DeepREAL: a deep learning powered multi-scale modeling framework for predicting out-of-distribution ligand-induced GPCR activity.

ABSTRACT:

Motivation

Drug discovery has witnessed intensive exploration of predictive modeling of drug-target physical interactions over two decades. However, a critical knowledge gap needs to be filled for correlating drug-target interactions with clinical outcomes: predicting genome-wide receptor activities or function selectivity, especially agonist versus antagonist, induced by novel chemicals. Two major obstacles compound the difficulty on this task: known data of receptor activity is far too scarce to train a robust model in light of genome-scale applications, and real-world applications need to deploy a model on data from various shifted distributions.

Results

To address these challenges, we have developed an end-to-end deep learning framework, DeepREAL, for multi-scale modelin

SUBMITTER: Cai T 

PROVIDER: S-EPMC9048666 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature