ArticleBriefings in bioinformatics2022
Mitigating cold-start problems in drug-target affinity prediction with interaction knowledge transferring.
Article in Briefings in bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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16 citing papers in PubMed, 28 citations in OpenAlex.
- Graph-based drug-target interaction modeling: from representation learning to output-driven drug discovery.Briefings in bioinformatics · 2026Review
- Intelligence on the graph: Graph neural networks for mechanistic drug target discovery.Journal of pharmaceutical analysis · 2026Review
- A novel method for drug-target affinity prediction by integrating predicted evolutionary information and multi-scale protein graphs.BMC biology · 2026Article
- NovelHTI: An Interpretable Pathway-Enhanced Framework for De Novo Target Prediction of Medicinal Herbs via Cross-Scale Heterogeneous Information Fusion.Pharmaceuticals (Basel, Switzerland) · 2026Article
- Empowering federated learning for robust compound-protein interaction prediction across heterogeneous cross-pharma domains.Journal of cheminformatics · 2026Article
- CS-DTA: a language model-driven framework for robust drug-target affinity prediction under strict cold-start scenarios.Frontiers in chemistry · 2026Article
- Leveraging artificial intelligence for efficient microbial production.Bioresource technology · 2026Review
- Benchmarking Sequence-Based Compound-Protein Interaction Prediction through Constructing a Debiased Data Set CDPN.Journal of chemical information and modeling · 2025Article
- AI-enabled language models (LMs) to large language models (LLMs) and multimodal large language models (MLLMs) in drug discovery and development.Journal of advanced research · 2025Review
- Application of Artificial Intelligence In Drug-target Interactions Prediction: A Review.npj biomedical innovations · 2025Review
- Improving Molecule Generation and Drug Discovery with a Knowledge-enhanced Generative Model.ArXiv · 2024Article
- DTI-LM: language model powered drug-target interaction prediction.Bioinformatics (Oxford, England) · 2024Article
- DrugMGR: a deep bioactive molecule binding method to identify compounds targeting proteins.Bioinformatics (Oxford, England) · 2024Article
- MULGA, a unified multi-view graph autoencoder-based approach for identifying drug-protein interaction and drug repositioning.Bioinformatics (Oxford, England) · 2023Article
- MDTips: a multimodal-data-based drug-target interaction prediction system fusing knowledge, gene expression profile, and structural data.Bioinformatics (Oxford, England) · 2023Article
- BiComp-DTA: Drug-target binding affinity prediction through complementary biological-related and compression-based featurization approach.PLoS computational biology · 2023Article
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Predicting the drug-target interaction is crucial for drug discovery as well as drug repurposing. Machine learning is commonly used in drug-target affinity (DTA) problem. However, the machine learning model faces the cold-start problem where the model performance drops when predicting the interaction of a novel drug or target. Previous works try to solve the cold start problem by learning the drug or target representation using unsupervised learning. While the drug or target representation can be learned in an unsupervised manner, it still lacks the interaction information, which is critical in drug-target interaction. To incorporate the interaction information into the drug and protein interaction, we proposed using transfer learning from chemical-chemical interaction (CCI) and protein-protein interaction (PPI) task to drug-target interaction task. The representation learned by CCI and PPI tasks can be transferred smoothly to the DTA task due to the similar nature of the tasks. The result on the DTA datasets shows that our proposed method has advantages compared to other pre-training methods in the DTA task.
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