Conformal prediction of molecule-induced cancer cell growth inhibition challenged by strong distribution shifts

The drug discovery process often employs phenotypic and target-based virtual screening to identify potential drug candidates. Despite the longstanding dominance of target-based approaches, phenotypic virtual screening is undergoing a resurgence due to its potential being now better understood. In the context of cancer cell lines, a well-established experimental system for phenotypic screens, molecules are tested to identify their whole-cell activity, as summarized by their half-maximal inhibitory concentrations. Machine learning has emerged as a potent tool for computationally guiding such screens, yet important research gaps persist. Consequently, this study focuses on the application of Conformal Prediction (CP) to predict the activities of novel molecules on specific cancer cell lines. Two CP models were constructed and evaluated on each cell line, resulting in a total of 120 performance evaluations (60 cell lines x 2 CP models) per training-test partition. From this comprehensive evaluation, we concluded that, regardless of the cell line or model, novel molecules with smaller CP-calculated confidence intervals tend to have smaller predicted errors once measured activities are revealed. It was also possible to anticipate the activities of dissimilar test molecules across 50 or more cell lines. These outcomes demonstrate the robust efficacy that CP models can achieve in realistic and challenging scenarios, thereby providing valuable insights for enhancing decision-making processes in drug discovery.