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向新的阻断PD-1/PD-L1通路的小分子抑制剂:从可解释的机器学习模型到分子动力学模拟

Xiaoyan Wu¹, Jingyi Liang¹, Luming Meng¹

¹College of Materials and Energy, South China Agricultural University, Guangzhou 510630, China.

International journal of biological macromolecules

|October 16, 2024

概括

开发了可解释的机器学习模型,以设计新的小分子抑制剂,针对癌症免疫治疗的编程细胞死亡-1 (PD-1) /编程细胞死亡联体-1 (PD-L1) 途径,从而导致有前途的新药候选者.

科学领域:

计算化学是一种计算化学.
药物发现药物发现
免疫治疗是一种免疫疗法.

背景情况:

编程细胞死亡-1 (PD-1) /编程细胞死亡联体-1 (PD-L1) 途径是癌症免疫疗法的关键目标.
机器学习 (ML) 加快了药物设计,但往往缺乏可解释性,阻碍了优化.
药物设计的现有ML模型往往是错误的.

研究的目的:

为合理的药物设计开发可解释的ML模型,针对PD-1/PD-L1通路.
为了确定影响生物活性的关键分子碎片,以优化抑制剂.
发现新型小分子抑制剂,提高疗效和安全性.

主要方法:

通过将ML算法与SHAP方法集成,构建了五个可解释的ML模型.
在4000多个分子上预先训练模型,实现R2值从0.835到0.86.
根据模型洞察力修改了BMS-1166,产生了60种新型化合物,随后进行对接和ADMET预测.

主要成果:

通过使用可解释的ML,确定了BMS-1166的结构-活性关系.
选了三个新型化合物 (C27,C52,C54),其对接分数优越,毒性低于BMS-1166.
通过分子动力学模拟验证了C27和C52对PD-L1二元体的增强结合亲和力.

结论:

开发了一种高效,可解释的ML驱动协议,用于合理设计PD-1/PD-L1抑制剂.
证明了可解释AI在克服药物发现中的ML模型解释性挑战方面的实用性.
确定了有前途的化合物,用于进一步开发癌症免疫疗法.

关键词:

药物设计药物设计机器学习是机器学习.在PD-1/PD-L1路径上.根据SHAP方法,我们可以小分子抑制剂小分子抑制剂

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