计算工具用于预测有机反应的地点和区域选择性
Lukas M Sigmund1, Michele Assante2,3, Magnus J Johansson4
1Molecular AI, Discovery Sciences, R&D, AstraZeneca Gothenburg Pepparedsleden 1 43183 Mölndal Sweden lukas.sigmund@astrazeneca.com mikhail.kabeshov@astrazeneca.com.
Chemical science
|March 12, 2025
概括
本综述涵盖了用于预测有机反应的区域选择性和地点选择性的计算工具. 它强调了机器学习模型及其对合成化学家和工具开发者的应用.
科学领域:
- 有机化学 有机化学
- 计算化学计算化学
- 化学信息学 化学信息学
背景情况:
- 区域和地点选择性对于计划有机合成至关重要.
- 选择性的预测模型已经有了显著的发展.
- 机器学习最近已经成为化学科学的强大工具.
研究的目的:
- 为区域和地点选择性提供当前预测工具的全面概述.
- 专注于机器学习模型在这个领域的应用和进步.
- 引导合成化学家和开发人员理解和利用这些计算工具.
主要方法:
- 对选择性预测的计算模型现有文献的审查.
- 详细检查机器学习方法,包括特色化技术和模型架构.
- 基于有机反应类别的工具的分类.
主要成果:
- 各种特色化技术和机器学习模型架构的比较.
- 突出这些预测工具在现实世界合成中的实际应用.
- 识别计算选择性预测领域的趋势和挑战.
结论:
- 机器学习模型在预测区域和地点选择性方面取得了重大进展.
- 这篇评论对于这个专业领域的用户和开发人员来说都是一个有价值的资源.
- 建议未来的研究途径进一步提高预测能力.
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