Innovative Multistage ML-QSAR Models for Malaria: From Data to Discovery.

Malaria presents a significant challenge to global public health, with around 247 million cases estimated to occur annually worldwide. The growing resistance of parasites to existing therapies underscores the urgent need for new and innovative antimalarial drugs. This study leveraged artificial intelligence (AI) to tackle this complex challenge.

Hit-to-lead optimization of a pyrazinylpiperazine series against Leishmania infantum and Leishmania braziliensis.

An early hit-to-lead optimization of a novel pyrazinylpiperazine series against L. infantum and L. braziliensis has been performed after an extensive SAR focusing on the benzoyl fragment of hit (4).

Pred-hERG: A Novel web-Accessible Computational Tool for Predicting Cardiac Toxicity.

The blockage of the hERG K(+) channels is closely associated with lethal cardiac arrhythmia. The notorious ligand promiscuity of this channel earmarked hERG as one of the most important antitargets to be considered in early stages of drug development process. Herein we report on the development of an innovative and freely accessible web server for early identification of putative hERG blockers and non-blockers in chemical libraries.

Tuning HERG out: antitarget QSAR models for drug development.

Several non-cardiovascular drugs have been withdrawn from the market due to their inhibition of hERG K+ channels that can potentially lead to severe heart arrhythmia and death. As hERG safety testing is a mandatory FDArequired procedure, there is a considerable interest for developing predictive computational tools to identify and filter out potential hERG blockers early in the drug discovery process. In this study, we aimed to generate predictive and well-characterized quantitative structure-activity relationship (QSAR) models for hERG blockage using the largest publicly available dataset of 11,958 compounds from the ChEMBL database.