Disrupting The Drug Development Process Using Multi-Modal Deep Learning and Patient-on-a-Chip Platform

Drug development process is a lengthy and expensive process that can take more than 10 years and cost billions of dollars. Despite the major efforts, the process will fail in more than 90% of the cases due to safety concerns. In this talk I will present a different approach for the task of assessing drug safety. An alternative to the animal models widely used today. We tackle the task using a BIO-AI approach. The main ingredient is a 'patient-on-a-chip', state-of-the-art biological and robotic AI platform. The platform acts both as a human body simulator and as a data generator. It is capable of performing millions of interactions between the patient-on-a-chip and potential drugs, while taking into account patient genomics, ethnicity and current conditions. The platform generates high frequency microscopy and biochemical time series data, describing the interactions between the miniaturised organs and the drug compound in detail. The unique data generated by the platform is used to train state-of-the-art multimodal, multi-task deep learning architecture to predict drug safety. The patient-on-a-chip trained platform is used by big pharmaceutical companies to predict safety of new drug candidates, and to provide patient-specific drug safety recommendations.

About the Speaker:
Shahar Harel is the Head of AI at Quris, where he tackles some of the world's greatest challenges in the drug development space. Previously to Quris, he led data science teams in both NASDAQ-listed companies and in smaller, highly innovative startups. Shahar holds an M.Sc. in Computer Science from the Technion – Israel Institute of Technology. During his research, Shahar developed generative deep learning methods for generation of novel chemical compounds with unique characteristics, with the ultimate goal of accelerating drug discovery and development. His research works are presented at high profile data science conferences and published in top-tier pharmaceutical science journals.