一种基因优化策略,以不对称的有机催化剂作为主要目标的普遍性
Simone Gallarati1, Puck van Gerwen1,2, Ruben Laplaza1,2
1Laboratory for Computational Molecular Design, Institute of Chemical Sciences and Engineering, Ecole Polytechnique Fédérale de Lausanne (EPFL) 1015 Lausanne Switzerland clemence.corminboeuf@epfl.ch.
我们开发了一种新的反向设计策略,使用遗传算法来发现不对称合成的通用催化剂. 这种方法同时优化了催化剂和基质范围,克服了传统选方法的局限性.
科学领域:
- 计算化学和化学信息学
- 非对称的有机催化剂.
- 催化剂的发现和优化.
背景情况:
- 一般的催化剂,表现出广泛的基质范围和高的酶选择性,在不对称合成中非常有价值,但很罕见,很难发现.
- 传统的高通量选是昂贵和有限的,而现有的计算工具主要是优化反应条件,而不是产生新的催化剂.
- 发现具有增强基板宽度的催化剂仍然是合成化学的一个重大挑战.
研究的目的:
- 引入一个反向设计策略,用于发现具有广泛基质范围和高选活性的通用催化剂.
- 为了同时优化催化剂和基质范围,将一般性作为主要的优化目标.
- 将这种策略应用于Pictet-Spengler凝结反应,用于合成四-β-碳醇蛋白产物.
主要方法:
- 利用了一个开源的遗传算法NaviCatGA,与OSCAR的有机催化剂数据库相结合.
- 策划了820个皮克特-斯勒凝结反应的数据库,以训练活动和选择性的统计模型.
- 在数以百万计的潜在催化剂和化学空间的代表性多样化的基质范围上进行了进化实验.
主要成果:
- 确定了在一系列基板上表现出卓越性能的"特权"催化剂.
- 在不对称合成中证明了基因优化在解决催化剂普遍性的有效性.
- 从具有挑战性的化学空间中提取了关键的结构-性能关系.
结论:
- 开发的反向设计策略成功地解决了发现一般催化剂的挑战.
- 这种计算方法可以有效地探索广的催化剂空间,以提高基板宽度.
- 这些发现为非对称合成中的催化剂设计提供了一个强大的新范式.
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