Valence Discovery to Support Repare Therapeutics on AI-Enabled Drug Discovery in Precision Oncology
MONTREAL and CAMBRIDGE, Mass., March 17, 2021 -- Valence Discovery (“Valence”), an emerging leader in AI-enabled drug design, today announced 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. 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.
“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 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.
About Valence Discovery
Valence Discovery is committed to unlocking the true potential of deep learning in drug design by unifying best-in-class deep learning technologies with intuitive infrastructure to make these technologies more broadly accessible to R&D organizations of all sizes. Valence’s AI-enabled drug design platform has been extensively validated and is currently being used to identify and design drug candidates in collaboration with industry-leading partners. The company is pioneering the application of few-shot learning in drug design and is developing and deploying novel machine learning methods for molecular property prediction, generative chemistry, and multiparameter optimization. Valence (formerly InVivo AI) was founded in 2018, is advised by deep learning pioneer, Dr. Yoshua Bengio, and is proudly headquartered in Montreal at Mila, the world’s largest deep learning research institute, with an office in Cambridge, Mass. To learn more, please visit: valencediscovery.com.
About Repare Therapeutics
Repare Therapeutics is a leading clinical-stage precision oncology company enabled by its proprietary synthetic lethality approach to the discovery and development of novel therapeutics. The Company utilizes its genome-wide, CRISPR-enabled SNIPRx® platform to systematically discover and develop highly targeted cancer therapies focused on genomic instability, including DNA damage repair. The Company’s pipeline includes its lead product candidate RP-3500, a potential leading ATR inhibitor, as well asRP-6306, its CCNE1-SL inhibitor and a Polθ inhibitor programs. For more information, please visit www.reparerx.com.
This post originated from Business Wire.