ReviewComputational and structural biotechnology journal2021
A review on compound-protein interaction prediction methods: Data, format, representation and model.
Review in Computational and structural biotechnology journal, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers.
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Who cites it
47 citing papers in PubMed.
- Integrative machine learning approaches for enzyme kinetic parameter prediction.Briefings in bioinformatics · 2026Review
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- The return of metabolism: biochemistry and physiology of glycolysis.Biological reviews of the Cambridge Philosophical Society · 2026Review
- Machine learning for drug-target interaction prediction: A comprehensive review of models, challenges, and computational strategies.Computational and structural biotechnology journal · 2026Review
- WDGBANDTI: A Deep Graph Convolutional Network-Based Bilinear Attention Network for Drug-Target Interaction Prediction with Domain Adaptation.Interdisciplinary sciences, computational life sciences · 2025Article
- Deep learning-based radiolabelled compound-protein interaction prediction for NDUFS1-targeting radiopharmaceutical discovery.EJNMMI research · 2025Article
- Evidential deep learning-based drug-target interaction prediction.Nature communications · 2025Article
- ColdstartCPI: Induced-fit theory-guided DTI predictive model with improved generalization performance.Nature communications · 2025Article
- MixingDTA: improved drug-target affinity prediction by extending mixup with guilt-by-association.Bioinformatics (Oxford, England) · 2025Article
- EM-PLA: environment-aware heterogeneous graph-based multimodal protein-ligand binding affinity prediction.Bioinformatics (Oxford, England) · 2025Article
- Review
- Impact of Alignments on the Accuracy of Protein Subcellular Localization Predictions.Proteins · 2025Article
- BioStructNet: Structure-Based Network with Transfer Learning for Predicting Biocatalyst Functions.Journal of chemical theory and computation · 2025Article
- Precision cardiovascular medicine: shifting the innovation paradigm.Frontiers in science · 2025Article
- Artificial intelligence in antidiabetic drug discovery: The advances in QSAR and the prediction of α-glucosidase inhibitors.Computational and structural biotechnology journal · 2024Review
- A deep learning framework combining molecular image and protein structural representations identifies candidate drugs for pain.Cell reports methods · 2024Article
- Drug-Target Interactions Prediction at Scale: The Komet Algorithm with the LCIdb Dataset.Journal of chemical information and modeling · 2024Article
- MvGraphDTA: multi-view-based graph deep model for drug-target affinity prediction by introducing the graphs and line graphs.BMC biology · 2024Article
- A review of deep learning methods for ligand based drug virtual screening.Fundamental research · 2024Review
- Surface-based multimodal protein-ligand binding affinity prediction.Bioinformatics (Oxford, England) · 2024Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
There has recently been a rapid progress in computational methods for determining protein targets of small molecule drugs, which will be termed as compound protein interaction (CPI). In this review, we comprehensively review topics related to computational prediction of CPI. Data for CPI has been accumulated and curated significantly both in quantity and quality. Computational methods have become powerful ever to analyze such complex the data. Thus, recent successes in the improved quality of CPI prediction are due to use of both sophisticated computational techniques and higher quality information in the databases. The goal of this article is to provide reviews of topics related to CPI, such as data, format, representation, to computational models, so that researchers can take full advantages of these resources to develop novel prediction methods. Chemical compounds and protein data from various resources were discussed in terms of data formats and encoding schemes. For the CPI methods, we grouped prediction methods into five categories from traditional machine learning techniques to state-of-the-art deep learning techniques. In closing, we discussed emerging machine learning topics to help both experimental and computational scientists leverage the current knowledge and strategies to develop more powerful and accurate CPI prediction methods.
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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.