Python tools for structural tasks in chemistry

• Published in: Molecular diversity Vol. 29; no. 4; pp. 3733 - 3752
• Main Authors: Ryzhkov, Fedor V., Ryzhkova, Yuliya E., Elinson, Michail N.
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.08.2025; Springer Nature B.V
• Subjects: Algorithms; Artificial intelligence; Automation; Biochemistry; Biomedical and Life Sciences; Cheminformatics - methods; Chemistry; Comprehensive Review; Data analysis; Data processing; Graphs; Information storage; Libraries; Life Sciences; Linear algebra; Machine learning; Neural networks; Optimization; Organic Chemistry; Pharmacy; Polymer Sciences; Programming Languages; Python; Software; Visualization; Computational chemistry; Visualization; Molecular graphs; Automation using Python; Python; Structural chemistry
• ISSN: 1381-1991; 1573-501X; 1573-501X
• 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. Graphical Abstract