多实例学习方法用于模拟形态灵活有机催化剂的反选择性
Dmitry Zankov1,2, Timur Madzhidov3, Pavel Polishchuk4
1Laboratory of Chemoinformatics, University of Strasbourg, Strasbourg 67081, France.
Journal of chemical information and modeling
|October 30, 2023
概括
我们开发了一种使用多实例学习的新计算方法,用于预测奇拉有机催化剂的酶选择性. 这种方法模拟了多个催化剂结构,提高了酸盐化合物合成的效率.
科学领域:
- 计算化学是一种计算化学.
- 有机合成 有机合成
- 机器学习 机器学习
背景情况:
- 的有机催化剂对于不对称的合成至关重要,使得能有效地生产反纯化合物.
- 目前用于预测催化剂反选择性的计算方法可能是资源密集型的,通常需要符合性选择和空间对齐.
- 在催化剂设计中减少材料和人力资源是可持续化学的关键目标.
研究的目的:
- 提出一种新的多实例学习方法来建模催化剂的enantioselectivity.
- 开发一个计算策略,可以考虑多个催化剂对应器,而没有明确的选择或对齐.
- 提供一个高效和准确的工具,用于计算设计的性有机催化剂.
主要方法:
- 使用多实例学习框架来建模催化剂的enantioselectivity.
- 用3D pmapper描述符编码的,作为合并器的集合表示的催化剂.
- 编码化学转换作为分子图形与2D片段描述器.
- 综合催化剂和转化描述符,用于全面编码反应.
主要成果:
- 成功证明了拟议的方法在模拟同质和相转移反应的酶选择性.
- 多实例学习方法有效地处理了多个催化剂对象.
- 性能与现有的最先进的计算方法进行了比较.
结论:
- 拟议的多实例学习方法为设计性有机催化剂提供了一个有前途的计算策略.
- 这种方法通过避免符合性选择和空间对齐来简化建模过程.
- 这种方法有可能显著减少在制备乙纯化合物的资源.
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