ArticleBMC genomics2023
Drug-target binding affinity prediction using message passing neural network and self supervised learning.
Article in BMC genomics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Protein Sequence Analysis landscape: A Systematic Review of Task Types, Databases, Datasets, Word Embeddings Methods, and Language Models.Database : the journal of biological databases and curation · 2025Pooled it
- SuperEdgeGO: Edge-supervised graph representation learning for enhanced protein function prediction.PLoS computational biology · 2025Article
- DTBA-net: Drug-Target Binding Affinity prediction using feature selection in hybrid CNN model.Journal of computer-aided molecular design · 2025Article
- Enhancing molecular property prediction with quantized GNN models.Journal of cheminformatics · 2025Article
- RNAmigos2: accelerated structure-based RNA virtual screening with deep graph learning.Nature communications · 2025Article
- A deep-learning approach to predict reproductive toxicity of chemicals using communicative message passing neural network.Frontiers in toxicology · 2025Article
- MIPPIS: protein-protein interaction site prediction network with multi-information fusion.BMC bioinformatics · 2024Article
- MSH-DTI: multi-graph convolution with self-supervised embedding and heterogeneous aggregation for drug-target interaction prediction.BMC bioinformatics · 2024Article
- DCGAN-DTA: Predicting drug-target binding affinity with deep convolutional generative adversarial networks.BMC genomics · 2024Article
- Triple Generative Self-Supervised Learning Method for Molecular Property Prediction.International journal of molecular sciences · 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
6 authors.
Funding
Abstract
backgroundDrug-target binding affinity (DTA) prediction is important for the rapid development of drug discovery. Compared to traditional methods, deep learning methods provide a new way for DTA prediction to achieve good performance without much knowledge of the biochemical background. However, there are still room for improvement in DTA prediction: (1) only focusing on the information of the atom leads to an incomplete representation of the molecular graph; (2) the self-supervised learning method could be introduced for protein representation.
resultsIn this paper, a DTA prediction model using the deep learning method is proposed, which uses an undirected-CMPNN for molecular embedding and combines CPCProt and MLM models for protein embedding. An attention mechanism is introduced to discover the important part of the protein sequence. The proposed method is evaluated on the datasets Ki and Davis, and the model outperformed other deep learning methods.
conclusionsThe proposed model improves the performance of the DTA prediction, which provides a novel strategy for deep learning-based virtual screening 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.