Abstract
Drug‐induced phospholipidosis (PLD) is a side effect of the administration of cationic amphiphilic drugs (CADs). It is desirable to identify and screen compounds with the potential to induce PLD as early as possible in drug development. Recently, a number of in silico methods have been developed to predict PLD. These models are low‐cost and high‐throughput strategies; however, they produce a high number of false positive predictions. The aim of this study was to assess the predictive performance of existing in silico approaches and to develop new strategies for the rapid identification of the potential PLD‐inducers. Studies on 450 chemicals confirmed the high false positive rate of prediction of models based only on log P and p__K__~a~ values. Modification of the methods by incorporating structural information gave moderate improvements in the prediction performance. Therefore, a new strategy, based on molecular fragments captured by SMARTS strings was developed. These structural fragments were able to identify potential PLD‐inducers and achieved a high sensitivity of 85 %. The results showed that the phospholipidosis is linked directly to the molecular structure of chemical; therefore the SMARTS pattern methodology could be used as a first line of screening of PLD potential during the drug discovery process.