Ryzhkov Fedor V, Ryzhkova Yuliya E, Elinson Michail N
N. D. Zelinsky Institute of Organic Chemistry Russian Academy of Sciences, 47 Leninsky Prospekt, Moscow, 119991, Russia.
Mol Divers. 2024 May 14. doi: 10.1007/s11030-024-10889-7.
In recent decades, the use of computational approaches and artificial intelligence in the scientific environment has become more widespread. In this regard, the popular and versatile programming language Python has attracted considerable attention from scientists in the field of chemistry. It is used to solve a variety of chemical and structural problems, including calculating descriptors, molecular fingerprints, graph construction, and computing chemical reaction networks. Python offers high-quality visualization tools for analyzing chemical spaces and compound libraries. This review is a list of tools for the above tasks, including scripts, libraries, ready-made programs, and web interfaces. Inevitably this manuscript does not claim to be an all-encompassing handbook including all the existing Python-based structural chemistry codes. The review serves as a starting point for scientists wishing to apply automatization or optimization to routine chemistry problems.
近几十年来，计算方法和人工智能在科学环境中的应用越来越广泛。在这方面，流行且通用的编程语言Python引起了化学领域科学家的广泛关注。它被用于解决各种化学和结构问题，包括计算描述符、分子指纹、构建图形以及计算化学反应网络。Python提供了用于分析化学空间和化合物库的高质量可视化工具。本综述列出了用于上述任务的工具，包括脚本、库、现成的程序和网络接口。不可避免地，本手稿并不声称是一本涵盖所有现有基于Python的结构化学代码的全面手册。该综述为希望将自动化或优化应用于常规化学问题的科学家提供了一个起点。
