Abstract 1826: New inhibitors of oncology targets discovered using the affinity-mediated selection of DNA-encoded chemical libraries followed by machine-learning

Abstract X-Chem’s DNA-Encoded Chemical Library platform generates a large amount of binding data for each target it is screened against. Here, we report the application of machine learning to these large datasets with case studies that include the identification of novel inhibitors of two oncology targets. One, ERα, is an estrogen-activated receptor that controls transcription and stimulates breast cell proliferation including in breast cancer. The second, DCAF1, is the substrate-recognition component of an E3 ligase complex that is associated with the proliferation of colon cancer cells. Over 100 billion DNA-Encoded compounds were synthesized and screened for their affinity to each target. The selection output data was then featurized and used to train multiple machine-learned models. Models with demonstrated predictive power were then used to score virtual catalogue compounds which were subsequently acquired and tested. A range of successful data-science strategies will be described including those that lead to double-digit nM inhibitors of each of DCAF1 and ERα. Citation Format: Anthony D. Keefe, Mohamed AbouZleikha, AJ Baghaie, Matthew A. Clark, Marie-Aude Guie, John P. Guilinger, Ryan Walsh, Ying Zhang. New inhibitors of oncology targets discovered using the affinity-mediated selection of DNA-encoded chemical libraries followed by machine-learning abstract. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts) ; 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84 (6Suppl): Abstract nr 1826.