ArticleJournal of chemical information and modeling2015
Strong nonadditivity as a key structure-activity relationship feature: distinguishing structural changes from assay artifacts.
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 19 papers.
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Who cites it
19 citing papers in PubMed.
- Machine Learning-Guided Repositioning of a SARS-CoV-2-Targeting Molecular Series as Cruzain Inhibitors.ChemMedChem · 2026Article
- From mundane to surprising nonadditivity: drivers and impact on ML models.Journal of computer-aided molecular design · 2024Article
- Dipeptide-Derived Alkynes as Potent and Selective Irreversible Inhibitors of Cysteine Cathepsins.Journal of medicinal chemistry · 2023Article
- Critical Review of Plant-Derived Compounds as Possible Inhibitors of SARS-CoV-2 Proteases: A Comparison with Experimentally Validated Molecules.ACS omega · 2022Review
- Implications of Additivity and Nonadditivity for Machine Learning and Deep Learning Models in Drug Design.ACS omega · 2022Article
- Efficient Hit-to-Lead Searching of Kinase Inhibitor Chemical Space via Computational Fragment Merging.Journal of chemical information and modeling · 2021Article
- Nonadditivity in public and inhouse data: implications for drug design.Journal of cheminformatics · 2021Article
- Comparison of structure- and ligand-based scoring functions for deep generative models: a GPCR case study.Journal of cheminformatics · 2021Article
- Cytotoxic and Antifungal Amides Derived from Ferulic Acid: Molecular Docking and Mechanism of Action.BioMed research international · 2021Article
- Allosteric Mechanisms of Nonadditive Substituent Contributions to Protein-Ligand Binding.Biophysical journal · 2020Article
- Mapping the S1 and S1' subsites of cysteine proteases with new dipeptidyl nitrile inhibitors as trypanocidal agents.PLoS neglected tropical diseases · 2020Article
- The nature of ligand efficiency.Journal of cheminformatics · 2019Article
- Decision Making in Medicinal Chemistry: The Power of Our Intuition.ACS medicinal chemistry letters · 2018Article
- Automating drug discovery.Nature reviews. Drug discovery · 2018Review
- The use of matched molecular series networks for cross target structure activity relationship translation and potency prediction.MedChemComm · 2017Article
- Binding Pose Flip Explained via Enthalpic and Entropic Contributions.Journal of chemical information and modeling · 2017Article
- Matched Molecular Pair Analysis in Short: Algorithms, Applications and Limitations.Computational and structural biotechnology journal · 2017Review
- Kinetic barriers in the isomerization of substituted ureas: implications for computer-aided drug design.Journal of computer-aided molecular design · 2016Article
- Matched Peptides: Tuning Matched Molecular Pair Analysis for Biopharmaceutical Applications.Journal of chemical information and modeling · 2015Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Nonadditivity in protein-ligand affinity data represents highly instructive structure-activity relationship (SAR) features that indicate structural changes and have the potential to guide rational drug design. At the same time, nonadditivity is a challenge for both basic SAR analysis as well as many ligand-based data analysis techniques such as Free-Wilson Analysis and Matched Molecular Pair analysis, since linear substituent contribution models inherently assume additivity and thus do not work in such cases. While structural causes for nonadditivity have been analyzed anecdotally, no systematic approaches to interpret and use nonadditivity prospectively have been developed yet. In this contribution, we lay the statistical framework for systematic analysis of nonadditivity in a SAR series. First, we develop a general metric to quantify nonadditivity. Then, we demonstrate the non-negligible impact of experimental uncertainty that creates apparent nonadditivity, and we introduce techniques to handle experimental uncertainty. Finally, we analyze public SAR data sets for strong nonadditivity and use recourse to the original publications and available X-ray structures to find structural explanations for the nonadditivity observed. We find that all cases of strong nonadditivity (ΔΔpKi and ΔΔpIC50 > 2.0 log units) with sufficient structural information to generate reasonable hypothesis involve changes in binding mode. With the appropriate statistical basis, nonadditivity analysis offers a variety of new attempts for various areas in computer-aided drug design, including the validation of scoring functions and free energy perturbation approaches, binding pocket classification, and novel features in SAR analysis tools.
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