A Hybrid Structure-Based Machine Learning Approach for Predicting Kinase Inhibition by Small Molecules.

ABSTRACT: Kinases have been the focus of drug discovery programs for three decades leading to over 70 therapeutic kinase inhibitors and biophysical affinity measurements for over 130,000 kinase-compound pairs. Nonetheless, the precise target spectrum for many kinases remains only partly understood. In this study, we describe a computational approach to unlocking qualitative and quantitative kinome-wide binding measurements for structure-based machine learning. Our study has three components: (i) a Kinase Inhibitor Complex (KinCo) data set comprising in silico predicted kinase structures paired with experimental binding constants, (ii) a machine learning loss function that integrates qualitative and quantitative data for model training, and (iii) a structure-based machine learning model traine

SUBMITTER: Liu C 

PROVIDER: S-EPMC10498990 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature