IEdgeDTA: integrated edge information and 1D graph convolutional neural networks for binding affinity prediction.

ABSTRACT: Artificial intelligence has become more prevalent in broad fields, including drug discovery, in which the process is costly and time-consuming when conducted through wet experiments. As a result, drug repurposing, which tries to utilize approved and low-risk drugs for a new purpose, becomes more attractive. However, screening candidates from many drugs for specific protein targets is still expensive and tedious. This study aims to leverage computational resources to aid drug discovery by utilizing drug-protein interaction data and estimating their interaction strength, so-called binding affinity. Our estimation approach addresses multiple challenges encountered in the field. First, we employed a graph-based deep learning technique to overcome the limitations of drug compounds represented i

SUBMITTER: Suviriyapaisal N 

PROVIDER: S-EPMC10448119 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature