ArticleJournal of computer-aided molecular design2020
Advances in exploring activity cliffs.
Article in Journal of computer-aided molecular design, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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Who cites it
29 citing papers in PubMed.
- Robots and Minimal, Physics-Informed Features: A Hybrid Framework for Enzyme Catalysis.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- A survey of transformer and LLM-based architectures, workflows, and systems in drug discovery.Journal of computer-aided molecular design · 2026Review
- Smiles-based bioactivity prediction through molecular encoder selection and data augmentation.Journal of cheminformatics · 2026Article
- How far can you go? Extrapolating values of catalytic activity from known protein landscapes in natural and directed evolution.Chemical Society reviews · 2026Review
- De novo design of anticancer 4-thiazolidinone derivatives: a generative framework shaped by activity cliffs.Journal of cheminformatics · 2026Article
- AMPCliff: Quantitative definition and benchmarking of activity cliffs in antimicrobial peptides.Journal of advanced research · 2026Article
- Deep learning model for activity cliffs prediction: a comprehensive approach to protein kinase inhibitors.Journal of computer-aided molecular design · 2025Article
- MaskMol: knowledge-guided molecular image pre-training framework for activity cliffs with pixel masking.BMC biology · 2025Article
- Enhancing Drug-Target Interaction Prediction through Transfer Learning from Activity Cliff Prediction Tasks.Journal of chemical information and modeling · 2025Article
- GraphGIM: rethinking molecular graph contrastive learning via geometry image modeling.BMC biology · 2025Article
- iCliff Taylor's Version: Robust and Efficient Activity Cliff Determination.Journal of chemical information and modeling · 2025Article
- The Quasi-Bound State as a Predictor of Relative Binding Free Energy.Journal of chemical information and modeling · 2025Article
- Cheminformatics Approaches to the Analysis of Additives for Sustainable Polymeric Materials.Polymers · 2025Article
- Article
- Molecular similarity: Theory, applications, and perspectives.Artificial intelligence chemistry · 2024Article
- Molecular property prediction based on graph structure learning.Bioinformatics (Oxford, England) · 2024Article
- Protein characteristics substantially influence the propensity of activity cliffs among kinase inhibitors.Scientific reports · 2024Article
- The pursuit of accurate predictive models of the bioactivity of small molecules.Chemical science · 2024Review
- RECOVER identifies synergistic drug combinations in vitro through sequential model optimization.Cell reports methods · 2023Article
- A benchmark dataset for machine learning in ecotoxicology.Scientific data · 2023Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The activity cliff (AC) concept is of comparable relevance for medicinal chemistry and chemoinformatics. An AC is defined as a pair of structurally similar compounds with a large potency difference against a given target. In medicinal chemistry, ACs are of interest because they reveal small chemical changes with large potency effects, a concept referred to as structure-activity relationship (SAR) discontinuity. Computationally, ACs can be systematically identified, going far beyond individual compound series considered during lead optimization. Large-scale analysis of ACs has revealed characteristic features across many different compound activity classes. The way in which the molecular similarity and potency difference criteria have been addressed for defining ACs distinguishes between different generations of ACs and mirrors the evolution of the AC concept. We discuss different stages of this evolutionary path and highlight recent advances in AC research.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.