Abstract 3662: DNA-encoded chemical library screening and machine-learning for hit identification for WD40 repeat targets including oncology targets such as DCAF1, WDR5 and WDR12

Abstract Training machine-learned models using DNA-Encoded Chemical Library screening data “DEL-ML” and using these models to rank compounds in virtual catalogs has been successful for hit identification for a range of individual oncology targets including ER alpha and c-KIT [McCloskey et al 2020]. Here we describe the importance of library quality and diversity, protein quality, screen design, appropriate profile choice, training set preparation and evaluation, promiscuity labeling, chemical representations, modeling algorithms and their tuning in the context of a systematic target class-based evaluation for a range of oncology targets including DCAF1, WDR5 and WDR12. This approach yielded first-in-class, drug-like ligands for multiple targets that were characterized using a range of biophysical approaches including x-ray crystallography. Citation Format: Anthony D. Keefe, Arrash J. Baghaie, Paolo A. Centrella, Matthew A. Clark, Marie-Aude Guié, John P. Guilinger, Jenny Liu, Ryan T. Walsh, Ying Zhang. DNA-encoded chemical library screening and machine-learning for hit identification for WD40 repeat targets including oncology targets such as DCAF1, WDR5 and WDR12 [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 3662.