ArticleNature communications2024
Interformer: an interaction-aware model for protein-ligand docking and affinity prediction.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Article
- 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
- Elucidating the molecular mechanisms linking bisphenol A to breast cancer: an integrated study of bioinformatics, machine learning, and molecular docking.Naunyn-Schmiedeberg's archives of pharmacology · 2026Article
- Enhancing virtual screening of cystathionine β-synthase inhibitors: benchmarking target-specific machine-learning scoring functions against state-of-the-art AI docking and co-folding approaches.Journal of cheminformatics · 2026Article
- Deep generative models for 3D structure-based drug design and molecular optimisation: a comprehensive survey.Journal of computer-aided molecular design · 2026Review
- FlowDock: A unified flow-based framework for flexible protein-ligand docking and binding affinity prediction.Acta pharmaceutica Sinica. B · 2026Article
- PL-PatchSurfer3: improved structure-based virtual screening for structure variation using 3D Zernike descriptors.Journal of cheminformatics · 2026Article
- Leucettamine B and nacryline derivative promote the first steps of endochondral ossification in vitro.Scientific reports · 2026Article
- ArtiDock: Accurate Machine Learning Approach to Protein-Ligand Docking Optimized for High-Throughput Virtual Screening.Journal of chemical information and modeling · 2026Article
- Advancing the modernization of traditional Chinese medicine through artificial intelligence and multimodal data integration.Chinese medicine · 2026Review
- PLXFPred: interpretable cross-attention networks with hierarchical fusion of multi-modal features for predicting protein-ligand interactions and affinities.Bioinformatics (Oxford, England) · 2026Article
- Multimodal pre-training models of molecular representation for drug discovery.National science review · 2026Review
- Support field neural representation learner framework for learning stability landscapes in molecular geometry.npj biomedical innovations · 2025Article
- [Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2025Article
- The covalent docking software landscape: features and applications in drug design.Briefings in bioinformatics · 2025Review
- SIRT1 Activation by Lignans Identified via UPLC-qTOF-MS/MS-Based Metabolomic Profiling ofPlants (Basel, Switzerland) · 2025Article
- Molecular Docking as a Key Driver of Biocontrol for Agri-Food Security.Biotech (Basel (Switzerland)) · 2025Review
- GatorAffinity: Boosting Protein-Ligand Binding Affinity Prediction with Large-Scale Synthetic Structural Data.bioRxiv : the preprint server for biology · 2025Article
- Decoding the limits of deep learning in molecular docking for drug discovery.Chemical science · 2025Article
- MetaboGNN: predicting liver metabolic stability with graph neural networks and cross-species data.Journal of cheminformatics · 2025Article
Corrections and comments
- Erratum issued
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In recent years, the application of deep learning models to protein-ligand docking and affinity prediction, both vital for structure-based drug design, has garnered increasing interest. However, many of these models overlook the intricate modeling of interactions between ligand and protein atoms in the complex, consequently limiting their capacity for generalization and interpretability. In this work, we propose Interformer, a unified model built upon the Graph-Transformer architecture. The proposed model is designed to capture non-covalent interactions utilizing an interaction-aware mixture density network. Additionally, we introduce a negative sampling strategy, facilitating an effective correction of interaction distribution for affinity prediction. Experimental results on widely used and our in-house datasets demonstrate the effectiveness and universality of the proposed approach. Extensive analyses confirm our claim that our approach improves performance by accurately modeling specific protein-ligand interactions. Encouragingly, our approach advances docking tasks state-of-the-art (SOTA) performance.
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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.