Vincent Blay; Xiaoyu Li; Jacob Gerlach; Fabio Urbina; Sean Ekins
Drug Discov. Today, 2022, 103351
https://doi.org/10.1016/j.drudis.2022.103351

Abstract

DNA-encoded libraries (DELs) allow starting chemical matter to be identified in drug discovery. The volume of experimental data generated also makes DELs an attractive resource for machine learning (ML). ML allows modeling complex relationships between compounds and numerical endpoints, such as the binding to a target measured by DELs. DELs could also empower other areas of drug discovery. Here, we propose that DELs and ML could be combined to model binding to off-targets, enabling better predictive toxicology. With enough data, ML models can make accurate predictions across a vast chemical space, and they can be reused and expanded across projects. Although there are limitations, more general toxicology models could be applied earlier during drug discovery, illuminating safety liabilities at a lower cost.