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

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 ac

SUBMITTER: Bagdad Y 

PROVIDER: S-EPMC11465252 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature