R. P. Vivek-Ananth

This study develops machine learning models to predict xenobiotic chemicals likely to transfer into human milk using a curated dataset of 375 chemicals and evaluates their performance on internal and external test data.

The paper identifies activity cliffs in the structure-activity landscape of androgen receptor binding chemicals, revealing heterogeneity and improving predictive toxicity models.

This study analyzes the scaffold and structural diversity of secondary metabolites from medicinal fungi and compares them to approved drugs and other libraries, finding high diversity and minimal overlap with existing drugs.

IMPPAT 2.0 is a comprehensive phytochemical atlas of Indian medicinal plants, providing curated data on 4010 plants, 17967 phytochemicals, and traditional formulations.