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BRAFPred: A Novel Approach for Accurate Prediction of the B-Type Rapidly Accelerated Fibrosarcoma Inhibitor
Ming Zhang1, Chaoming Zhang1, Keyu Liu1
1School of Computer, Jiangsu University of Science and Technology, 666 Changhui Road, Zhenjiang 212100, China.
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 (R 2) of 0.855, surpassing Chemprop (MAE = 0.443, R 2 = 0.803), FG-BERT (MAE = 0.460, R 2 = 0.785), and Stack_BRAF (MAE = 0.403, R 2 = 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.
Insights
BRAFPred, a new stacked ensemble learning framework, precisely predicts BRAF inhibitors by integrating diverse molecular features and advanced machine learning models. This approach significantly outperforms existing methods in predicting drug efficacy for cancer therapeutics.
Area of Science:
- Computational chemistry and cheminformatics
- Machine learning in drug discovery
- Oncology and cancer therapeutics
Background:
- B-type rapidly accelerated fibrosarcoma (BRAF) oncogene is a critical target in cancer therapy.
- Traditional quantitative structure-activity relationship (QSAR) models face limitations in handling complex nonlinearities and generalization.
- Predictive efficacy of existing models is often undermined by reliance on singular approaches.
Purpose of the Study:
- To develop a novel framework, BRAFPred, for precise prediction of BRAF inhibitors.
- To overcome limitations of traditional QSAR methods by integrating diverse machine learning techniques.
- To enhance the predictive accuracy and generalization capabilities for BRAF inhibitor discovery.
Main Methods:
- Utilized a stacked ensemble learning approach integrating classical and deep learning models.
- Incorporated handcrafted molecular descriptors (PaDeL) and small molecule sequence features.
- Employed extreme gradient boosting (XGB), support vector regression (SVR), Chemprop, and FG-BERT (pretrained BERT model) for feature generation.
- Integrated multisource features into a meta-ensemble random forest regression model.
Main Results:
- BRAFPred achieved a mean absolute error (MAE) of 0.383 and R-squared (R²) of 0.855.
- Significantly outperformed benchmark models including Chemprop (MAE=0.443, R²=0.803), FG-BERT (MAE=0.460, R²=0.785), and Stack_BRAF (MAE=0.403, R²=0.839).
- Demonstrated robust generalization capabilities on blind test sets and benchmark datasets.
Conclusions:
- BRAFPred represents a superior methodology for predicting BRAF inhibitors compared to state-of-the-art approaches.
- The stacked ensemble framework effectively integrates diverse data sources and models for enhanced predictive performance.
- The study provides a robust and generalizable tool for accelerating the discovery of novel BRAF-targeted cancer therapeutics.
