ArticleBioinformatics (Oxford, England)2021
Bayesian neural network with pretrained protein embedding enhances prediction accuracy of drug-protein interaction.
Article in Bioinformatics (Oxford, England), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Intelligence on the graph: Graph neural networks for mechanistic drug target discovery.Journal of pharmaceutical analysis · 2026Review
- AMCF-RDP: a self-attention-based multi-source and cascade framework for the identification of drug-protein relationships.Molecular diversity · 2026Article
- DeepRNA-DTI: a deep learning approach for RNA-compound interaction prediction with binding site interpretability.Journal of cheminformatics · 2025Article
- Bayesian LSTM-Based Missing Data Estimation and Flexural Strength Assessment for Determination of Novel Smart Mobility Pavement Materials: Mg(OH)Materials (Basel, Switzerland) · 2025Article
- Negative sampling strategies impact the prediction of scale-free biomolecular network interactions with machine learning.BMC biology · 2025Article
- Achieving well-informed decision-making in drug discovery: a comprehensive calibration study using neural network-based structure-activity models.Journal of cheminformatics · 2025Article
- ET-PROTACs: modeling ternary complex interactions using cross-modal learning and ternary attention for accurate PROTAC-induced degradation prediction.Briefings in bioinformatics · 2024Article
- Drug-Target Interactions Prediction at Scale: The Komet Algorithm with the LCIdb Dataset.Journal of chemical information and modeling · 2024Article
- Reducing overconfident errors in molecular property classification using Posterior Network.Patterns (New York, N.Y.) · 2024Article
- Pmf-cpi: assessing drug selectivity with a pretrained multi-functional model for compound-protein interactions.Journal of cheminformatics · 2023Article
- Sequence-based drug design as a concept in computational drug design.Nature communications · 2023Article
- Self- and cross-attention accurately predicts metabolite-protein interactions.NAR genomics and bioinformatics · 2023Article
- BayeStab: Predicting effects of mutations on protein stability with uncertainty quantification.Protein science : a publication of the Protein Society · 2022Article
- Review
- A brief review of protein-ligand interaction prediction.Computational and structural biotechnology journal · 2022Review
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Authors and funding
5 authors.
Funding
Abstract
motivationCharacterizing drug-protein interactions (DPIs) is crucial to the high-throughput screening for drug discovery. The deep learning-based approaches have attracted attention because they can predict DPIs without human trial and error. However, because data labeling requires significant resources, the available protein data size is relatively small, which consequently decreases model performance. Here, we propose two methods to construct a deep learning framework that exhibits superior performance with a small labeled dataset.
resultsAt first, we use transfer learning in encoding protein sequences with a pretrained model, which trains general sequence representations in an unsupervised manner. Second, we use a Bayesian neural network to make a robust model by estimating the data uncertainty. Our resulting model performs better than the previous baselines at predicting interactions between molecules and proteins. We also show that the quantified uncertainty from the Bayesian inference is related to confidence and can be used for screening DPI data points. AVAILABILITY AND IMPLEMENTATION: The code is available at https://github.com/QHwan/PretrainDPI. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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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.