Before revision: Accurate prediction of drug-target interactions (DTI) enables drug discoverytasks, including virtual screening and drug repurposing, which can shorten the time to identify promising drug candidates and provide cures to patients . Recently, there is a growing number of research that developed deep learning (DL) models for DTI. Despite their superior performance, these research models are difficult to use in real drug discovery practice due to the complexity of deploying the research code as well as the restricted data formatting, model capacity, and evaluation setting .
After revision: Accurate prediction of drug-target interactions (DTI) enables drug discoverytasks, including virtual screening and drug repurposing, which can shorten the time to identify promising drug candidates and provide cures to patients . Recently, there is a growing number of research that developed deep learning (DL) models for show promising performance for DTI prediction. However, these models can be difficult to use in real drug discovery practice due to the complexity of deploying the research code as well as the restricted data formatting, model capacity, and evaluation setting .