ArticleRSC advances2024
Exploring protein-ligand binding affinity prediction with electron density-based geometric deep learning.
Article in RSC advances, 2024. 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.
- BONAFIDE: a Python framework for the calculation of local features for atoms and bonds in molecules.Journal of cheminformatics · 2026Article
- An electron-density point-cloud framework for robust protein-ligand interaction prediction.Nature communications · 2026Article
- AI accelerate the identification of druggable targets by 3D structures of proteins and compounds.NPJ precision oncology · 2026Review
- Expediting hit-to-lead progression in drug discovery through reaction prediction and multi-dimensional optimization.Nature communications · 2025Article
- Computer-Aided Drug Design Across Breast Cancer Subtypes: Methods, Applications and Translational Outlook.International journal of molecular sciences · 2025Review
- LAGNet: better electron density prediction for LCAO-based data and drug-like substances.Journal of cheminformatics · 2025Article
- The physics-AI dialogue in drug design.RSC medicinal chemistry · 2025Review
- Molecular dynamics, docking and quantum calculations reveal conformational changes influenced by CYP271A amino acid mutations related to cerebrotendinous xanthomatosis.Scientific reports · 2025Article
- Navigating structure-based drug discovery with emerging innovations in physics- and knowledge-based approaches.npj drug discovery · 2025Review
- Simple User-Friendly Reaction Format.Molecular informatics · 2025Article
- G-Computational and structural biotechnology journal · 2024Article
- CombiningRSC advances · 2024Article
- Geometric deep learning-guided Suzuki reaction conditions assessment for applications in medicinal chemistry.RSC medicinal chemistry · 2024Article
- Prospective de novo drug design with deep interactome learning.Nature communications · 2024Article
- Identifying opportunities for late-stage C-H alkylation with high-throughput experimentation and in silico reaction screening.Communications chemistry · 2023Article
- The Impact of Data on Structure-Based Binding Affinity Predictions Using Deep Neural Networks.International journal of molecular sciences · 2023Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Rational structure-based drug design relies on accurate predictions of protein-ligand binding affinity from structural molecular information. Although deep learning-based methods for predicting binding affinity have shown promise in computational drug design, certain approaches have faced criticism for their potential to inadequately capture the fundamental physical interactions between ligands and their macromolecular targets or for being susceptible to dataset biases. Herein, we propose to include bond-critical points based on the electron density of a protein-ligand complex as a fundamental physical representation of protein-ligand interactions. Employing a geometric deep learning model, we explore the usefulness of these bond-critical points to predict absolute binding affinities of protein-ligand complexes, benchmark model performance against existing methods, and provide a critical analysis of this new approach. The models achieved root-mean-squared errors of 1.4-1.8 log units on the PDBbind dataset, and 1.0-1.7 log units on the PDE10A dataset, not indicating significant advantages over benchmark methods, and thus rendering the utility of electron density for deep learning models context-dependent. The relationship between intermolecular electron density and corresponding binding affinity was analyzed, and Pearson correlation coefficients
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Registered trials
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