ArticleProcedia computer science2023
Towards a more general drug target interaction prediction model using transfer learning.
Article in Procedia computer science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Small data, big challenges: Machine- and deep-learning strategies for data-limited drug discovery.Advanced drug delivery reviews · 2026Review
- Advancing Drug-Drug Interaction Prediction with Biomimetic Improvements: Leveraging the Latest Artificial Intelligence Techniques to Guide Researchers in the Field.Biomimetics (Basel, Switzerland) · 2026Review
- Review
- PrCRS: a prediction model of severe CRS in CAR-T therapy based on transfer learning.BMC bioinformatics · 2024Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The topic of Drug-Target Interaction (DTI) topic has emerged nowadays since the COVID-19 outbreaks. DTI is one of the stages of finding a new cure for a recent disease. It determines whether a chemical compound would affect a particular protein, known as binding affinity. Recently, significant efforts have been devoted to artificial intelligence (AI) powered DTI. However, the use of transfer learning in DTI has not been explored extensively. This paper aims to make a more general DTI model by investigating DTI prediction method using Transfer learning. Three popular models will be tested and observed: CNN, RNN, and Transformer. Those models combined in several scenarios involving two extensive public datasets on DTI (BindingDB and DAVIS) to find the most optimum architecture. In our finding, combining the CNN model and BindingDB as the source data became the most recommended pre-trained model for real DTI cases. This conclusion was proved with the 6% AUPRC increase after fine-tuning the BindingDB pre-trained model to DAVIS dataset than without pre-training the model first.
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Registered trials
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