用药物发现堆回归器进行元建模:一种可解释的AI视角
Spoorthi J S1, Vijayalakshmi M2, Sasithradevi A3
1School of Computer Science and Engineering, Vellore Institute of Technology, 600127, Chennai, India.
Current drug discovery technologies
|October 28, 2025
概括
可解释组合模型通过提高预测准确性和提供对化合物行为的清晰洞察来增强药物发现中的AI. 这种方法提高了人们对人工智能驱动的治疗开发的信心.
科学领域:
- 人工智能在药物发现中的作用
- 计算化学的计算化学
- 机器学习用于药理学
背景情况:
- 药物发现受到复杂的数据集,长时间和药物向相互作用的不准确预测的挑战.
- 这些挑战阻碍了及时的治疗开发,特别是在COVID-19等全球卫生危机期间.
- 本研究通过整合合体机器学习与可解释的人工智能 (XAI) 来解决这些问题.
研究的目的:
- 提高AI模型在药物发现中的预测准确度和透明度.
- 利用整体方法和XAI进行更强大和可解释的药物向相互作用预测.
- 为明智的分子设计提供化学上有意义的见解.
主要方法:
- 在104个COVID-19化合物上训练了三个回归模型 (随机森林,支持向量回归,多层感知器).
- 实施的整体策略:投票回归和堆叠回归.
- 使用SHAP (夏普利添加式解释) 和LIME (局部可解释模型不可知解释) 进行特征重要性分析.
主要成果:
- 药物发现堆回归模型表现出优异的性能,MSE为0.18和R2为0.88.
- SHAP和LIME确定了EffectiveRotorCount3D和YStericQuadrupole3D作为关键的分子描述符.
- 这些特征与分子灵活性和对药物活性至关重要的固体效应有关.
结论:
- 将组合建模与可解释性相结合,可显著提高药物发现中的预测稳定性和可解释性.
- SHAP和LIME的整合提供了化学相关的见解,支持合理的分子设计和增加模型透明度.
- 可解释的组合模型提高了AI在药物发现中的可靠性和适用性,为治疗开发提供了可扩展的解决方案.
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