ArticleBMC medical genomics2025
Drug-target binding affinity prediction based on power graph and word2vec.
Article in BMC medical genomics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Sequence-based prediction of drug-target binding using machine learning, deep learning and ensemble models without 3D structural information.Scientific reports · 2026Article
- Article
- Integrative Computational and Transcriptional Analysis of NF-κB and HIF-1α Modulation Following Doxorubicin Treatment in Triple-Negative Breast Cancer Cells.Journal of experimental pharmacology · 2026Article
- A Systematic Evaluation of Protein Phase Separation Predictors across Diverse Protein Landscapes.Computational and structural biotechnology journal · 2026Article
- Danqi soft capsules alleviate myocardial ischemia/reperfusion injury through inhibiting apoptosis-related signaling pathways.American journal of translational research · 2025Article
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Authors and funding
5 authors.
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Abstract
backgroundDrug and protein targets affect the physiological functions and metabolic effects of the body through bonding reactions, and accurate prediction of drug-protein target interactions is crucial for drug development. In order to shorten the drug development cycle and reduce costs, machine learning methods are gradually playing an important role in the field of drug-target interactions.
resultsCompared with other methods, regression-based drug target affinity is more representative of the binding ability. Accurate prediction of drug target affinity can effectively reduce the time and cost of drug retargeting and new drug development. In this paper, a drug target affinity prediction model (WPGraphDTA) based on power graph and word2vec is proposed.
conclusionsIn this model, the drug molecular features in the power graph module are extracted by a graph neural network, and then the protein features are obtained by the Word2vec method. After feature fusion, they are input into the three full connection layers to obtain the drug target affinity prediction value. We conducted experiments on the Davis and Kiba datasets, and the experimental results showed that WPGraphDTA exhibited good prediction performance.
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