ArticleJournal of computer-aided molecular design2021
Prediction of activity cliffs on the basis of images using convolutional neural networks.
Article in Journal of computer-aided molecular design, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Activity-cliff awareness enables robust graph learning for molecular property prediction.Nature communications · 2026Article
- Accurate prediction of activity cliff compounds based on bioactivity profiles depends on assay nearest neighbor relationships.Journal of cheminformatics · 2026Article
- Deep learning model for activity cliffs prediction: a comprehensive approach to protein kinase inhibitors.Journal of computer-aided molecular design · 2025Article
- ACtriplet: An improved deep learning model for activity cliffs prediction by in tegrating triplet loss and pre-training.Journal of pharmaceutical analysis · 2025Article
- Enhancing Drug-Target Interaction Prediction through Transfer Learning from Activity Cliff Prediction Tasks.Journal of chemical information and modeling · 2025Article
- A data-driven generative strategy to avoid reward hacking in multi-objective molecular design.Nature communications · 2025Article
- OLB-AC: toward optimizing ligand bioactivities through deep graph learning and activity cliffs.Bioinformatics (Oxford, England) · 2024Article
- Exploring QSAR models for activity-cliff prediction.Journal of cheminformatics · 2023Article
- Large-scale prediction of activity cliffs using machine and deep learning methods of increasing complexity.Journal of cheminformatics · 2023Article
- Multi-task convolutional neural networks for predicting in vitro clearance endpoints from molecular images.Journal of computer-aided molecular design · 2022Article
- Interpretation of Ligand-Based Activity Cliff Prediction Models Using the Matched Molecular Pair Kernel.Molecules (Basel, Switzerland) · 2021Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
An activity cliff (AC) is formed by a pair of structurally similar compounds with a large difference in potency. Accordingly, ACs reveal structure-activity relationship (SAR) discontinuity and provide SAR information for compound optimization. Herein, we have investigated the question if ACs could be predicted from image data. Therefore, pairs of structural analogs were extracted from different compound activity classes that formed or did not form ACs. From these compound pairs, consistently formatted images were generated. Image sets were used to train and test convolutional neural network (CNN) models to systematically distinguish between ACs and non-ACs. The CNN models were found to predict ACs with overall high accuracy, as assessed using alternative performance measures, hence establishing proof-of-principle. Moreover, gradient weights from convolutional layers were mapped to test compounds and identified characteristic structural features that contributed to successful predictions. Weight-based feature visualization revealed the ability of CNN models to learn chemistry from images at a high level of resolution and aided in the interpretation of model decisions with intrinsic black box character.
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Registered trials
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