Genesis Therapeutics, Genentech Partner for AI-driven Drug Discovery

Genesis Therapeutics, a privately-held company inventing and deploying state-of-the-art artificial intelligence (AI) techniques to augment drug discovery and development, today announced that it has entered into a multi-target collaboration agreement with Genentech, a member of the Roche Group. The collaboration leverages Genesis’ graph machine learning and drug discovery expertise to identify innovative drug candidates for therapeutic targets in multiple disease areas. Genesis will receive an upfront payment and is eligible to receive pre-clinical, clinical and regulatory milestone payments, as well as future royalties on Genentech’s sales of approved drugs resulting from the collaboration.

“This partnership with Genentech enables us to leverage our AI technology platform with Genentech’s unmatched capabilities in molecular innovation and structural biology. We look forward to executing rapidly on this opportunity, which has the potential to change the standard of treatment for patients,” said Evan Feinberg, Ph.D., Genesis co-founder and CEO.

Genesis originated from breakthrough AI research at Stanford University’s Pande Lab, where Dr. Feinberg co-invented PotentialNet, a graph convolutional neural network algorithm that represented a leap forward in molecular property prediction when first published in the peer-reviewed journal, ACS Central Science. Since its founding, Genesis has further accelerated its innovation in AI and developed next generation algorithms, thus pioneering the intersection of protein motion — a key element of drug binding — with neural networks to develop fast and accurate technology for predicting the potency, selectivity, and ADME properties of drug-like molecules.

James Sabry, M.D., Ph.D., Global Head of Roche Pharma Partnering, stated, “AI can help unlock the next generation of innovative therapies for patients in need of additional options. We are excited to work with Genesis’ team to discover medicines currently out of reach using conventional methods.”

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