ArticleBioinformatics (Oxford, England)2018
Development and evaluation of a deep learning model for protein-ligand binding affinity prediction.
Article in Bioinformatics (Oxford, England), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 242 papers.
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Who cites it
242 citing papers in PubMed.
- Target discovery and drug design in the era of artificial intelligence.Medicinal chemistry research : an international journal for rapid communications on design and mechanisms of action of biologically active agents · 2026Review
- 3D Geometric Equivariant Graph Neural Networks with Contrastive Learning for Protein-Ligand Binding Affinity Prediction.Interdisciplinary sciences, computational life sciences · 2026Article
- UniPept: A Universal Model for Predicting Peptide Properties Based on Atom-Residue Dual-Scale Integration.Interdisciplinary sciences, computational life sciences · 2026Article
- A Unified Hierarchical Multiscale Fusion Framework for Drug-Target Affinity Prediction: From Benchmark Performance to Nanomolar Inhibitor Discovery.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- GAMT-GINE: A Graph Isomorphism Network Integrating Continuous Spatial Awareness and Multi-Task Learning for Protein-Ligand Binding Affinity Prediction.International journal of molecular sciences · 2026Article
- Edge-aware GAT-based protein binding sites prediction.PLoS computational biology · 2026Article
- AlphaDTA: integrating AlphaFold3 embeddings and 3D complex structures for drug-target binding affinity prediction.Journal of cheminformatics · 2026Article
- Synthesis and machine learning techniques to enable data-driven investigation of supramolecular host-guest interactions.Chemical science · 2026Article
- FlowDock: A unified flow-based framework for flexible protein-ligand docking and binding affinity prediction.Acta pharmaceutica Sinica. B · 2026Article
- Deep learning for small-molecule drug discovery: From molecular design to clinical translation.Journal of pharmaceutical analysis · 2026Review
- DVNDTA: a dual virtual node based heterogeneous interaction model for accurate prediction of drug-target affinity.BMC bioinformatics · 2026Article
- Structural Knowledge Is What Matters in Protein-Ligand Binding Affinity Prediction.Molecules (Basel, Switzerland) · 2026Article
- Pocket-Surface Discrete Differential Geometry as a Leakage-Robust Feature Class for Protein-Ligand Binding Affinity Prediction.Molecules (Basel, Switzerland) · 2026Article
- DynamicDTA: Drug-Target Binding Affinity Prediction Using Dynamic Descriptors and Graph Representation.Interdisciplinary sciences, computational life sciences · 2026Article
- Sequence-based drug-target binding site pre-training enables cryptic pocket detection and improves binding affinity and kinetics prediction.Journal of cheminformatics · 2026Article
- Sequence-based Drug-Target Binding Site Pretraining Enables Cryptic Pocket Detection and Improves Binding Affinity and Kinetics Prediction.bioRxiv : the preprint server for biology · 2026Article
- GRIPHIN: grids of pharmacophore interaction fields for affinity prediction.Journal of cheminformatics · 2026Article
- AI-Driven Secondary Immunomodulatory Effects of Conventional Drugs on Patient-Derived Macrophages.International journal of molecular sciences · 2026Article
- [A protein-ligand binding affinity prediction model integrating multi-scale interaction features].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2026Article
- BA-Pred and RMSD-Pred: Integrated Graph Neural Network Models for Accurate Protein-Ligand Binding Affinity and Binding Pose Prediction.Journal of chemical information and modeling · 2026Article
182 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Motivation: Structure based ligand discovery is one of the most successful approaches for augmenting the drug discovery process. Currently, there is a notable shift towards machine learning (ML) methodologies to aid such procedures. Deep learning has recently gained considerable attention as it allows the model to 'learn' to extract features that are relevant for the task at hand. Results: We have developed a novel deep neural network estimating the binding affinity of ligand-receptor complexes. The complex is represented with a 3D grid, and the model utilizes a 3D convolution to produce a feature map of this representation, treating the atoms of both proteins and ligands in the same manner. Our network was tested on the CASF-2013 'scoring power' benchmark and Astex Diverse Set and outperformed classical scoring functions. Availability and implementation: The model, together with usage instructions and examples, is available as a git repository at http://gitlab.com/cheminfIBB/pafnucy. Supplementary information: Supplementary data are available at Bioinformatics online.
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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.