ArticleJournal of chemical information and modeling2024
MDF-DTA: A Multi-Dimensional Fusion Approach for Drug-Target Binding Affinity Prediction.
Article in Journal of chemical information and modeling, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- GraESM-FuseDTA: adaptive gated multimodal fusion of graph neural networks and protein language models for robust drug-target affinity prediction.Naunyn-Schmiedeberg's archives of pharmacology · 2026Article
- Bidirectional cross-modal fusion with tensor interaction for drug-target binding prediction.Scientific reports · 2026Article
- IHDFN-DTI: Interpretable Hybrid Deep Feature Fusion Network for Drug-Target Interaction Prediction.Interdisciplinary sciences, computational life sciences · 2026Article
- A multimodal human protein embeddings database: DeepDrug Protein Embeddings Bank (DPEB).NAR genomics and bioinformatics · 2026Article
- LightDTA: lightweight drug-target affinity prediction via random-walk network embedding and knowledge distillation.Molecular diversity · 2026Article
- Predicting drug-target interactions and binding affinity using an optimized deep learning approach.Journal of computer-aided molecular design · 2026Article
- Learnable protein representations in computational biology for predicting drug-target affinity.Journal of cheminformatics · 2026Review
- Review
- DTBA-net: Drug-Target Binding Affinity prediction using feature selection in hybrid CNN model.Journal of computer-aided molecular design · 2025Article
- Improving Identification of Drug-Target Binding Sites Based on Structures of Targets Using Residual Graph Transformer Network.Biomolecules · 2025Article
- PocketDTA: an advanced multimodal architecture for enhanced prediction of drug-target affinity from 3D structural data of target binding pockets.Bioinformatics (Oxford, England) · 2024Article
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Authors and funding
4 authors.
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Abstract
Drug-target affinity (DTA) prediction is an important task in the early stages of drug discovery. Traditional biological approaches are time-consuming, effort-consuming, and resource-consuming due to the large size of genomic and chemical spaces. Computational approaches using machine learning have emerged to narrow down the drug candidate search space. However, most of these prediction models focus on single feature encoding of drugs and targets, ignoring the importance of integrating different dimensions of these features. We propose a deep learning-based approach called Multi-Dimensional Fusion for Drug Target Affinity Prediction (MDF-DTA) incorporating different dimensional features. Our model fuses 1D, 2D, and 3D representations obtained from different pretrained models for both drugs and targets. We evaluated MDF-DTA on two standard benchmark data sets: DAVIS and KIBA. Experimental results show that MDF-DTA outperforms many state-of-the-art techniques in the DTA task across both data sets. Through ablation studies and performance evaluation metrics, we evaluate the importance of individual representations and the impact of each representation on MDF-DTA.
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