Integration of Phenotypic Drug Efficacy and Molecular Chemogenomics Data

This thesis thoroughly analyzed the sequence, structure, and molecular interactions of the two major classes of protein kinases - eukaryotic and atypical. This analysis was intended to enhance our understanding of inhibitor binding and selectivity and to create advanced machine-learning pipelines capable of leveraging these differences to predict small molecules with desired polypharmacological characteristics.To reach this aim, the following objectives were defined: 1). Extend the KLIFS database with the atypical kinase class. 2). Systematically analyze the sequence, structural, and molecular interaction kinase data from KLIFS for new insights into inhibitor binding and specificity. 3). Integrate and curate bioactivity data from ChEMBL, KIEO, literature, and genomics data from COSMIC, dbSNP, and cBioPortal. 4). Develop a convolutional neural network pipeline that can learn from 3D kinase structures to predict the polypharmacological profiles of small- molecule protein kinase inhibitors.