Speaker Profile

Ph.D., Head of Translational Sciences, VeriSIM Life

Biography
Dr. Chakravarty leads external research collaborations and pursues opportunities and defines scientific applications of the BIOiSIM platform. He works specifically in the areas of preclinical data translatability, relevant endpoints, and exhaustive review of preclinical models, leveraging our AI/ML platform for the acceleration of the drug development process for potential shortening of the timeline to IND and NDA. His research and development experience encompasses metabolic disease, physiology, in vivo pharmacology, and translational science. He has been in the drug discovery space for 15+ years with one of his antisense compounds completing Phase II clinical trials recently. Prior to VeriSIM Life, Chakravarty led teams at Pfizer, Roche, and NASA. He holds a Ph.D. in Nutrition and Biochemistry from Case Western Reserve University. He has published 18+ peer-reviewed scientific articles, delivered talks at several conferences, and received numerous awards for his contributions.


AI & Data Science Showcase:
VeriSIM Life

VeriSIM Life created a platform that enables AI-driven bio-simulations to de-risk drug R&D decisions by predicting the value before human trials.

AI-driven Accelerated Therapeutics Development for COVID-19
We use in silico modeling using BIOiSIM, an AI-integrated mechanistic modeling platform by utilizing known preclinical in vitro and in vivo datasets to accurately simulate systemic therapy disposition and site-of-action penetration of the investigational compounds implicated in COVID 19 pathogenesis.

 Session Abstract – PMWC 2022 Silicon Valley


The PMWC 2022 AI Company Showcase will provide a 15-minute time slot for selected AI companies to present their latest technologies to an audience of leading investors, potential clients, and partners. We will hear from companies building technologies that expedite the pre-clinical and clinical drug discovery and development process, accelerate patient diagnosis and treatment, or develop scalable systems framework to make AI and deep/machine learning a reality.