ArticleBioinformatics (Oxford, England)2024
DEAttentionDTA: protein-ligand binding affinity prediction based on dynamic embedding and self-attention.
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 9 papers.
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9 citing papers in PubMed.
- Structure-agnostic protein-ligand binding affinity prediction via hierarchical representation alignment.Bioinformatics (Oxford, England) · 2026Article
- Critical Artifacts Improve Reproducibility of Protein-Ligand Binding Affinity Prediction Models on CASF-2016.Journal of chemical information and modeling · 2026Article
- Structural Knowledge Is What Matters in Protein-Ligand Binding Affinity Prediction.Molecules (Basel, Switzerland) · 2026Article
- IHDFN-DTI: Interpretable Hybrid Deep Feature Fusion Network for Drug-Target Interaction Prediction.Interdisciplinary sciences, computational life sciences · 2026Article
- DynamicDTA: Drug-Target Binding Affinity Prediction Using Dynamic Descriptors and Graph Representation.Interdisciplinary sciences, computational life sciences · 2026Article
- Advances in computational prediction of RNA-small molecule binding affinity.Journal of computer-aided molecular design · 2026Review
- PLXFPred: interpretable cross-attention networks with hierarchical fusion of multi-modal features for predicting protein-ligand interactions and affinities.Bioinformatics (Oxford, England) · 2026Article
- Recent trends in machine learning and deep learning-based prediction of G-protein coupled receptor-ligand binding affinities.Frontiers in bioinformatics · 2025Review
- Improving drug-target affinity prediction by adaptive self-supervised learning.PeerJ. Computer science · 2025Article
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9 authors.
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Abstract
motivationPredicting protein-ligand binding affinity is crucial in new drug discovery and development. However, most existing models rely on acquiring 3D structures of elusive proteins. Combining amino acid sequences with ligand sequences and better highlighting active sites are also significant challenges.
resultsWe propose an innovative neural network model called DEAttentionDTA, based on dynamic word embeddings and a self-attention mechanism, for predicting protein-ligand binding affinity. DEAttentionDTA takes the 1D sequence information of proteins as input, including the global sequence features of amino acids, local features of the active pocket site, and linear representation information of the ligand molecule in the SMILE format. These three linear sequences are fed into a dynamic word-embedding layer based on a 1D convolutional neural network for embedding encoding and are correlated through a self-attention mechanism. The output affinity prediction values are generated using a linear layer. We compared DEAttentionDTA with various mainstream tools and achieved significantly superior results on the same dataset. We then assessed the performance of this model in the p38 protein family. AVAILABILITY AND IMPLEMENTATION: The resource codes are available at https://github.com/whatamazing1/DEAttentionDTA.
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