Reciprocal perspective as a super learner improves drug-target interaction prediction (MUSDTI).

ABSTRACT: The identification of novel drug-target interactions (DTI) is critical to drug discovery and drug repurposing to address contemporary medical and public health challenges presented by emergent diseases. Historically, computational methods have framed DTI prediction as a binary classification problem (indicating whether or not a drug physically interacts with a given protein target); however, framing the problem instead as a regression-based prediction of the physiochemical binding affinity is more meaningful. With growing databases of experimentally derived drug-target interactions (e.g. Davis, Binding-DB, and Kiba), deep learning-based DTI predictors can be effectively leveraged to achieve state-of-the-art (SOTA) performance. In this work, we formulated a DTI competition as part of the co

SUBMITTER: Dick K 

PROVIDER: S-EPMC9344797 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature