Explainable deep drug-target representations for binding affinity prediction.

ABSTRACT:

Background

Several computational advances have been achieved in the drug discovery field, promoting the identification of novel drug-target interactions and new leads. However, most of these methodologies have been overlooking the importance of providing explanations to the decision-making process of deep learning architectures. In this research study, we explore the reliability of convolutional neural networks (CNNs) at identifying relevant regions for binding, specifically binding sites and motifs, and the significance of the deep representations extracted by providing explanations to the model's decisions based on the identification of the input regions that contributed the most to the prediction. We make use of an end-to-end deep learning architecture to predict binding affinit

SUBMITTER: Monteiro NRC 

PROVIDER: S-EPMC9204982 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature