PINNED: identifying characteristics of druggable human proteins using an interpretable neural network.

ABSTRACT: The identification of human proteins that are amenable to pharmacologic modulation without significant off-target effects remains an important unsolved challenge. Computational methods have been devised to identify features which distinguish between "druggable" and "undruggable" proteins, finding that protein sequence, tissue and cellular localization, biological role, and position in the protein-protein interaction network are all important discriminant factors. However, many prior efforts to automate the assessment of protein druggability suffer from low performance or poor interpretability. We developed a neural network-based machine learning model capable of generating druggability sub-scores based on each of four distinct categories, combining them to form an overall druggability scor

SUBMITTER: Cunningham M 

PROVIDER: S-EPMC10354961 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature