基于激素的方法用于多目标药物发现:三重目标抑制剂的PTML建模
Valeria V Kleandrova1, M Natália D S Cordeiro1, Alejandro Speck-Planche1
1LAQV@REQUIMTE/Department of Chemistry and Biochemistry, Faculty of Sciences, University of Porto, 4169-007, Porto, Portugal.
Current topics in medicinal chemistry
|August 22, 2024
概括
这项研究引入了一种结合机器学习和基于片段的设计的计算方法,以发现新的三重向癌症抑制剂. 开发的模型准确地预测了药物样分子针对关键的癌症相关蛋白质.
科学领域:
- 计算化学和药物发现
- 瘤学研究的研究.
- 在药理学中的机器学习.
背景情况:
- 癌症是一种复杂的多基因疾病,需要多目标药物发现方法.
- 计算方法加快了多目标抗癌剂的识别.
- 这项研究的重点是设计Tropomyosin受体激酶A (TRKA),多[ADP-ribose]聚合酶1 (PARP-1) 和胰岛素样生长因子1受体 (IGF1R) 的抑制剂.
研究的目的:
- 使用计算方法合理设计和预测针对TRKA,PARP-1和IGF1R的三重位抑制剂.
- 将扰乱理论机器学习模型 (PTML-EL-MLP) 与基于碎片的拓设计 (FBTD) 结合起来.
- 为了在瘤学中产生新的化学多样性,用于多目标药物发现.
主要方法:
- 利用ChEMBL数据库进行化学和生物数据提取.
- 应用了Box-Jenkins方法来生成多标签的拓索引.
- 开发了多层感知子网络 (PTML-EL-MLP) 的扰动理论机器学习模型集合.
- 集成的PTML-EL-MLP与基于碎片的拓设计 (FBTD) 进行分子设计.
主要成果:
- PTML-EL-MLP模型的准确性达到了大约80%.
- 通过FBTD,可以对模型进行物理化学和结构解释.
- 确定了关键的分子碎片,积极影响多目标活动.
- 虚拟设计了四种新的类似药物的分子,预测它们是三重目标抑制剂.
结论:
- 结合PTML建模和FBTD是产生新化学实体的有效方法.
- 这种方法有助于在瘤学中发现多目标药物.
- 该研究强调了在药物开发中创造新的化学多样性的潜力.
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