Prediction of Different Classes of Promiscuous and Nonpromiscuous Compounds Using Machine Learning and Nearest Neighbor Analysis

Abstract

The ability of compounds to interact with multiple targets is also referred to as promiscuity. Multitarget activity of pharmaceutically relevant compounds provides the foundation of polypharmacology. Promiscuity cliffs (PCs) were introduced as a data structure to identify and organize similar compounds with large differences in promiscuity. Many PCs were obtained on the basis of biological screening data or compound activity data from medicinal chemistry. In this work, PCs were used as a source of different classes of promiscuous and nonpromiscuous compounds with close structural relationships. Various machine learning models were built to distinguish between promiscuous and nonpromiscuous compounds, yielding overall successful predictions. Analysis of nearest neighbor relationships between training and test compounds were found to rival machine learning, indicating the presence of promiscuity-relevant structural features, as further supported by feature weighting and mapping. Thus, although origins of promiscuity remain to be fully understood at the molecular level of detail, our study provides evidence for the presence of structure-promiscuity relationships that can be detected computationally and further explored.