ArticleBiomolecules2021
MCN-CPI: Multiscale Convolutional Network for Compound-Protein Interaction Prediction.
Article in Biomolecules, 2021. 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.
- MFDR-DTI: Multisource Feature Decoupling and Dual-Level Reorganization Fusion for Accurate Drug-Target Interaction Prediction.ACS omega · 2026Article
- In silico methods for drug-target interaction prediction.Cell reports methods · 2025Review
- Bioactivity Deep Learning for Complex Structure-Free Compound-Protein Interaction Prediction.Journal of chemical information and modeling · 2025Article
- SaeGraphDTI: drug-target interaction prediction based on sequence attribute extraction and graph neural network.BMC bioinformatics · 2025Article
- Improving drug response prediction via integrating gene relationships with deep learning.Briefings in bioinformatics · 2024Article
- Drug-target affinity prediction with extended graph learning-convolutional networks.BMC bioinformatics · 2024Article
- The role of artificial intelligence in drug screening, drug design, and clinical trials.Frontiers in pharmacology · 2024Review
- A general hypergraph learning algorithm for drug multi-task predictions in micro-to-macro biomedical networks.PLoS computational biology · 2023Article
- Molecular Property Prediction by Combining LSTM and GAT.Biomolecules · 2023Article
- Advances in Drug Design and Development for Human Therapeutics Using Artificial Intelligence-I.Biomolecules · 2022Article
- DCSE:Double-Channel-Siamese-Ensemble model for protein protein interaction prediction.BMC genomics · 2022Article
- SDNN-PPI: self-attention with deep neural network effect on protein-protein interaction prediction.BMC genomics · 2022Article
- Sequence-based drug-target affinity prediction using weighted graph neural networks.BMC genomics · 2022Article
- Pose Classification Using Three-Dimensional Atomic Structure-Based Neural Networks Applied to Ion Channel-Ligand Docking.Journal of chemical information and modeling · 2022Review
- Multi-TransDTI: Transformer for Drug-Target Interaction Prediction Based on Simple Universal Dictionaries with Multi-View Strategy.Biomolecules · 2022Article
- Exploring the effects of drug, disease, and protein dependencies on biomedical named entity recognition: A comparative analysis.Frontiers in pharmacology · 2022Article
- SAG-DTA: Prediction of Drug-Target Affinity Using Self-Attention Graph Network.International journal of molecular sciences · 2021Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In the process of drug discovery, identifying the interaction between the protein and the novel compound plays an important role. With the development of technology, deep learning methods have shown excellent performance in various situations. However, the compound-protein interaction is complicated and the features extracted by most deep models are not comprehensive, which limits the performance to a certain extent. In this paper, we proposed a multiscale convolutional network that extracted the local and global features of the protein and the topological feature of the compound using different types of convolutional networks. The results showed that our model obtained the best performance compared with the existing deep learning methods.
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