Recent Applications of Artificial Intelligence in Discovery of New Antibacterial Agents.

Antimicrobial resistance (AMR) represents today a major challenge for global public health, compromising the effectiveness of treatments against a multitude of bacterial infections. In recent decades, artificial intelligence (AI) has emerged as a promising technology for the identification and development of new antibacterial agents. This review focuses on AI methodologies applied to discover new antibacterial candidates.

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

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.

Ruthenium-Catalyzed Cycloaddition for Introducing Chemical Diversity in Second-Generation β-Lactamase Inhibitors.

Ruthenium(II) alkyne azide cycloaddition (RuAAC) is an attractive reaction to access 1,5-triazole derivatives and is applicable to internal alkynes. Here, we explore RuAAC to introduce molecular diversity on the diazabicyclooctane (DBO) scaffold of β-lactamase inhibitors. The methodology presented is fully regioselective and enabled synthesis of a series of 1,5-triazole DBOs and trisubstituted analogues.

Machine learning and structure-based modeling for the prediction of UDP-glucuronosyltransferase inhibition.

UDP-glucuronosyltransferases (UGTs) are responsible for 35% of the phase II drug metabolism. In this study, we focused on UGT1A1, which is a key UGT isoform. Strong inhibition of UGT1A1 may trigger adverse drug/herb-drug interactions, or result in disorders of endobiotic metabolism.

Capturing a Comprehensive Picture of Biological Events From Adverse Outcome Pathways in the Drug Exposome.

The chemical part of the exposome, including drugs, may explain the increase of health effects with outcomes such as infertility, allergies, metabolic disorders, which cannot be only explained by the genetic changes. To better understand how drug exposure can impact human health, the concepts of adverse outcome pathways (AOPs) and AOP networks (AONs), which are representations of causally linked events at different biological levels leading to adverse health, could be used for drug safety assessment. To explore the action of drugs across multiple scales of the biological organization, we investigated the use of a network-based approach in the known AOP space.