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一个基于深度学习的理论协议,用于识别潜在的异形选择性PI3Kα抑制剂

Muhammad Shafiq¹, Zaid Anis Sherwani², Mamona Mushtaq²

¹H.E.J. Research Institute of Chemistry, International Center for Chemical and Biological Sciences, University of Karachi, Karachi, 75270, Pakistan.

Molecular diversity

|February 2, 2024

概括

研究人员开发了一种机器学习和in silico药物设计策略,以确定用于癌症治疗的新PI3Kα抑制剂. 这种方法成功地确定了六种有前途的候选药物,与现有治疗方法相比,它们具有更好的结合亲和力和药理动力学特征.

科学领域:

在瘤学瘤学.
计算化学的计算化学
药理学药理学是指药理学的学科.

背景情况:

氨基酸3-酶α (PI3Kα) 在癌症中经常失调,导致瘤发生.
开发异形选择性PI3Kα抑制剂是具有挑战性的,因为PI3Ks之间的结构相似性.
现有的PI3Kα抑制剂具有相关的副作用,需要新的治疗策略.

研究的目的:

设计一个混合计算协议,整合机器学习 (ML) 和in silico药物设计,用于识别新型PI3Kα抑制剂.
为了克服PI3Kα抑制剂开发中异型选择性的挑战.
为了确定潜在的药物候选者,提高疗效和安全性.

主要方法:

开发和训练了已知PI3Kα抑制剂的物理化学描述器的深度学习分类模型.
使用ML模型选一个小分子数据库来预测潜在的抑制剂.
应用多相分子对接来评估预测化合物的结合亲和和模式.
进行了药物相似性,药物动力学 (ADME-T) 和机械动力学研究.

主要成果:

使用ML分类模型预测了662种潜在的PI3Kα抑制剂.
通过分子对接确定了12种具有高结合亲和度和与PI3Kα活性部位残留物通过分子对接的有利相互作用的化合物.
选择了六种化合物 (1, 2, 3, 6, 7, 11),具有合适的ADME-T概况和生物可用性.
机理学研究表明,化合物1,2,和11与ATP结合部位的结合力比alpelisib更高.

结论:

开发的ML模型和计算策略可靠地识别出新型的异形选择性PI3Kα抑制剂.
化合物1,2和11显示出PI3Kα向癌症治疗的显著潜力.
这种方法为发现具有改善治疗指数的新抗癌药物候选人提供了一个有希望的途径.

关键词:

在 silico 药物发现中.异形选择性抑制剂异形选择性抑制剂机器学习是机器学习.分子动力学模拟模型多层感知器多层感知器酸胺3-酶αα基因酶是什么

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