Automated detection of toxicophores and prediction of mutagenicity using PMCSFG algorithm

Leander Schietgat, Bertrand Cuissart, Kurt De Grave, Kyriakos Efthymiadis, Ronan Bureau, Bruno Crémilleux, Jan Ramon, Alban Lepailleur

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish
Article number2200232
JournalMolecular Informatics
Volume42
Issue number3
DOIs
Publication statusPublished - Mar 2023

Bibliographical note

Funding Information:
This work has been achieved thanks to the financial support of ERC Starting Grant 240186 “MiGraNT”, Research Fund KU Leuven, IWT (SBO Nemoa, SBO InSPECtor) and Région Normandie.

Publisher Copyright:
© 2023 Wiley-VCH GmbH.

Copyright:
Copyright 2023 Elsevier B.V., All rights reserved.

Keywords

  • machine learning
  • maximum common substructure
  • MCS
  • mutagenicitystructural alert
  • toxicophore

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