A comprehensive comparison of deep learning-based compound-target interaction prediction models to unveil guiding design principles.

ABSTRACT: The evaluation of compound-target interactions (CTIs) is at the heart of drug discovery efforts. Given the substantial time and monetary costs of classical experimental screening, significant efforts have been dedicated to develop deep learning-based models that can accurately predict CTIs. A comprehensive comparison of these models on a large, curated CTI dataset is, however, still lacking. Here, we perform an in-depth comparison of 12 state-of-the-art deep learning architectures that use different protein and compound representations. The models were selected for their reported performance and architectures. To reliably compare model performance, we curated over 300 thousand binding and non-binding CTIs and established several gold-standard datasets of varying size and information. Based

SUBMITTER: Abdollahi S 

PROVIDER: S-EPMC11520803 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature