ArticleBioinformatics (Oxford, England)2019
DeepAffinity: interpretable deep learning of compound-protein affinity through unified recurrent and convolutional neural networks.
Article in Bioinformatics (Oxford, England), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 192 papers, 2 of them syntheses that pooled it.
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Who cites it
192 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Knowledge mapping of graph neural networks for drug discovery: a bibliometric and visualized analysis.Frontiers in pharmacology · 2024Pooled it
- Protein Science Meets Artificial Intelligence: A Systematic Review and a Biochemical Meta-Analysis of an Inter-Field.Frontiers in bioengineering and biotechnology · 2022Pooled it
- Inhibition of MEKK3 Prevents Cerebral Cavernous Malformation Progression Via Restoring Endothelial Integrity and Restricting Microglial Activation.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- KAN-GIN: Adaptive Nonlinear Molecular Representation Learning for Drug-Target Affinity Prediction.Pharmaceuticals (Basel, Switzerland) · 2026Article
- AI-enhanced adaptive virtual screening of large libraries for ligand discovery.Nature biotechnology · 2026Article
- 3D Geometric Equivariant Graph Neural Networks with Contrastive Learning for Protein-Ligand Binding Affinity Prediction.Interdisciplinary sciences, computational life sciences · 2026Article
- Article
- Graph-based drug-target interaction modeling: from representation learning to output-driven drug discovery.Briefings in bioinformatics · 2026Review
- Bidirectional cross-modal fusion with tensor interaction for drug-target binding prediction.Scientific reports · 2026Article
- DTANet+: Dual Interaction and Kernel-Diverse Network for Drug-Target Affinity Prediction.Interdisciplinary sciences, computational life sciences · 2026Article
- CME-KGDTI: integrating clustered mutations into knowledge graph embedding for drug-target interaction prediction.BioData mining · 2026Article
- An electron-density point-cloud framework for robust protein-ligand interaction prediction.Nature communications · 2026Article
- Relevance of 3D Rotationally Equivariant Neural Networks for Predicting Protein-Ligand Binding Affinities.Interdisciplinary sciences, computational life sciences · 2026Article
- PRGNet: a Parallel Residual Graph Network for enhanced drug-target binding affinity prediction.BMC genomics · 2026Article
- Explainability Methods from Machine Learning Detect Important Drugs' Atoms in Drug-Target Interactions.Journal of chemical information and modeling · 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
- LightDTA: lightweight drug-target affinity prediction via random-walk network embedding and knowledge distillation.Molecular diversity · 2026Article
- MicroRNAs in oncology: a translational perspective in the era of AI.Nature reviews. Clinical oncology · 2026Review
- KansFormer network based on a multimodal cross-attention mechanism for drug-target interaction prediction.Molecular diversity · 2026Article
- AI-Enhanced Adaptive Virtual Screening Platform Enabling Exploration of 69 Billion Molecules Discovers Structurally Validated FSP1 Inhibitors.bioRxiv : the preprint server for biology · 2026Article
132 more citing papers are in PubMed but not listed here.
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Authors and funding
4 authors.
Funding
Abstract
motivationDrug discovery demands rapid quantification of compound-protein interaction (CPI). However, there is a lack of methods that can predict compound-protein affinity from sequences alone with high applicability, accuracy and interpretability.
resultsWe present a seamless integration of domain knowledges and learning-based approaches. Under novel representations of structurally annotated protein sequences, a semi-supervised deep learning model that unifies recurrent and convolutional neural networks has been proposed to exploit both unlabeled and labeled data, for jointly encoding molecular representations and predicting affinities. Our representations and models outperform conventional options in achieving relative error in IC50 within 5-fold for test cases and 20-fold for protein classes not included for training. Performances for new protein classes with few labeled data are further improved by transfer learning. Furthermore, separate and joint attention mechanisms are developed and embedded to our model to add to its interpretability, as illustrated in case studies for predicting and explaining selective drug-target interactions. Lastly, alternative representations using protein sequences or compound graphs and a unified RNN/GCNN-CNN model using graph CNN (GCNN) are also explored to reveal algorithmic challenges ahead. AVAILABILITY AND IMPLEMENTATION: Data and source codes are available at https://github.com/Shen-Lab/DeepAffinity. 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.