ArticleNature communications2023
Improving the generalizability of protein-ligand binding predictions with AI-Bind.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 76 papers.
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Who cites it
76 citing papers in PubMed.
- Computational data as the fuel for AI in chemistry.Chemical science · 2026Review
- A linear interpretable model for drug target prediction.Bioinformatics (Oxford, England) · 2026Article
- Cross-task interpretability through unified modeling reveals a universal shortcut bias in neoantigen prediction.Cell genomics · 2026Article
- Reliable evaluation and learning in multi-input biological association prediction.Briefings in bioinformatics · 2026Article
- Technology-enabled insights into SLC transporters in MAFLD: redefining the multi-hit pathogenesis and therapeutic landscape.Acta pharmacologica Sinica · 2026Review
- Enhancing molecular property prediction of transformer models with dual graph representation.Nature communications · 2026Article
- Facilitating structure-based drug discovery with an artificial intelligence-driven virtual screening platform.Nature protocols · 2026Review
- Structure-guided compound prioritization strategy for virtual screening identifies putative binders for the nuclear receptor LRH-1.bioRxiv : the preprint server for biology · 2026Article
- Article
- Reproducibility, validation, and failure modes across classical and AI-driven molecular docking.Journal of computer-aided molecular design · 2026Review
- The cocrystal advantage: overcoming polymorph patent barriers in generic drug development.Molecular diversity · 2026Review
- Review
- Assessing the Generalizability of Machine Learning and Physics-Based Methods with DNA-Encoded Libraries.bioRxiv : the preprint server for biology · 2026Article
- LiBRe: A Ligand-Aware Sequence-Based Binding Residue Prediction Model for Virtual Screening.Journal of chemical information and modeling · 2026Article
- A paired sequence language model for protein-protein interaction modeling.Nature communications · 2026Article
- Nature meets machine: the AI renaissance in natural product drug discovery.Natural products and bioprospecting · 2026Review
- Review
- One-hot news: drug synergy models shortcut molecular features.Bioinformatics (Oxford, England) · 2026Article
- Green Drug Discovery: Leveraging Biodiversity for Sustainable Pharmaceutical Solutions.Chemistry & biodiversity · 2026Review
- AI and organoid platforms for brain-targeted theranostics.Theranostics · 2026Review
16 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
Identifying novel drug-target interactions is a critical and rate-limiting step in drug discovery. While deep learning models have been proposed to accelerate the identification process, here we show that state-of-the-art models fail to generalize to novel (i.e., never-before-seen) structures. We unveil the mechanisms responsible for this shortcoming, demonstrating how models rely on shortcuts that leverage the topology of the protein-ligand bipartite network, rather than learning the node features. Here we introduce AI-Bind, a pipeline that combines network-based sampling strategies with unsupervised pre-training to improve binding predictions for novel proteins and ligands. We validate AI-Bind predictions via docking simulations and comparison with recent experimental evidence, and step up the process of interpreting machine learning prediction of protein-ligand binding by identifying potential active binding sites on the amino acid sequence. AI-Bind is a high-throughput approach to identify drug-target combinations with the potential of becoming a powerful tool in drug discovery.
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Registered trials
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