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使用集成堆叠自编码器和层次自适应优化进行可药物目标识别的自动化药物设计

Seyed Saeed Masoomkhah¹, Khosro Rezaee², Mojtaba Ansari³

¹Department of Biomedical Engineering, Meybod University, Meybod, Iran.

Scientific reports

|September 1, 2025

概括

一个新的optSAE+HSAPSO框架通过提高分类准确性和减少计算复杂性来增强药物发现. 这种方法提供了一个可扩展和可靠的解决方案来识别药物点.

科学领域:

医药信息学
计算生物学
机器学习在药物发现中

背景情况:

药物分类和目标识别在药物发现中至关重要但具有挑战性.
像SVM,XGBoost和深度学习模型这样的现有方法在效率,可扩展性,可解释性和通用性方面面临限制.
需要先进的计算框架来处理复杂的药物数据.

研究的目的:

引入一个新的计算框架,optSAE+HSAPSO,以实现高效准确的药物分类和目标识别.
在准确性,计算复杂性和可扩展性方面解决现有方法的局限性.
为现实世界药物发现应用提供强大而适应性的解决方案.

主要方法:

集成堆叠自动编码器 (SAE) 进行强大的特征提取.
使用等级自适应粒子群优化 (HSAPSO) 算法进行自适应参数优化.
对药物银行和瑞士-Prot数据集的实验评估.

主要成果:

在optSAE+HSAPSO框架实现了95.52%的高精度.
显著降低计算复杂度 (0.010秒/样本) 和特殊稳定性 (±0.003).
在精度,融合速度和适应变化方面表现优于最先进的方法.

结论:

选择SAE+HSAPSO框架为药物分类和目标识别提供了可扩展,可适应和高效的解决方案.
该框架显示了强度和概括能力,在验证和未见数据集上保持一致的性能.
这项工作促进了药物信息学,加速了药物开发,并有可能在疾病诊断和遗传数据分类方面应用.

关键词:

深度学习药物设计机器学习粒子群优化算法堆叠自动编码器

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