Mitigating cold-start problems in drug-target affinity prediction with interaction knowledge transferring.

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-pr

SUBMITTER: Nguyen TM 

PROVIDER: S-EPMC9353967 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature