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DGMM:一个深度学习遗传算法框架,用于有效优化药物发现的领先优化.

Jiebin Fang1,2, Churu Mao2, Yuchen Zhu3

  • 1Hainan Institute, Zhejiang University, Sanya 572025, China.

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概括
此摘要是机器生成的。

深度遗传分子修饰 (DGMM) 算法通过平衡分子结构和活性来增强药物发现. 这种计算工具成功地识别了具有增加生物效力的新型ROCK2抑制剂.

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科学领域:

  • 计算化学是一种计算化学.
  • 药物发现 药物发现
  • 机器学习 机器学习

背景情况:

  • 药物发现中的优化需要平衡结构多样性与核心分子特征,并优化生物活性与药物类似性质的平衡.
  • 现有的方法在有效地探索化学空间,同时保持支架完整性和所需的药理学配置文件时面临挑战.

研究的目的:

  • 引入和验证深度遗传分子修改 (DGMM) 算法,这是一种用于药物发现中高效分子优化的新型计算框架.
  • 证明DGMM在平衡结构变异与支架保留和优化药物相似性与目标活动之间的能力.

主要方法:

  • 开发了深度遗传分子修改 (DGMM) 算法,集成了深度学习 (带有支架约束的变量自编码器) 和遗传算法.
  • 采用多目标优化策略,使用蒙特卡洛搜索和马尔科夫过程进行权衡探索.
  • 对CHK1,CDK2和HDAC8位进行了追溯验证的DGMM,随后进行了ROCK2抑制剂发现的前性运动.

主要成果:

  • DGMM在活动优化方面展示了最先进的性能,产生结构多样化但药理相关的化合物.
  • 追溯验证成功地复制了已知的优化路径,为所选的目标.
  • 未来的运动导致发现了新型ROCK2抑制剂,其生物活性增加了100倍.

结论:

  • DGMM算法是一种有效的计算工具,用于药物分子的结构优化.
  • DGMM成功地平衡了结构多样性,脚手架保留和生物活性和药物样性质的优化.
  • 有力的ROCK2抑制剂的潜在发现证实了DGMM在现实世界药物发现运动中的实用性.