BiComp-DTA: Drug-target binding affinity prediction through complementary biological-related and compression-based featurization approach.

ABSTRACT: Drug-target binding affinity prediction plays a key role in the early stage of drug discovery. Numerous experimental and data-driven approaches have been developed for predicting drug-target binding affinity. However, experimental methods highly rely on the limited structural-related information from drug-target pairs, domain knowledge, and time-consuming assays. On the other hand, learning-based methods have shown an acceptable prediction performance. However, most of them utilize several simple and complex types of proteins and drug compounds data, ranging from the protein sequences to the topology of a graph representation of drug compounds, employing multiple deep neural networks for encoding and feature extraction, and so, leads to the computational overheads. In this study, we propos

SUBMITTER: Kalemati M 

PROVIDER: S-EPMC10096306 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature