Improving Compound-Protein Interaction Prediction by Self-Training with Augmenting Negative Samples.

ABSTRACT: Identifying compound-protein interactions (CPIs) is crucial for drug discovery. Since experimentally validating CPIs is often time-consuming and costly, computational approaches are expected to facilitate the process. Rapid growths of available CPI databases have accelerated the development of many machine-learning methods for CPI predictions. However, their performance, particularly their generalizability against external data, often suffers from a data imbalance attributed to the lack of experimentally validated inactive (negative) samples. In this study, we developed a self-training method for augmenting both credible and informative negative samples to improve the performance of models impaired by data imbalances. The constructed model demonstrated higher performance than those constru

SUBMITTER: Koyama T 

PROVIDER: S-EPMC10428206 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature