LCK-SafeScreen-Model:一种先进的集成机器学习方法,用于估计化合物与LCK目标之间的绑定亲和关系
Ying Cheng1,2, Cong Ji1, Jun Xu1,3
1College of Pharmaceutical Sciences, Hangzhou First People's Hospital, Zhejiang Chinese Medical University, Hangzhou 311402, China.
Molecules (Basel, Switzerland)
|November 14, 2023
概括
我们开发了一种集体机器学习模型,以预测药物分子与淋巴细胞特异性蛋白氨酸激酶 (LCK) 的结合亲和力. 这种模型准确地识别了潜在的LCK抑制剂,有助于白血病治疗研究.
科学领域:
- 生物化学 生物化学
- 计算化学计算化学
- 药理学 药理学是指药理学的学科.
背景情况:
- 淋巴细胞特异性蛋白氨酸激酶 (LCK) 是白血病治疗的关键标.
- LCK 抑制剂的非目标相互作用可能会导致不良影响,需要准确的预测方法.
- 在药物开发过程中,对药物-LCK相互作用的可靠预测至关重要.
研究的目的:
- 开发和优化一个整体机器学习模型,用于预测药物分子和LCK之间的结合亲和力.
- 为了提高LCK抑制剂预测的准确性,用于药物发现和安全性评估.
- 为研究人员提供可访问的工具 (web服务器,GitHub).
主要方法:
- 产生和选择分子指纹.
- 集成机器学习模型的设计和超参数调整.
- 药物库的选,使用ADP-Glo测定进行实验验证,以及分子对接.
主要成果:
- 整体模型实现了更好的预测性能,将测试R平方从0.644增加到0.730.
- 测试根平均平方误差 (RMSE) 从0.841减少到0.732.
- 该模型成功地从药物库中识别出最高得分的化合物,并通过实验分析和对接验证.
结论:
- 精细的整体模型在预测LCK抑制剂方面表现出高准确性.
- 这种方法对安全面板预测和发现新型LCK抑制剂都有效.
- 开发的工具有助于在LCK向疗法的更广泛的应用和研究.
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