Ashootosh Tripathi

Ashu Tripathi, Ph.D., specializes in fusing state-of-the-art data-science workflows with microbial natural-product discovery. His group leverages large-scale genome mining, MS/MS-networking, and cheminformatics pipelines to prioritize strain–metabolite pairs, then applies machine-learning classifiers to predict bioactivity and dereplicate known scaffolds. Tripathi built a virtual screening platform—an AI-augmented funnel that combines virtual docking, polypharmacology network analysis, and biophysical kinetics to uncover molecular-glue degraders—and recently launched DeepChiral, an ECD- and NMR-informed deep-learning model for automated stereochemical assignment. These tools sit atop a 55 k-member natural-product library and >6 TB of searchable multi-omic data, enabling rapid hit validation, resistance-pathway mapping, and structure-guided analogue design. By coupling predictive analytics with automated fermentation, high-throughput LC-MS, and cell-based phenomics, Tripathi delivers a fully integrated “data-to-lead” pipeline that has produced first-in-class UV-protective metabolites, next-generation CRBN modulators, and five patent families—demonstrating the translational power of data-driven natural-products research.