Machine Learning-Based Drug Response Prediction Identifies Novel Therapeutic Candidates for Colorectal Cancer Cell Line KM-12

Abstract

One major hurdle in the discovery of new drugs is the limited capacity of traditional screening methods to efficiently explore the immense space of drug candidates. To bypass this difficulty, computational approaches such as virtual screening (VS) have been developed supported by Machine Learning (ML) models to predict the activities of drug-like molecules on a given target. We developed a ML model for VS of a library of 25 million highly-diverse synthesis-on-demand molecules against the KM-12 colorectal cancer (CRC) cell line. By combining different biological readouts, we discovered compounds with a novel chemical scaffold, not yet described in the context of CRC, with either a prominent cytotoxic or cytostatic effect. This finding underpins the strength of our ML-guided VS protocol in discovering promising CRC drug leads triggering altered biological responses.