ArticleJournal of chemical information and modeling2014
Does a more precise chemical description of protein-ligand complexes lead to more accurate prediction of binding affinity?
Article in Journal of chemical information and modeling, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 67 papers.
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Who cites it
67 citing papers in PubMed.
- Systematic evaluation of computational tools to predict the effects of mutations on protein-ligand binding affinity in the absence of experimental structures.Briefings in bioinformatics · 2026Article
- Boosting Drug Discovery: Expanding the Applicability of Fragment Dissolved Molecular Dynamics to Accelerate Binding Mode Elucidation.Journal of chemical information and modeling · 2025Article
- Unveiling Fluorescence Spectroscopy, Molecular Docking and Dynamic Simulations: Interactions Between Protein and 2, 4-Dinitrophenylhydrazine Schiff Base.Journal of fluorescence · 2025Article
- SPLIF-Enhanced Attention-Driven 3D CNNs for Precise and Reliable Protein-Ligand Interaction Modeling for METTL3.ACS omega · 2025Article
- Robustly interrogating machine learning-based scoring functions: what are they learning?Bioinformatics (Oxford, England) · 2025Article
- The algebraic extended atom-type graph-based model for precise ligand-receptor binding affinity prediction.Journal of cheminformatics · 2025Article
- Identification of Potential Selective PAK4 Inhibitors Through Shape and Protein Conformation Ensemble Screening and Electrostatic-Surface-Matching Optimization.Current issues in molecular biology · 2025Article
- Inactive-enriched machine-learning models exploiting patent data improve structure-based virtual screening for PDL1 dimerizers.Journal of advanced research · 2025Article
- Docking-Informed Machine Learning for Kinome-wide Affinity Prediction.Journal of chemical information and modeling · 2024Article
- Comprehensive machine learning boosts structure-based virtual screening for PARP1 inhibitors.Journal of cheminformatics · 2024Article
- Machine Learning Methods as a Cost-Effective Alternative to Physics-Based Binding Free Energy Calculations.Molecules (Basel, Switzerland) · 2024Article
- SQM2.20: Semiempirical quantum-mechanical scoring function yields DFT-quality protein-ligand binding affinity predictions in minutes.Nature communications · 2024Article
- Exploring protein-ligand binding affinity prediction with electron density-based geometric deep learning.RSC advances · 2024Article
- Exploring Scoring Function Space: Developing Computational Models for Drug Discovery.Current medicinal chemistry · 2024Review
- An overview of recent advances and challenges in predicting compound-protein interaction (CPI).Medical review (2021) · 2023Review
- A practical guide to machine-learning scoring for structure-based virtual screening.Nature protocols · 2023Review
- A generalized protein-ligand scoring framework with balanced scoring, docking, ranking and screening powers.Chemical science · 2023Article
- Article
- DyScore: A Boosting Scoring Method with Dynamic Properties for Identifying True Binders and Nonbinders in Structure-Based Drug Discovery.Journal of chemical information and modeling · 2022Article
- Machine intelligence-driven framework for optimized hit selection in virtual screening.Journal of cheminformatics · 2022Article
7 more citing papers are in PubMed but not listed here.
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Authors and funding
3 authors.
Funding
Abstract
Predicting the binding affinities of large sets of diverse molecules against a range of macromolecular targets is an extremely challenging task. The scoring functions that attempt such computational prediction are essential for exploiting and analyzing the outputs of docking, which is in turn an important tool in problems such as structure-based drug design. Classical scoring functions assume a predetermined theory-inspired functional form for the relationship between the variables that describe an experimentally determined or modeled structure of a protein-ligand complex and its binding affinity. The inherent problem of this approach is in the difficulty of explicitly modeling the various contributions of intermolecular interactions to binding affinity. New scoring functions based on machine-learning regression models, which are able to exploit effectively much larger amounts of experimental data and circumvent the need for a predetermined functional form, have already been shown to outperform a broad range of state-of-the-art scoring functions in a widely used benchmark. Here, we investigate the impact of the chemical description of the complex on the predictive power of the resulting scoring function using a systematic battery of numerical experiments. The latter resulted in the most accurate scoring function to date on the benchmark. Strikingly, we also found that a more precise chemical description of the protein-ligand complex does not generally lead to a more accurate prediction of binding affinity. We discuss four factors that may contribute to this result: modeling assumptions, codependence of representation and regression, data restricted to the bound state, and conformational heterogeneity in data.
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