inhibitors and predicted the binding modes for some of them. Such models are helpful for identifying new noncovalent inhibitors in order to establish novel solutions against the growing resistance to standard β-lactam inhibitors. The best RF, SVM, and FFNN models for predicting noncovalent inhibitors of AmpC β-lactamase are available in the GitHub repository, https://github.com/UPCmctr/ML-DL-AmpC-B-lactamase.