通过人工智能驱动的QSAR建模加速发现白血病抑制剂.
Samuel Kakraba1, Edmund Fosu Agyemang2, Robert J Shmookler Reis3
1Biostatistics and Data Science, Celia Scott Weatherhead School of Public Health and Tropical Medicince at Tulane University, Tulane University, 1440 Canal St., New Orleans, US.
JMIR AI
|December 8, 2025
概括
机器学习增强的定量结构-活性关系 (QSAR) 模型准确地预测了Thiadiazolidinone (TDZD) 类型的抗白血病活性. 确定了影响功效的关键分子特征,指导了未来的药物设计.
科学领域:
- 计算化学和化学信息学
- 瘤学和药物发现
- 机器学习在药物化学中的应用.
背景情况:
- 白血病治疗面临挑战,因为目前的抑制剂的效力和选择性有限.
- 传统的药物发现方法难以在复杂的化学空间中寻找新兴抑制剂.
- 机器学习 (ML) 增强的定量结构-活性关系 (QSAR) 建模提供了一种计算策略,以优化药物候选物.
研究的目的:
- 开发和验证一个集成的ML增强的QSAR工作流程,用于合理设计提亚利丁 (TDZD) 类似物.
- 预测和优化具有改善抗白血病活性的TDZD类似物.
- 识别强度的关键分子决定因素,指导未来的抑制剂优化.
主要方法:
- 对35种具有确定的抗白血病活性的TDZD衍生物的分析.
- 使用施罗丁格MAESTRO计算220个分子描述符 (1D-4D).
- 训练和测试17个ML模型 (例如随机森林,XGBoost,神经网络),使用分层采样和5倍交叉验证.
- 使用12个指标进行性能评估,包括MSE,R2和SHAP值,并进行超参数调整.
主要成果:
- 集合方法,特别是LightGBM和随机森林,显示出优异的预测性能 (LightGBM:R2 = 0.971).
- 适度的训练到测试性能下降表明了真正的模式学习.
- 影响抗白血病活性的关键特征包括分子形状,极地表面积,极化性,分离系数和结合能力.
结论:
- 集成的ML和QSAR建模有效地分析TDZD类型的结构-活动关系.
- 组合方法显示高的内部验证,但外部验证和实验测试至关重要.
- 鉴定的分子特征为未来抗白血病抑制剂的验证和优化提供了基础.
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