ArticleBMC bioinformatics2024
CCL-DTI: contributing the contrastive loss in drug-target interaction prediction.
Article in BMC bioinformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 2 of them syntheses that pooled it.
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Who cites it
33 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Computational approaches for drug-drug interaction prediction: a systematic review of data sources, modeling strategies, and evaluation frameworks.Frontiers in pharmacology · 2026Pooled it
- Knowledge mapping of graph neural networks for drug discovery: a bibliometric and visualized analysis.Frontiers in pharmacology · 2024Pooled it
- PRGNet: a Parallel Residual Graph Network for enhanced drug-target binding affinity prediction.BMC genomics · 2026Article
- Phenotypic prediction of missense variants via deep contrastive learning.Nature biomedical engineering · 2026Article
- KansFormer network based on a multimodal cross-attention mechanism for drug-target interaction prediction.Molecular diversity · 2026Article
- A comprehensive review of neural network-based approaches for drug-target interaction prediction.Molecular diversity · 2026Review
- Multimodal learning on heterogeneous subgraphs and LLMs representation for MHC-peptide binding affinity prediction.BMC bioinformatics · 2026Article
- Article
- Multi-DDA: drug-disease association prediction using a hybrid graph convolutional network with multi-modal drug representations.Bioinformatics advances · 2026Article
- DCGAT-DTI: dynamic cross-graph attention network for drug-target interaction prediction.Bioinformatics advances · 2026Article
- A Systematic Review of Drug-Related InteractionsUtilizing Deep Learning and LLMs for Prediction and Mitigation.ACS omega · 2025Review
- MoleProLink-RL: geometric transport for domain-policy reinforcement learning in drug-target interaction prediction.NPJ digital medicine · 2025Article
- [A heterogeneous graph method integrating multi-layer semantics and topological information for improving drug-target interaction prediction].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2025Article
- NASNet-DTI: accurate drug-target interaction prediction using heterogeneous graphs and node adaptation.Briefings in bioinformatics · 2025Article
- DTBA-net: Drug-Target Binding Affinity prediction using feature selection in hybrid CNN model.Journal of computer-aided molecular design · 2025Article
- Feature fusion with attributed deepwalk for protein-protein interaction prediction.Scientific reports · 2025Article
- GS-DTA: integrating graph and sequence models for predicting drug-target binding affinity.BMC genomics · 2025Article
- A novel multi-modal retrieval framework for tracking vehicles using natural language descriptions.PloS one · 2025Article
- MTAF-DTA: multi-type attention fusion network for drug-target affinity prediction.BMC bioinformatics · 2024Article
- Learning genotype-phenotype associations from gaps in multi-species sequence alignments.Briefings in bioinformatics · 2024Article
Corrections and comments
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5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe Drug-Target Interaction (DTI) prediction uses a drug molecule and a protein sequence as inputs to predict the binding affinity value. In recent years, deep learning-based models have gotten more attention. These methods have two modules: the feature extraction module and the task prediction module. In most deep learning-based approaches, a simple task prediction loss (i.e., categorical cross entropy for the classification task and mean squared error for the regression task) is used to learn the model. In machine learning, contrastive-based loss functions are developed to learn more discriminative feature space. In a deep learning-based model, extracting more discriminative feature space leads to performance improvement for the task prediction module.
resultsIn this paper, we have used multimodal knowledge as input and proposed an attention-based fusion technique to combine this knowledge. Also, we investigate how utilizing contrastive loss function along the task prediction loss could help the approach to learn a more powerful model. Four contrastive loss functions are considered: (1) max-margin contrastive loss function, (2) triplet loss function, (3) Multi-class N-pair Loss Objective, and (4) NT-Xent loss function. The proposed model is evaluated using four well-known datasets: Wang et al. dataset, Luo's dataset, Davis, and KIBA datasets.
conclusionsAccordingly, after reviewing the state-of-the-art methods, we developed a multimodal feature extraction network by combining protein sequences and drug molecules, along with protein-protein interaction networks and drug-drug interaction networks. The results show it performs significantly better than the comparable state-of-the-art approaches.
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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.