Automated detection of toxicophores and prediction of mutagenicity using PMCSFG algorithm

• Published in: Molecular informatics Vol. 42; no. 3; pp. e2200232 - n/a
• Main Authors: Schietgat, Leander, Cuissart, Bertrand, De Grave, Kurt, Efthymiadis, Kyriakos, Bureau, Ronan, Crémilleux, Bruno, Ramon, Jan, Lepailleur, Alban
• Format: Journal Article
• Language: English
• Published: Germany Wiley Subscription Services, Inc 01-03-2023; Wiley-VCH
• Subjects: Algorithms; Chemical fingerprinting; Chemical Sciences; Cheminformatics; Machine Learning; maximum common substructure; MCS; Mutagenesis; Mutagenicity; mutagenicitystructural alert; Mutagens - chemistry; Performance prediction; Statistics; toxicophore; mutagenicitystructural alert; toxicophore; MCS; machine learning; maximum common substructure; structural alert
• ISSN: 1868-1743; 1868-1751
• DOI: 10.1002/minf.202200232

Maximum common substructures (MCS) have received a lot of attention in the chemoinformatics community. They are typically used as a similarity measure between molecules, showing high predictive performance when used in classification tasks, while being easily explainable substructures. In the present work, we applied the Pairwise Maximum Common Subgraph Feature Generation (PMCSFG) algorithm to automatically detect toxicophores (structural alerts) and to compute fingerprints based on MCS. We present a comparison between our MCS‐based fingerprints and 12 well‐known chemical fingerprints when used as features in machine learning models. We provide an experimental evaluation and discuss the usefulness of the different methods on mutagenicity data. The features generated by the MCS method have a state‐of‐the‐art performance when predicting mutagenicity, while they are more interpretable than the traditional chemical fingerprints.