ArticleJournal of chemical information and modeling2015
Structure-based predictions of activity cliffs.
Article in Journal of chemical information and modeling, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- 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
- Activity cliff-aware reinforcement learning for de novo drug design.Journal of cheminformatics · 2025Article
- A data-driven generative strategy to avoid reward hacking in multi-objective molecular design.Nature communications · 2025Article
- 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
- Development and evaluation of a java-based deep neural network method for drug response predictions.Frontiers in artificial intelligence · 2023Article
- Exposing the Limitations of Molecular Machine Learning with Activity Cliffs.Journal of chemical information and modeling · 2022Article
- Discovery and SAR Evolution of Pyrazole Azabicyclo[3.2.1]octane Sulfonamides as a Novel Class of Non-CovalentJournal of medicinal chemistry · 2021Article
- Advances in exploring activity cliffs.Journal of computer-aided molecular design · 2020Article
- Introducing a new category of activity cliffs combining different compound similarity criteria.RSC medicinal chemistry · 2020Article
- Macrocycle modeling in ICM: benchmarking and evaluation in D3R Grand Challenge 4.Journal of computer-aided molecular design · 2019Article
- Evolving Concept of Activity Cliffs.ACS omega · 2019Review
- Ligand-biased ensemble receptor docking (LigBEnD): a hybrid ligand/receptor structure-based approach.Journal of computer-aided molecular design · 2018Article
- Matched Molecular Pair Analysis in Short: Algorithms, Applications and Limitations.Computational and structural biotechnology journal · 2017Review
- Historeceptomic Fingerprints for Drug-Like Compounds.Frontiers in physiology · 2015Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
In drug discovery, it is generally accepted that neighboring molecules in a given descriptor's space display similar activities. However, even in regions that provide strong predictability, structurally similar molecules can occasionally display large differences in potency. In QSAR jargon, these discontinuities in the activity landscape are known as "activity cliffs". In this study, we assessed the reliability of ligand docking and virtual ligand screening schemes in predicting activity cliffs. We performed our calculations on a diverse, independently collected database of cliff-forming cocrystals. Starting from ideal situations, which allowed us to establish our baseline, we progressively moved toward simulating more realistic scenarios. Ensemble- and template-docking achieved a significant level of accuracy, suggesting that, despite the well-known limitations of empirical scoring schemes, activity cliffs can be accurately predicted by advanced structure-based methods.
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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.