Prediction of Promiscuity Cliffs Using Machine Learning.

Compounds with the ability to interact with multiple targets, also called promiscuous compounds, provide the basis for polypharmacological drug discovery. In recent years, a plethora of structural analogs with different promiscuity has been identified. Nevertheless, the molecular origins of promiscuity remain to be elucidated. In this study, we systematically extracted different structural analogs with varying promiscuity using the matched molecular pair (MMP) formalism from public biological screening and medicinal chemistry data. Care was taken to eliminate all compounds with potential false-positive activity annotations from the analysis. Promiscuity predictions were then attempted at the level of compound pairs representing promiscuity cliffs (PCs; formed by analogs with large promiscuity differences) and corresponding non-PC MMPs (analog pairs without significant promiscuity differences). To address this prediction task, different machine learning models were generated and the results were compared with single compound predictions. PCs encoding promiscuity differences were found to contain more structure-promiscuity relationship information than sets of individual promiscuous compounds. In addition, feature analysis was carried out revealing key contributions to the correct prediction of PCs and non-PC MMPs via machine learning.