On March 17, 2021 Valence Discovery ("Valence"), an emerging leader in AI-enabled drug design, reported it has entered into a drug discovery agreement with Repare Therapeutics ("Repare", Nasdaq: RPTX), a precision oncology company pioneering synthetic lethality to develop novel therapeutics for genetically-defined patient populations (Press release, Repare Therapeutics, MAR 17, 2021, View Source [SID1234576788]). This agreement will combine Valence’s unique expertise in few-shot learning, generative chemistry, and multiparameter optimization with Repare’s expertise in target identification and medicinal chemistry to rapidly optimize drug candidates against multiple potency, selectivity, safety, and pharmacology criteria.
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"We broadly sought out a machine learning partner for our proprietary drug discovery and ultimately found the ideal fit in our own backyard," says Cameron Black, Ph.D., Executive Vice President of Discovery at Repare Therapeutics. "The depth and breadth of the machine learning talent here in Montreal is exceptional, and we are pleased to be initiating this project with the drug design experts at Valence Discovery."
"As a world-leader in precision oncology, Repare’s team and technologies have the potential to unlock the next generation of precision oncology medicines for patients," says Daniel Cohen, CEO at Valence. "We look forward to bringing our expertise in AI-enabled drug design to bear on such important challenges in drug discovery."
About Valence’s AI-Enabled Drug Design Platform
The Valence platform expands upon academia-leading research done by the company’s founding team at Mila, the world’s largest deep learning research institute. In particular, Valence has pioneered the application of few-shot learning in drug design, allowing the company to unlock prediction tasks for which only small amounts of training data are available, including novel targets and complex ADME criteria, while also ensuring that AI-generated molecules are of high medicinal chemistry quality and readily synthesizable. In addition, Valence uses active learning and iterative optimization strategies to ensure that only the most information-rich compounds are selected for synthesis, enabling the design of compounds meeting the target potency, selectivity, and ADME criteria in fewer iterations, and with far less data, than otherwise possible.