ArticleBioinformatics (Oxford, England)2024
Surface-based multimodal protein-ligand binding affinity prediction.
Article in Bioinformatics (Oxford, England), 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.
- 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
- Structure-agnostic protein-ligand binding affinity prediction via hierarchical representation alignment.Bioinformatics (Oxford, England) · 2026Article
- Deep Contrastive Learning for High-Throughput Prediction of Drug Resistance Mutations from Sequences.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Critical Artifacts Improve Reproducibility of Protein-Ligand Binding Affinity Prediction Models on CASF-2016.Journal of chemical information and modeling · 2026Article
- Robustness of Protein-Ligand Binding Affinity Prediction Models to Docked and Predicted Structures.Journal of chemical information and modeling · 2026Article
- DVNDTA: a dual virtual node based heterogeneous interaction model for accurate prediction of drug-target affinity.BMC bioinformatics · 2026Article
- IHDFN-DTI: Interpretable Hybrid Deep Feature Fusion Network for Drug-Target Interaction Prediction.Interdisciplinary sciences, computational life sciences · 2026Article
- Pocket-Surface Discrete Differential Geometry as a Leakage-Robust Feature Class for Protein-Ligand Binding Affinity Prediction.Molecules (Basel, Switzerland) · 2026Article
- Advances in computational prediction of RNA-small molecule binding affinity.Journal of computer-aided molecular design · 2026Review
- Multimodal graph, surface, and language-based model for protein protein interaction prediction.Scientific reports · 2026Article
- SurfFold: a unified model for protein inverse folding by integrating surface and structural information.Bioinformatics (Oxford, England) · 2026Article
- Prediction of protein-protein interactions using point transformer and spherical Convex Hull graphs.Computational and structural biotechnology journal · 2026Article
- PLiSAGE: enhancing protein-ligand interaction prediction with multimodal surface and geometry encoding.Bioinformatics (Oxford, England) · 2025Article
- EM-PLA: environment-aware heterogeneous graph-based multimodal protein-ligand binding affinity prediction.Bioinformatics (Oxford, England) · 2025Article
- GNNSeq: A Sequence-Based Graph Neural Network for Predicting Protein-Ligand Binding Affinity.Pharmaceuticals (Basel, Switzerland) · 2025Article
- MM-DRPNet: A multimodal dynamic radial partitioning network for enhanced protein-ligand binding affinity prediction.Computational and structural biotechnology journal · 2024Article
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Authors and funding
8 authors.
Funding
Abstract
motivationIn the field of drug discovery, accurately and effectively predicting the binding affinity between proteins and ligands is crucial for drug screening and optimization. However, current research primarily utilizes representations based on sequence or structure to predict protein-ligand binding affinity, with relatively less study on protein surface information, which is crucial for protein-ligand interactions. Moreover, when dealing with multimodal information of proteins, traditional approaches typically concatenate features from different modalities in a straightforward manner without considering the heterogeneity among them, which results in an inability to effectively exploit the complementary between modalities.
resultsWe introduce a novel multimodal feature extraction (MFE) framework that, for the first time, incorporates information from protein surfaces, 3D structures, and sequences, and uses cross-attention mechanism for feature alignment between different modalities. Experimental results show that our method achieves state-of-the-art performance in predicting protein-ligand binding affinity. Furthermore, we conduct ablation studies that demonstrate the effectiveness and necessity of protein surface information and multimodal feature alignment within the framework. AVAILABILITY AND IMPLEMENTATION: The source code and data are available at https://github.com/Sultans0fSwing/MFE.
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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.