Masoomkhah Seyed Saeed, Rezaee Khosro, Ansari Mojtaba, Eslami Hossein, Shirani Shahin, Alizadeh Mohammad Hossein
Department of Biomedical Engineering, Meybod University, Meybod, Iran.
Department of Biomedical Engineering, Amirkabir University of Technology, Tehran, Iran.
Sci Rep. 2025 Sep 1;15(1):32205. doi: 10.1038/s41598-025-18091-x.
Drug classification and target identification are crucial yet challenging steps in drug discovery. Existing methods often suffer from inefficiencies, overfitting, and limited scalability. Traditional approaches like support vector machines and XGBoost struggle to handle large, complex pharmaceutical datasets effectively. Deep learning models, while powerful, face challenges with interpretability, computational complexity, and generalization to unseen data. This study addresses these limitations by introducing a novel framework: optSAE + HSAPSO. This framework integrates a stacked autoencoder (SAE) for robust feature extraction with a hierarchically self-adaptive particle swarm optimization (HSAPSO) algorithm for adaptive parameter optimization. This combination delivers superior performance across various classification metrics. Experimental evaluations on datasets from DrugBank and Swiss-Prot demonstrate that optSAE + HSAPSO achieves a high accuracy of 95.52%. Notably, it exhibits significantly reduced computational complexity (0.010 s per sample) and exceptional stability (± 0.003). Compared to state-of-the-art methods, the framework offers higher accuracy, faster convergence, and greater resilience to variability. Furthermore, ROC and convergence analyses confirm its robustness and generalization capability, maintaining consistent performance across both validation and unseen datasets. By leveraging advanced optimization techniques, the framework efficiently handles large feature sets and diverse pharmaceutical data, making it a scalable and adaptable solution for real-world drug discovery applications. However, the method's performance is dependent on the quality of the training data, and fine-tuning may be necessary for high-dimensional datasets. Despite these limitations, the optSAE + HSAPSO framework demonstrates transformative potential, significantly reducing computational overhead while improving classification accuracy and reliability. This work advances the field of pharmaceutical informatics by presenting a reliable and efficient framework for drug classification and target identification. These findings open promising avenues for future research, including extending the framework to other domains such as disease diagnostics or genetic data classification, ultimately accelerating the drug development process.
药物分类和靶点识别是药物研发中至关重要但又具有挑战性的步骤。现有方法常常存在效率低下、过拟合以及可扩展性有限等问题。像支持向量机和XGBoost这样的传统方法难以有效处理大型、复杂的药学数据集。深度学习模型虽然强大，但在可解释性、计算复杂性以及对未见数据的泛化能力方面面临挑战。本研究通过引入一种新颖的框架：optSAE + HSAPSO来解决这些局限性。该框架将用于稳健特征提取的堆叠自编码器（SAE）与用于自适应参数优化的分层自适应粒子群优化（HSAPSO）算法相结合。这种组合在各种分类指标上都具有卓越的性能。对来自DrugBank和Swiss - Prot数据集的实验评估表明，optSAE + HSAPSO实现了95.52%的高精度。值得注意的是，它的计算复杂性显著降低（每个样本0.010秒）且具有出色的稳定性（±0.003）。与现有最先进的方法相比，该框架具有更高的准确性、更快的收敛速度以及对变异性更强的适应能力。此外，ROC和收敛性分析证实了其稳健性和泛化能力，在验证数据集和未见数据集上都保持一致的性能。通过利用先进的优化技术，该框架有效地处理了大型特征集和多样的药学数据，使其成为适用于实际药物研发应用的可扩展且适应性强的解决方案。然而，该方法的性能取决于训练数据的质量，对于高维数据集可能需要进行微调。尽管存在这些局限性，optSAE + HSAPSO框架仍展现出变革性潜力，在显著降低计算开销的同时提高了分类准确性和可靠性。这项工作通过提出一个用于药物分类和靶点识别的可靠且高效的框架，推动了药学信息学领域的发展。这些发现为未来的研究开辟了有前景的途径，包括将该框架扩展到其他领域，如疾病诊断或基因数据分类，最终加速药物研发进程。
