BRAFPred: A Novel Approach for Accurate Prediction of the B-Type Rapidly Accelerated Fibrosarcoma Inhibitor.

B-type rapidly accelerated fibrosarcoma (BRAF) is a key oncogene that regulates cell signaling and proliferation, rendering it a crucial target for cancer therapeutics. Traditional QSAR methods are hindered by their reliance on a singular model, their inability to grasp complex nonlinearities, and limited generalization, undermining predictive efficacy. To address these challenges, we introduce BRAFPred, a novel framework that leverages stacked ensemble learning to integrate both classical machine learning and advanced deep learning techniques for the precise prediction of BRAF inhibitors. We utilized 12 handcrafted molecular descriptors derived from PaDeL, in conjunction with small molecule sequence features, as foundational inputs. Furthermore, we employed extreme gradient boosting (XGB), support vector regression (SVR), and deep learning architectures based on Chemprop and a pretrained BERT model (FG-BERT) to generate additional predictive features. These multisource features were subsequently integrated within a meta-ensemble random forest regression model, which utilized 26 input variables. Empirical results demonstrate that BRAFPred significantly outperforms benchmark models, achieving a mean absolute error (MAE) of 0.383 and a coefficient of determination (