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Python tools for structural tasks in chemistry

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Abstract

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.

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References

  1. Python programming language. http://Python.org. Accessed 25 Nov 2023

  2. Chirila DB, Lohmann G (2015). Introduction to Modern FORTRAN for the Earth System Sciences; Springer Berlin Heidelberg: Berlin, Heidelberg, ISBN 9783642370083

  3. Ryzhkov FV, Ryzhkova YE, Elinson MN (2023) Python in chemistry: physicochemical tools. Processes 11:2897. https://doi.org/10.3390/pr11102897

    Article  CAS  Google Scholar 

  4. Morita S (2020) Chemometrics and related fields in Python. Anal Sci 36:107–111. https://doi.org/10.2116/analsci.19r006

    Article  CAS  PubMed  Google Scholar 

  5. Baskin II, Madzhidov TI, Antipin IS, Varnek AA (2017) Artificial intelligence in synthetic chemistry: achievements and prospects. Russ Chem Rev 86:1127–1156. https://doi.org/10.1070/rcr4746

    Article  CAS  Google Scholar 

  6. Varnek A, Tropsha A, editors (2008) Chemoinformatics approaches to virtual screening. The Royal Society of Chemistry. https://doi.org/10.1039/9781847558879

  7. Varnek A, Baskin II (2011) Chemoinformatics as a theoretical chemistry discipline. Mol Inform 30:20–32. https://doi.org/10.1002/minf.201000100

    Article  CAS  PubMed  Google Scholar 

  8. Chemoinformatics in Drug Discovery: Oprea:Chemoinformatics o-Bk (2005) Oprea, T.I., Ed.; Wiley-VCH Verlag: Weinheim, Germany; ISBN 9783527307531

  9. Walters P.W. Chemoinformatics for Drug Discovery. 1st ed. (Eds J. Bajorath.) (2014) John Wiley & Sons, Nashville. P. 1

  10. F. Ntie-Kang fundamental concepts: of natural products. (Eds F. Ntie-Kang) (2022) De Gruyter, Berlin. P. 1.

  11. Nugmanov RI, Mukhametgaleev RN, Akhmetshin T, Gimadiev TR, Afonina VA, Madzhidov TI, Varnek A (2019) CGRtools: python library for molecule, reaction, and condensed graph of reaction processing. J Chem Inf Model 59:2516–2521. https://doi.org/10.1021/acs.jcim.9b00102

    Article  CAS  PubMed  Google Scholar 

  12. RDKit: Open-source cheminformatics (2023) http://www.rdkit.org. Accessed 28 Nov 2023

  13. Terlouw BR, Vromans SPJM, Medema MH (2022) PIKAChU: a python-based informatics kit for analysing chemical units. J Cheminform. https://doi.org/10.1186/s13321-022-00616-5

    Article  PubMed  PubMed Central  Google Scholar 

  14. Shao Y, Hellström M, Mitev PD, Knijff L, Zhang C (2020) PiNN: a python library for building atomic neural networks of molecules and materials. J Chem Inf Model 60:1184–1193. https://doi.org/10.1021/acs.jcim.9b00994

    Article  CAS  PubMed  Google Scholar 

  15. O’Boyle NM, Morley C, Hutchison GR (2008) Pybel: a python wrapper for the openbabel cheminformatics toolkit. Chem Cent J. https://doi.org/10.1186/1752-153x-2-5

    Article  PubMed  PubMed Central  Google Scholar 

  16. Verstraelen T, Adams W, Pujal L, Tehrani A, Kelly BD, Macaya L, Meng F, Richer M, Hernández-Esparza R, Yang XD et al (2021) IOData: a python library for reading, writing, and converting computational chemistry file formats and generating input files. J Comput Chem 42:458–464. https://doi.org/10.1002/jcc.26468

    Article  CAS  PubMed  Google Scholar 

  17. Tudoran M, Putz M (2015) Molecular graph theory: from adjacency information to colored topology by chemical reactivity. Curr Org Chem 19(4):359–386. https://doi.org/10.2174/1385272819666141216232941

    Article  CAS  Google Scholar 

  18. Saldívar-González FI, Huerta-García CS, Medina-Franco JL (2020) Chemoinformatics-based enumeration of chemical libraries: a tutorial. J Cheminform. https://doi.org/10.1186/s13321-020-00466-z

    Article  PubMed  PubMed Central  Google Scholar 

  19. Heller SR, McNaught A, Pletnev I, Stein S, Tchekhovskoi D (2015) InChI, the IUPAC International Chemical Identifier. J Cheminform. https://doi.org/10.1186/s13321-015-0068-4

    Article  PubMed  PubMed Central  Google Scholar 

  20. Weininger D, Weininger A, Weininger JL (1989) SMILES. 2. Algorithm for generation of unique SMILES notation. J Chem Inf Comput Sci 29:97–101. https://doi.org/10.1021/ci00062a008

    Article  CAS  Google Scholar 

  21. Weininger D (1988) SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules. J Chem Inf Comput Sci 28:31–36. https://doi.org/10.1021/ci00057a005

    Article  CAS  Google Scholar 

  22. David L, Thakkar A, Mercado R, Engkvist O (2020) Molecular representations in AI-driven drug discovery: a review and practical guide. J Cheminform. https://doi.org/10.1186/s13321-020-00460-5

    Article  PubMed  PubMed Central  Google Scholar 

  23. Krenn M, Ai Q, Barthel S, Carson N, Frei A, Frey NC, Friederich P, Gaudin T, Gayle AA, Jablonka KM et al (2022) SELFIES and the future of molecular string representations. Patterns 3:100588. https://doi.org/10.1016/j.patter.2022.100588

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Daylight theory: SMARTS - A language for describing molecular patterns. (2023) https://www.daylight.com/dayhtml/doc/theory/theory.smarts.html. Accessed 25 Nov 2023

  25. Kravtsov AA, Karpov PV, Baskin II, Palyulin VA, Zefirov NS (2007) “Bimolecular” QSPR: estimation of the solvation free energy of organic molecules in different solvents. Dokl Chem 414:128–131. https://doi.org/10.1134/s0012500807050072

    Article  CAS  Google Scholar 

  26. Sosnin S, Vashurina M, Withnall M, Karpov P, Fedorov M, Tetko IV (2019) A survey of multi-task learning methods in chemoinformatics. Mol Inform. https://doi.org/10.1002/minf.201800108

    Article  PubMed  Google Scholar 

  27. Zankov D, Madzhidov T, Baskin I, Varnek A (2023) Conjugated quantitative structure-property relationship models: prediction of kinetic characteristics linked by the Arrhenius Equation. Mol Inform. https://doi.org/10.1002/minf.202200275

    Article  PubMed  Google Scholar 

  28. Schneider G, Neidhart W, Giller T, Schmid G (1999) “Scaffold-Hopping” by topological pharmacophore search: a contribution to virtual screening. Angew Chem Int Ed Engl. 38, 2894–2896, https://doi.org/10.1002/(sici)1521-3773(19991004)38:19<2894::aid-anie2894>3.0.co;2-f.

  29. Di Palma F, Abate C, Decherchi S, Cavalli A (2023) Ligandability and druggability assessment via machine learning. Wiley Interdiscip Rev Comput Mol Sci 13:e1676. https://doi.org/10.1002/wcms.1676

    Article  Google Scholar 

  30. Getting started with the RDKit in python (2023) https://rdkit.org/docs/GettingStartedInPython.html. Accessed 25 Nov 2023

  31. Dong J, Yao Z-J, Zhang L, Luo F, Lin Q, Lu A-P, Chen AF, Cao D-S (2018) PyBioMed: a python library for various molecular representations of chemicals, proteins and DNAs and their interactions. J Cheminform. https://doi.org/10.1186/s13321-018-0270-2

    Article  PubMed  PubMed Central  Google Scholar 

  32. Bickerton GR, Paolini GV, Besnard J, Muresan S, Hopkins AL (2012) Quantifying the chemical beauty of drugs. Nat Chem 4:90–98. https://doi.org/10.1038/nchem.1243

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Sosnina EA, Osolodkin DI, Radchenko EV, Sosnin S, Palyulin VA (2018) Influence of descriptor implementation on compound ranking based on multiparameter assessment. J Chem Inf Model 58:1083–1093. https://doi.org/10.1021/acs.jcim.7b00734

    Article  CAS  PubMed  Google Scholar 

  34. Cao D-S, Xu Q-S, Hu Q-N, Liang Y-Z (2013) ChemoPy: freely available python package for computational biology and chemoinformatics. Bioinformatics 29:1092–1094. https://doi.org/10.1093/bioinformatics/btt105

    Article  CAS  PubMed  Google Scholar 

  35. Cao D-S, Xu Q-S, Liang Y-Z (2013) Propy: a tool to generate various modes of Chou’s PseAAC. Bioinformatics 29:960–962. https://doi.org/10.1093/bioinformatics/btt072

    Article  CAS  PubMed  Google Scholar 

  36. Cao D-S, Liang Y-Z, Yan J, Tan G-S, Xu Q-S, Liu S (2013) PyDPI: freely available python package for chemoinformatics, bioinformatics, and chemogenomics studies. J Chem Inf Model 53:3086–3096. https://doi.org/10.1021/ci400127q

    Article  CAS  PubMed  Google Scholar 

  37. O’Boyle NM, Hutchison GR (2008) Cinfony – combining open source cheminformatics toolkits behind a common interface. Chem Cent J. https://doi.org/10.1186/1752-153x-2-24

    Article  PubMed  PubMed Central  Google Scholar 

  38. Masand VH, Rastija V (2017) PyDescriptor : a new PyMOL plugin for calculating thousands of easily understandable molecular descriptors. Chemometr Intell Lab Syst 169:12–18. https://doi.org/10.1016/j.chemolab.2017.08.003

    Article  CAS  Google Scholar 

  39. Kristensen TG, Nielsen J, Pedersen CNS (2010) A tree-based method for the rapid screening of chemical fingerprints. Algorithms Mol Biol. https://doi.org/10.1186/1748-7188-5-9

    Article  PubMed  PubMed Central  Google Scholar 

  40. Willett P (2006) Similarity-based virtual screening using 2D fingerprints. Drug Discov Today 11:1046–1053. https://doi.org/10.1016/j.drudis.2006.10.005

    Article  CAS  PubMed  Google Scholar 

  41. Morgan HL (1965) The generation of a unique machine description for chemical structures-A technique developed at chemical abstracts service. J Chem Doc 5:107–113. https://doi.org/10.1021/c160017a018

    Article  CAS  Google Scholar 

  42. Rogers D, Hahn M (2010) Extended-connectivity fingerprints. J Chem Inf Model 50:742–754. https://doi.org/10.1021/ci100050t

    Article  CAS  PubMed  Google Scholar 

  43. Amigó JM, Gálvez J, Villar VM (2009) A review on molecular topology: applying graph theory to drug discovery and design. Sci Nat 96:749–761. https://doi.org/10.1007/s00114-009-0536-7

    Article  CAS  Google Scholar 

  44. Brammer JC, Blanke G, Kellner C, Hoffmann A, Herres-Pawlis S, Schatzschneider U (2022) TUCAN: a molecular identifier and descriptor applicable to the whole periodic table from hydrogen to oganesson. J Cheminform. https://doi.org/10.1186/s13321-022-00640-5

    Article  PubMed  PubMed Central  Google Scholar 

  45. Deshpande S, Maxson T, Greeley J (2020) Graph theory approach to determine configurations of multidentate and high coverage adsorbates for heterogeneous catalysis. Npj Comput Mater. https://doi.org/10.1038/s41524-020-0345-2

    Article  Google Scholar 

  46. Pastick NJ, Duffy P, Genet H, Rupp TS, Wylie BK, Johnson KD, Jorgenson MT, Bliss N, McGuire AD, Jafarov EE et al (2017) Historical and projected trends in landscape drivers affecting carbon dynamics in Alaska. Ecol Appl 27:1383–1402. https://doi.org/10.1002/eap.1538

    Article  PubMed  Google Scholar 

  47. Gimadiev T, Nugmanov R, Khakimova A, Fatykhova A, Madzhidov T, Sidorov P, Varnek A (2022) CGRdb2.0: a python database management system for molecules, reactions, and chemical data. J Chem Inf Model 62:2015–2020. https://doi.org/10.1021/acs.jcim.1c01105

    Article  CAS  PubMed  Google Scholar 

  48. Teixeira AL, Falcao AO (2013) Noncontiguous atom matching structural similarity function. J Chem Inf Model 53:2511–2524. https://doi.org/10.1021/ci400324u

    Article  CAS  PubMed  Google Scholar 

  49. Matter H, Buning C, Stefanescu DD, Ruf S, Hessler G (2020) Using graph databases to investigate trends in structure-activity relationship networks. J Chem Inf Model 60:6120–6134. https://doi.org/10.1021/acs.jcim.0c00947

    Article  CAS  PubMed  Google Scholar 

  50. Hemmateenejad B, Sanchooli M, Mehdipour A (2009) Quantitative structure-reactivity relationship studies on the catalyzed Michael addition reactions. J Phys Org Chem 22:613–618. https://doi.org/10.1002/poc.1491

    Article  CAS  Google Scholar 

  51. Wilcox CF Jr, Carpenter BK (1979) Quantitative prediction of structure-reactivity relationships for unimolecular reactions of unsaturated hydrocarbons. Development of a semiempirical model. J Am Chem Soc 101:3897–3905. https://doi.org/10.1021/ja00508a030

    Article  CAS  Google Scholar 

  52. Dugundji J, Ugi I (1971) Fortschritte Der Chemischen Forschung. Springer-Verlag, Berlin, p 19

    Google Scholar 

  53. Schneider N, Lowe DM, Sayle RA, Landrum GA (2015) Development of a novel fingerprint for chemical reactions and its application to large-scale reaction classification and similarity. J Chem Inf Model 55:39–53. https://doi.org/10.1021/ci5006614

    Article  CAS  PubMed  Google Scholar 

  54. Ridder L, Wagener M (2008) SyGMa: combining expert knowledge and empirical scoring in the prediction of metabolites. ChemMedChem 3:821–832. https://doi.org/10.1002/cmdc.200700312

    Article  CAS  PubMed  Google Scholar 

  55. Zefirov NS, Tratch SS (1980) Systematization of tautomeric processes and formal-logical approach to the search for new topological and reaction types of tautomerism. Chem Scr 15:4–12

    CAS  Google Scholar 

  56. Varnek A, Fourches D, Hoonakker F, Solov’ev VP (2005) Substructural fragments: an universal language to encode reactions, molecular and supramolecular structures. J Comput Aided Mol Des 19:693–703. https://doi.org/10.1007/s10822-005-9008-0

    Article  CAS  PubMed  Google Scholar 

  57. Wen M, Spotte-Smith EWC, Blau SM, McDermott MJ, Krishnapriyan AS, Persson KA (2023) Chemical reaction networks and opportunities for machine learning. Nat Comput Sci 3:12–24. https://doi.org/10.1038/s43588-022-00369-z

    Article  PubMed  Google Scholar 

  58. Saebi M, Nan B, Herr J, Wahlers J, Wiest O, Chawla N (2021) Graph neural networks for predicting chemical reaction performance. ChemRxiv. https://doi.org/10.26434/chemrxiv.14589498.v2

    Article  Google Scholar 

  59. Feigelman J, Weindl D, Theis FJ, Marr C, Hasenauer J (2018) LNA++: linear noise approximation with first and second order sensitivities. In: Computational methods in systems biology; Springer International Publishing: Cham: pp. 300–306 ISBN 9783319994284. Doi: https://doi.org/10.1007/978-3-319-99429-1_19

  60. Malpica Galassi R (2022) PyCSP: a python package for the analysis and simplification of chemically reacting systems based on computational singular perturbation. Comput Phys Commun 276:108364. https://doi.org/10.1016/j.cpc.2022.108364

    Article  CAS  Google Scholar 

  61. Wei JN, Duvenaud D, Aspuru-Guzik A (2016) Neural networks for the prediction of organic chemistry reactions. ACS Cent Sci 2:725–732. https://doi.org/10.1021/acscentsci.6b00219

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Madzhidov TI, Rakhimbekova A, Afonina VA, Gimadiev TR, Mukhametgaleev RN, Nugmanov RI, Baskin II, Varnek A (2021) Machine learning modelling of chemical reaction characteristics: yesterday, today, tomorrow. Mendeleev Commun 31:769–780. https://doi.org/10.1016/j.mencom.2021.11.003

    Article  CAS  Google Scholar 

  63. Behler J, Parrinello M (2007) Generalized neural-network representation of high-dimensional potential-energy surfaces. Phys Rev Lett. https://doi.org/10.1103/physrevlett.98.146401

    Article  PubMed  Google Scholar 

  64. Gupta U, Vlachos DG (2021) Learning chemistry of complex reaction systems via a python first-principles reaction rule stencil (pReSt) generator. J Chem Inf Model 61:3431–3441. https://doi.org/10.1021/acs.jcim.1c00297

    Article  CAS  PubMed  Google Scholar 

  65. Vogt-Geisse S (2016) Kudi: a free open-source python library for the analysis of properties along reaction paths. J Mol Model. https://doi.org/10.1007/s00894-016-2983-3

    Article  PubMed  Google Scholar 

  66. Lenci E, Trabocchi A (2022) Diversity-oriented synthesis and chemoinformatics: a fruitful synergy towards better chemical libraries. Eur J Org Chem. https://doi.org/10.1002/ejoc.202200575

    Article  Google Scholar 

  67. Wang Z, Zhang W, Liu B (2021) Computational analysis of synthetic planning: past and future. Chin J Chem 39:3127–3143. https://doi.org/10.1002/cjoc.202100273

    Article  CAS  Google Scholar 

  68. Gimadiev T, Nugmanov R, Batyrshin D, Madzhidov T, Maeda S, Sidorov P, Varnek A (2021) Combined graph/relational database management system for calculated chemical reaction pathway data. J Chem Inf Model 61:554–559. https://doi.org/10.1021/acs.jcim.0c01280

    Article  CAS  PubMed  Google Scholar 

  69. Kannas C, Genheden S (2022) Rxnutils – a cheminformatics python library for manipulating chemical reaction data. ChemRxiv. https://doi.org/10.26434/chemrxiv-2022-wt440

    Article  Google Scholar 

  70. Isamura BK, Lobb KA (2022) AMADAR: a python-based package for large scale prediction of diels-alder transition state geometries and IRC path analysis. J Cheminform. https://doi.org/10.1186/s13321-022-00618-3

    Article  PubMed  PubMed Central  Google Scholar 

  71. Osolodkin DI, Radchenko EV, Orlov AA, Voronkov AE, Palyulin VA, Zefirov NS (2015) Progress in visual representations of chemical space. Expert Opin Drug Discov 10:959–973. https://doi.org/10.1517/17460441.2015.1060216

    Article  CAS  PubMed  Google Scholar 

  72. Hanwell MD, Curtis DE, Lonie DC, Vandermeersch T, Zurek E, Hutchison GR (2012) Avogadro: an advanced semantic chemical editor, visualization, and analysis platform. J Cheminform. https://doi.org/10.1186/1758-2946-4-17

    Article  PubMed  PubMed Central  Google Scholar 

  73. Schrödinger, L., & DeLano, W. (2020). PyMOL.. http://pymol.org/pymol. Accessed 25 Nov 2023

  74. Tao Y, Zou W, Nanayakkara S, Kraka E (2020) PyVibMS: a PyMOL plugin for visualizing vibrations in molecules and solids. J Mol Model. https://doi.org/10.1007/s00894-020-04508-z

    Article  PubMed  Google Scholar 

  75. Scalfani VF, Patel VD, Fernandez AM (2022) Visualizing chemical space networks with RDKit and NetworkX. J Cheminform. https://doi.org/10.1186/s13321-022-00664-x

    Article  PubMed  PubMed Central  Google Scholar 

  76. Cihan Sorkun M, Mullaj D, Koelman JMVA, Er S (2022) ChemPlot, a python library for chemical space visualization. Chem Methods. https://doi.org/10.1002/cmtd.202200005

    Article  Google Scholar 

  77. Sicho M, Liu X, Svozil D, van Westen GJP (2021) GenUI: interactive and extensible open source software platform for de novo molecular generation and cheminformatics. J Cheminform. https://doi.org/10.1186/s13321-021-00550-y

    Article  PubMed  PubMed Central  Google Scholar 

  78. Cortes-Ciriano I (2016) Bioalerts: a python library for the derivation of structural alerts from bioactivity and toxicity data sets. J Cheminform. https://doi.org/10.1186/s13321-016-0125-7

    Article  PubMed  PubMed Central  Google Scholar 

  79. Brown SD, Tauler R, Walczak B (2009) Comprehensive chemometrics: chemical and biochemical data analysis, vol 4. Elsevier, Amsterdam, p 3

    Google Scholar 

  80. Bajorath J, Chávez-Hernández AL, Duran-Frigola M et al (2022) Chemoinformatics and artificial intelligence colloquium: progress and challenges in developing bioactive compounds. J Cheminform 14:82. https://doi.org/10.1186/s13321-022-00661-0

    Article  PubMed  PubMed Central  Google Scholar 

  81. Wang Y, Chen T-Y, Vlachos DG (2021) NEXTorch: a design and Bayesian optimization toolkit for chemical sciences and engineering. J Chem Inf Model 61:5312–5319. https://doi.org/10.1021/acs.jcim.1c00637

    Article  CAS  PubMed  Google Scholar 

  82. Soritz S, Moser D, Gruber-Wölfler H (2022) Comparison of derivative-free algorithms for their applicability in self-optimization of chemical processes. Chem Methods. https://doi.org/10.1002/cmtd.202100091

    Article  Google Scholar 

  83. Camp CH (2019) PyMCR: a python library for multivariate curve resolution analysis with alternating regression (MCR-AR). J Res Natl Inst Stand Technol. https://doi.org/10.6028/jres.124.018

    Article  PubMed  PubMed Central  Google Scholar 

  84. 2Dpy (2023), https://github.com/shigemorita/2Dpy. Accessed 25 Nov 2023

  85. Rodrigues JPGLM, Teixeira JMC, Trellet M, Bonvin AMJJ (2018) Pdb-tools: a Swiss army knife for molecular structures. F1000Res 7:1961. https://doi.org/10.12688/f1000research.17456.1

    Article  PubMed  PubMed Central  Google Scholar 

  86. Izumi H, Nafie LA, Dukor RK (2016) Three-dimensional chemical structure search using the conformational code for organic molecules (CCOM) program. Chirality 28:370–375. https://doi.org/10.1002/chir.22596

    Article  CAS  PubMed  Google Scholar 

  87. pyDOE2 (2023), https://github.com/clicumu/pyDOE2. Accessed 25 Nov 2023

  88. Nextmovesoftware (2023). CaffeineFix. https://Anextmovesoftware.com/caffeinefix.html. Accessed 25 Nov 2023

  89. Gali H (2017) An open-source automated peptide synthesizer based on arduino and python. SLAS Technol. 22:493–499. https://doi.org/10.1177/2472630316685844

    Article  PubMed  Google Scholar 

  90. O’Brien M, Konings L, Martin M, Heap J (2017) Harnessing open-source technology for low-cost automation in synthesis: flow chemical deprotection of silyl ethers using a homemade autosampling system. Tetrahedron Lett 58:2409–2413. https://doi.org/10.1016/j.tetlet.2017.05.008

    Article  CAS  Google Scholar 

  91. Luchini G, Ascough DMH, Alegre-Requena JV, Gouverneur V, Paton RS (2019) Data-mining the Diaryl(Thio)urea conformational landscape: understanding the contrasting behavior of ureas and thioureas with quantum chemistry. Tetrahedron 75:697–702. https://doi.org/10.1016/j.tet.2018.12.033

    Article  CAS  Google Scholar 

  92. Ryzhkova YE, Elinson MN, Vereshchagin AN, Kalashnikova VM, Korolev VA, Ryzhkov FV, Egorov MP (2022) Green electrocatalytic assembling of salicylaldehydes, kojic acid, and malonic acid derivatives into 2-amino-4h-chromenes as potent anti-inflammatory agents. ChemistrySelect. https://doi.org/10.1002/slct.202202872

    Article  Google Scholar 

  93. Ryzhkova YE, Elinson MN, Vereshchagin AN, Karpenko KA, Ryzhkov FV, Ushakov IE, Egorov MP (2022) Multicomponent electrocatalytic selective approach to unsymmetrical spiro[Furo[3,2-c]Pyran-2,5′-Pyrimidine] scaffold under a column chromatography-free protocol at room temperature. Chemistry 4:615–629. https://doi.org/10.3390/chemistry4020044

    Article  CAS  Google Scholar 

  94. Lafuente D, Cohen B, Fiorini G, García AA, Bringas M, Morzan E, Onna DA (2021) Gentle introduction to machine learning for chemists: an undergraduate workshop using python notebooks for visualization, data processing, analysis, and modeling. J Chem Educ 98:2892–2898. https://doi.org/10.1021/acs.jchemed.1c00142

    Article  CAS  Google Scholar 

  95. Weiss CJ (2017) Scientific computing for chemists: an undergraduate course in simulations, data processing, and visualization. J Chem Educ 94:592–597. https://doi.org/10.1021/acs.jchemed.7b00078

    Article  CAS  Google Scholar 

  96. Sydow D, Morger A, Driller M, Volkamer A (2019) TeachOpenCADD: a teaching platform for computer-aided drug design using open source packages and data. J Cheminform. https://doi.org/10.1186/s13321-019-0351-x

    Article  PubMed  PubMed Central  Google Scholar 

  97. Deutsch JM (2014) Biophysics software for interdisciplinary education and research. Am J Phys 82:442–450. https://doi.org/10.1119/1.4869198

    Article  CAS  Google Scholar 

  98. Engelberger F, Galaz-Davison P, Bravo G, Rivera M, Ramírez-Sarmiento CA (2021) Developing and implementing cloud-based tutorials that combine bioinformatics software, interactive coding, and visualization exercises for distance learning on structural bioinformatics. J Chem Educ 98:1801–1807. https://doi.org/10.1021/acs.jchemed.1c00022

    Article  CAS  Google Scholar 

  99. Bravenec AD, Ward KD (2023) Interactive python notebooks for physical chemistry. J Chem Educ 100:933–940. https://doi.org/10.1021/acs.jchemed.2c00665

    Article  CAS  Google Scholar 

  100. Weiss CJ, Klose A (2021) Introducing students to scientific computing in the laboratory through python and jupyter notebooks. In: ACS Symposium Series; American Chemical Society: Washington, DC: pp. 57–67 ISBN 9780841298194.

  101. Möglich AA (2018) An open-source, cross-platform resource for nonlinear least-squares curve fitting. J Chem Educ 95:2273–2278. https://doi.org/10.1021/acs.jchemed.8b00649

    Article  CAS  Google Scholar 

  102. Grazioli G, Ingwerson A, Santiago D, Cho H, Regan P (2022) Foregrounding the Code: computational chemistry instructional activities using a highly readable fluid simulation code. ChemRxiv.

  103. Vargas S, Zamirpour S, Menon S, Rothman A, Häse F, Tamayo-Mendoza T, Romero J, Sim S, Menke T, Aspuru-Guzik A (2020) Team-based learning for scientific computing and automated experimentation: visualization of colored reactions. J Chem Educ 97:689–694. https://doi.org/10.1021/acs.jchemed.9b00603

    Article  CAS  Google Scholar 

  104. Weiss CJA (2021) Creative commons textbook for teaching scientific computing to chemistry students with python and Jupyter Notebooks. J Chem Educ 98:489–494. https://doi.org/10.1021/acs.jchemed.0c01071

    Article  CAS  Google Scholar 

  105. Dickson-Karn NM, Orosz S (2021) Implementation of a Python Program to Simulate Sampling. J Chem Educ 98:3251–3257. https://doi.org/10.1021/acs.jchemed.1c00597

    Article  CAS  Google Scholar 

  106. ZINC Sigma Aldrich (Building Blocks) (2023). http://zinc.docking.org/catalogs/sialbb/. Accessed 25 Nov 2023

  107. Hallal K, Hamdan R, Tlais S (2023) Exploring the potential of AI-Chatbots in organic chemistry: an assessment of ChatGPT and Bard. Comput Educ Artif Intell 5:100170. https://doi.org/10.1016/j.caeai.2023.100170

    Article  Google Scholar 

  108. Clark TM (2023) Investigating the use of an artificial intelligence Chatbot with general chemistry exam questions. J Chem Educ 100:1905–1916. https://doi.org/10.1021/acs.jchemed.3c00027

    Article  CAS  Google Scholar 

  109. Zheng Z, Zhang O, Borgs C, Chayes JT, Yaghi OM (2023) ChatGPT chemistry assistant for text mining and the prediction of MOF synthesis. J Am Chem Soc 145:18048–18062. https://doi.org/10.1021/jacs.3c05819

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  110. Project jupyter. In: Jupyter.org. https://jupyter.org/. Accessed 14 Apr 2024

  111. Schwalbe-Koda D, Gómez-Bombarelli R (2021) Supramolecular recognition in crystalline nanocavities through Monte Carlo and Voronoi network algorithms. J Phys Chem C Nanomater Interfaces 125:3009–3017. https://doi.org/10.1021/acs.jpcc.0c10108

    Article  CAS  Google Scholar 

  112. Tashiro M, Imamura Y, Katouda M (2021) De novo generation of optically active small organic molecules using Monte Carlo tree search combined with recurrent neural network. J Comput Chem 42:136–143. https://doi.org/10.1002/jcc.26441

    Article  CAS  PubMed  Google Scholar 

  113. Young TA, Gheorghe R, Duarte F (2020) Cgbind: a python module and web app for automated metallocage construction and host-guest characterization. J Chem Inf Model 60:3546–3557. https://doi.org/10.1021/acs.jcim.0c00519

    Article  CAS  PubMed  Google Scholar 

  114. van Beek B, Zito J, Visscher L, Infante I (2022) CAT: a compound attachment tool for the construction of composite chemical compounds. J Chem Inf Model 62:5525–5535. https://doi.org/10.1021/acs.jcim.2c00690

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  115. Turcani L, Berardo E, Jelfs KE (2018) Stk: a python toolkit for supramolecular assembly. J Comput Chem 39:1931–1942. https://doi.org/10.1002/jcc.25377

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  116. Fredericks S, Parrish K, Sayre D, Zhu Q (2021) PyXtal: a python library for crystal structure generation and symmetry analysis. Comput Phys Commun 261:107810. https://doi.org/10.1016/j.cpc.2020.107810

    Article  CAS  Google Scholar 

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  1. N. D. Zelinsky Institute of Organic Chemistry Russian Academy of Sciences, 47 Leninsky Prospekt, Moscow, 119991, Russia

    Fedor V. Ryzhkov, Yuliya E. Ryzhkova & Michail N. Elinson

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  1. Fedor V. Ryzhkov
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  2. Yuliya E. Ryzhkova
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  3. Michail N. Elinson
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Contributions

Conceptualization, F.V.R.; methodology, F.V.R.; validation, Y.E.R. and M.N.E.; formal analysis, F.V.R.; investigation, F.V.R.; data curation, Y.E.R. and M.N.E.; writing—original draft preparation, F.V.R.; writing—review and editing, Y.E.R. and M.N.E.; visualization, Y.E.R.; supervision, M.N.E. All authors have read and agreed to the published version of the manuscript.

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Correspondence to Fedor V. Ryzhkov.

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Ryzhkov, F.V., Ryzhkova, Y.E. & Elinson, M.N. Python tools for structural tasks in chemistry. Mol Divers (2024). https://doi.org/10.1007/s11030-024-10889-7

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