Deep_TPPred: Improved Prediction of Protein Toxicity Using Feature Fusion and Hybrid Neural Network Approach

Protein toxicity prediction is crucial for drug discovery, safety assessment, and toxicological research. This study introduces $\mathrm{Deep\_{T}PPred}$, a novel hybrid deep learning (DL) model that integrates Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) for accurate protein toxicity prediction. The model effectively combines diverse protein sequence descriptors to capture complex sequence relationships by leveraging a feature fusion technique. The methodology involved advanced feature extraction, rigorous training, and performance evaluation using benchmark datasets. $\mathrm{Deep\_{T}PPred}$ demonstrates state-of-the-art performance with an accuracy of 0.9983, specificity of 0.9988, sensitivity of 0.9975, and Kappa and MCC values of 0.9963. These results underscore the proposed model's robustness, reliability, and generalization capability, surpassing existing models across all metrics. The study highlights the potential of hybrid DL and feature fusion techniques to significantly enhance protein toxicity prediction, providing valuable insights and tools for bioinformatics pipelines and applications.