1. Exploring QSAR models for activity-cliff prediction
- Author
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Dablander, M, Hanser, T, Lambiotte, R, and Morris, GM
- Subjects
FOS: Computer and information sciences ,Computer Science - Machine Learning ,Quantitative Biology - Biomolecules ,Statistics - Machine Learning ,FOS: Biological sciences ,Biomolecules (q-bio.BM) ,Machine Learning (stat.ML) ,Library and Information Sciences ,Physical and Theoretical Chemistry ,Computer Graphics and Computer-Aided Design ,Machine Learning (cs.LG) ,Computer Science Applications - Abstract
Pairs of similar compounds that only differ by a small structural modification but exhibit a large difference in their binding affinity for a given target are known as activity cliffs (ACs). It has been hypothesised that quantitative structure-activity relationship (QSAR) models struggle to predict ACs and that ACs thus form a major source of prediction error. However, a study to explore the AC-prediction power of modern QSAR methods and its relationship to general QSAR-prediction performance is lacking. We systematically construct nine distinct QSAR models by combining three molecular representation methods (extended-connectivity fingerprints, physicochemical-descriptor vectors and graph isomorphism networks) with three regression techniques (random forests, k-nearest neighbours and multilayer perceptrons); we then use each resulting model to classify pairs of similar compounds as ACs or non-ACs and to predict the activities of individual molecules in three case studies: dopamine receptor D2, factor Xa, and SARS-CoV-2 main protease. We observe low AC-sensitivity amongst the tested models when the activities of both compounds are unknown, but a substantial increase in AC-sensitivity when the actual activity of one of the compounds is given. Graph isomorphism features are found to be competitive with or superior to classical molecular representations for AC-classification and can thus be employed as baseline AC-prediction models or simple compound-optimisation tools. For general QSAR-prediction, however, extended-connectivity fingerprints still consistently deliver the best performance. Our results provide strong support for the hypothesis that indeed QSAR methods frequently fail to predict ACs. We propose twin-network training for deep learning models as a potential future pathway to increase AC-sensitivity and thus overall QSAR performance., Submitted to Journal of Cheminformatics
- Published
- 2023
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