ArticleBMC bioinformatics2023
SubMDTA: drug target affinity prediction based on substructure extraction and multi-scale features.
Article in BMC bioinformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- MDCL: Multi-source Information Fusion and Collaborative Contrastive Learning for Inferring miRNA-Drug Sensitivity and Resistance Associations.Interdisciplinary sciences, computational life sciences · 2026Article
- ADMET-XSpec: A Tool for Systematic Cross-Species Data Integration in ADMET Prediction.Chemical research in toxicology · 2026Article
- A pre-trained language model-based cross-modal fusion framework for predicting miRNA-drug resistance and sensitivity associations.PLoS computational biology · 2026Article
- Structure-free drug-target affinity prediction using protein and molecule language models.Journal of cheminformatics · 2026Article
- Multi-view fusion based on graph convolutional network with attention mechanism for predicting miRNA related to drugs.PLoS computational biology · 2025Article
- SuperEdgeGO: Edge-supervised graph representation learning for enhanced protein function prediction.PLoS computational biology · 2025Article
- Enhancing molecular property prediction with quantized GNN models.Journal of cheminformatics · 2025Article
- Improving drug-target affinity prediction by adaptive self-supervised learning.PeerJ. Computer science · 2025Article
- Drug target affinity prediction based on multi-scale gated power graph and multi-head linear attention mechanism.PloS one · 2025Article
- MIPPIS: protein-protein interaction site prediction network with multi-information fusion.BMC bioinformatics · 2024Article
- Protein language models are performant in structure-free virtual screening.Briefings in bioinformatics · 2024Article
- MSH-DTI: multi-graph convolution with self-supervised embedding and heterogeneous aggregation for drug-target interaction prediction.BMC bioinformatics · 2024Article
- Drug-Online: an online platform for drug-target interaction, affinity, and binding sites identification using deep learning.BMC bioinformatics · 2024Article
- Triple Generative Self-Supervised Learning Method for Molecular Property Prediction.International journal of molecular sciences · 2024Article
- Drug-target affinity prediction with extended graph learning-convolutional networks.BMC bioinformatics · 2024Article
- Graph neural pre-training based drug-target affinity prediction.Frontiers in genetics · 2024Article
- A comprehensive review of the recent advances on predicting drug-target affinity based on deep learning.Frontiers in pharmacology · 2024Review
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
backgroundDrug-target affinity (DTA) prediction is a critical step in the field of drug discovery. In recent years, deep learning-based methods have emerged for DTA prediction. In order to solve the problem of fusion of substructure information of drug molecular graphs and utilize multi-scale information of protein, a self-supervised pre-training model based on substructure extraction and multi-scale features is proposed in this paper.
resultsFor drug molecules, the model obtains substructure information through the method of probability matrix, and the contrastive learning method is implemented on the graph-level representation and subgraph-level representation to pre-train the graph encoder for downstream tasks. For targets, a BiLSTM method that integrates multi-scale features is used to capture long-distance relationships in the amino acid sequence. The experimental results showed that our model achieved better performance for DTA prediction.
conclusionsThe proposed model improves the performance of the DTA prediction, which provides a novel strategy based on substructure extraction and multi-scale features.
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