Benchmarking Deep Learning Models for Predicting Anticancer Drug Potency (IC50): Insights for the Medicinal Chemist

Potency (IC₅₀) prediction of anticancer small molecules plays a vital role in developing drug candidates. Five deep learning models for IC₅₀ prediction--DeepCDR, DrugCell, PaccMann, Precily, and tCNN--were benchmarked using standardized GDSC datasets and recently published anticancer compounds. To ensure practicality, conventional error statistics were supplemented with the absolute percentage error, the three-sigma limit, and a newly proposed statistic named Experimental Variability-Aware Prediction Accuracy. The models performed well on randomly split data and unseen cell lines, but performance declined sharply for unseen compounds. All models exhibited comparable performance trends; however, DrugCell and DeepCDR outperformed the others across several metrics. Assessing prediction accuracy against physicochemical and biological properties of compounds and cell lines revealed its broad independence from these parameters, highlighting an underexplored aspect of machine learning model performance. A user-friendly web server (https://nlplab1.isical.ac.in/ic50.php) was also developed for IC₅₀ prediction of new compounds against cancer cell lines.