Combining machine learning and structure-based approaches to develop oncogene PIM kinase inhibitors.

ABSTRACT: Introduction: PIM kinases are targets for therapeutic intervention since they are associated with a number of malignancies by boosting cell survival and proliferation. Over the past years, the rate of new PIM inhibitors discovery has increased significantly, however, new generation of potent molecules with the right pharmacologic profiles were in demand that can probably lead to the development of Pim kinase inhibitors that are effective against human cancer. Method: In the current study, a machine learning and structure based approaches were used to generate novel and effective chemical therapeutics for PIM-1 kinase. Four different machine learning methods, namely, support vector machine, random forest, k-nearest neighbour and XGBoost have been used for the development of mo

SUBMITTER: Almukadi H 

PROVIDER: S-EPMC10036574 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature