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Mitigating cold-start problems in drug-target affinity prediction with interaction knowledge transferring
journal contribution
posted on 2022-09-29, 06:06 authored by T M Nguyen, Thin NguyenThin Nguyen, Truyen TranTruyen TranAbstract
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.
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.
History
Journal
Briefings in bioinformaticsVolume
23Issue
4Publisher
OXFORD UNIV PRESSLocation
EnglandPublisher DOI
ISSN
1467-5463eISSN
1477-4054Language
EnglishPublication classification
C1 Refereed article in a scholarly journalUsage metrics
Keywords
Science & TechnologyLife Sciences & BiomedicineBiochemical Research MethodsMathematical & Computational BiologyBiochemistry & Molecular Biologyprotein-protein interactionchemical-chemical interactiondrug-target affinitytransfer learningPROTEIN-PROTEIN INTERACTIONSSMALL-MOLECULE INHIBITORSPDBBIND DATABASEchemical–chemical interactionprotein–protein interactionComputation Theory and Mathematics
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