VeriSIM Life Announces Webinar on Mitigating Translational Risk in Drug Development with AI/ML

San Francisco, CA, May 9th, 2023 - VeriSIM Life (VeriSIM), the leading artificial intelligence (AI)-enabled, unique R&D decisions de-risker for breakthrough drug development, today announced a webinar hosted by CEO and Founder Dr. Jo Varshney, PhD and Dr. Jeff Barrett, PhD, Chief Science Officer at Aridhia Digital Research Environment (

The webinar, titled “Mitigating translational risks with AI-enabled MIDD,” will be free to attend virtually on June 7th, 2023 11am PT/2pm PT. The webinar will follow an “ask-me-anything” format, but will be focused around discussing the challenges inherent in traditional approaches to drug development. 

“Drug developers increasingly incorporate computational/in silico solutions to reduce costs and improve translation from the bench to the clinic. But even with these tools, challenges persist in determining efficacy, disease pathways, mechanism of action, toxicity, best route of administration and ideal formulation,” explains Dr. Jo Varshney. “Now, predictive AI and machine learning techniques (such as the BIOiSIM platform) are filling gaps previously out of reach to traditional approaches.” 

Topics that attendees will learn about in the webinar include model-informed drug development, explainability, the role of machine learning in a “fused” framework, and high level insight into VeriSIM Life’s paradigm for yielding outputs from data inputs. 

About VeriSIM Life 

VeriSIM Life has developed a sophisticated computational platform that leverages advanced AI and ML techniques to improve drug discovery and development by significantly reducing the time and money it takes to bring a drug to market. BIOiSIM® is a first-in-class 'virtual drug development engine' that offers unprecedented value for the drug development industry by narrowing down the number of drug compounds that offer anticipated value for the treatment or cure of specific illnesses or diseases. The platform predicts the likelihood of a candidate’s success in clinical trials early in the preclinical stage, while reducing unnecessary experimentation and better informing key program decisions. For more information, visit