StackBRAF: A Large-Scale Stacking Ensemble Learning for BRAF Affinity Prediction.

ABSTRACT: The B-rapidly accelerated fibrosarcoma (BRAF) is a proto-oncogene that plays a vital role in cell signaling and growth regulation. Identifying a potent BRAF inhibitor can enhance therapeutic success in high-stage cancers, particularly metastatic melanoma. In this study, we proposed a stacking ensemble learning framework for the accurate prediction of BRAF inhibitors. We obtained 3857 curated molecules with BRAF inhibitory activity expressed as a predicted half-maximal inhibitory concentration value (pIC₅₀) from the ChEMBL database. Twelve molecular fingerprints from PaDeL-Descriptor were calculated for model training. Three machine learning algorithms including extreme gradient boosting, support vector regression, and multilayer perceptron were utilized for constructing new pred

SUBMITTER: Syahid NF 

PROVIDER: S-EPMC10268632 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature