Experimental and machine learning-based exploration of repurposed drugs reveals chemical features underlying phospholipidosis

Phospholipidosis (PLD) is a cellular adverse effect caused by, among other things, cationic amphiphilic drugs. There is interest within pharma discovery to predict this phenomenon, as it can impact the outcome of phenotypic cellular screens and significantly delay drug development processes. The development of accurate and validated machine learning models for predicting drug-induced PLD across different cell lines and research centers could provide a valuable early application tool for the pharmaceutical industry, potentially accelerating drug discovery and reducing the risk of late-stage failures. We report here the assembly, curation, testing, and modeling of one of the largest datasets of repurposed drugs (5,000+) tested for PLD induction on different cell lines. A machine learning classification method was developed and validated to predict whether molecules are prone to induce PLD effects when applied in cell-based screens.