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

Abstract

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 (R²) of 0.855, surpassing Chemprop (MAE = 0.443, R² = 0.803), FG-BERT (MAE = 0.460, R² = 0.785), and Stack_BRAF (MAE = 0.403, R² = 0.839). Extensive evaluation on benchmark data sets affirms BRAFPred’s superiority over state-of-the-art methodologies, with robust generalization capabilities demonstrated on blind test sets. Additionally, ablation studies and case analyses underscore the robustness of the model’s design. The source code, data sets, and prediction results for BRAFPred are available for further research at https://github.com/EvanZhang1216/BRAFPred.