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Nurix Therapeutics Presents Data at the AACR 2025 Annual Meeting Highlighting the Transformative Potential of Its Proprietary DEL-AI Platform Leveraging Machine Learning to Speed the Discovery of Novel Drugs

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Nurix Therapeutics, Inc.
Nurix Therapeutics, Inc.

Nurix’s DEL-AI platform uses a first-in-class DEL Foundation Model trained on the Company’s proprietary DNA encoded library data

Nurix’s DEL Foundation Model can accurately predict novel binders to therapeutically relevant targets, including many targets considered undruggable, with the potential to accelerate the discovery of novel drugs

SAN FRANCISCO, April 28, 2025 (GLOBE NEWSWIRE) -- Nurix Therapeutics, Inc. (Nasdaq: NRIX), a clinical-stage biopharmaceutical company focused on the discovery, development and commercialization of targeted protein degradation medicines, today presented data that demonstrate the potential of its DEL Foundation Model to enable the rapid in silico identification of novel binders for a broad range of therapeutically relevant proteins, addressing a key barrier in the discovery and development of small molecule drugs. These results were presented at the American Association of Cancer Research (AACR) Annual Meeting in Chicago, IL, which is being held from April 25-30, 2025.

“Nurix’s DEL-AI platform has the potential to accelerate the discovery of breakthrough small molecule drugs—whether they be protein degraders, molecular glues, or inhibitors—by enabling ready-access to tractable chemical matter for an expansive set of proteins, especially those previously considered beyond the reach of drug discovery organizations,” said Gwenn M. Hansen, Ph.D., chief scientific officer of Nurix. “Our team has leveraged the rich datasets generated from rigorously controlled screenings of our customize collection of over five billion unique DEL compounds against hundreds of disease targets and E3 ligase proteins to construct a powerful suite of machine learning models and tools. By directly integrating the sampling density provided by DEL compound repertoires with primary protein sequence information, our model can learn a generalizable structure activity relationship capable of predicting novel binders for nearly any disease-relevant protein target.”

“Our DEL-AI engine is a potential game changer, allowing us to substantially accelerate drug discovery workflows and efficiently identify therapeutic candidates for our wholly owned pipeline and our current and future discovery partnerships,” said Arthur T. Sands, M.D., Ph.D., president and chief executive officer of Nurix. “This powerful research engine is a result of our significant expertise and strategic investments in DEL methodology and our machine learning platform.”

Nurix’s presentation at the AACR 2025 Annual Meeting, titled: “DEL-AI: Proteome-wide in silico screening of multi-billion compound libraries using machine learning foundation models,” described the development of a first-in-class foundation model that was trained on the Company’s high quality, proprietary DEL data. Nurix’s DEL Foundation Model is able to perform virtual DEL experiments on prospective protein target sequences to accurately predict novel binders to a large proportion of therapeutically relevant targets, including many targets considered undruggable. In plots of virtually predicted vs. experimentally-derived DEL screens against therapeutically relevant proteins, Nurix’s DEL Foundation Model demonstrated the ability to accurately predict the experimental results, including experimentally validated binders. Success of the DEL Foundation Model was found to correlate to the degree of similarity of query sequences to proteins within the DEL training set, and data demonstrated that the current model requires as little as 50% amino acid sequence similarity of a query protein to training data to enable binder prediction. Nurix’s model was also capable of inferring binders from chemical space not represented in the training set, suggesting that the model is capable of both protein sequence and chemical structure generalizations.