Tools for Synthesis Planning, Automation, and Analytical Data Analysis

Authors

  • Miruna Cretu IBM Research Europe
  • Marvin Alberts IBM Research Europe
  • Anubhab Chakraborty IBM Research Europe https://orcid.org/0000-0002-3487-844X
  • Artem Leonov IBM Research Europe
  • Amol Thakkar IBM Research Europe; National Center for Competence in Research Catalysis (NCCR) https://orcid.org/0000-0003-0403-4067
  • Teodoro Laino IBM Research Europe; National Center for Competence in Research - Catalysis (NCCR)

DOI:

https://doi.org/10.2533/chimia.2023.17

PMID:

38047849

Keywords:

Analytical Chemistry, Automation, Cheminformatics, Synthesis planning

Abstract

Computer-aided synthesis design, automation, and analytics assisted by machine learning are promising resources in the researcher’s toolkit. Each component may alleviate the chemist from routine tasks, provide valuable insights from data, and enable more informed experimental design. Herein, we highlight selected works in the field and discuss the different approaches and the problems to which they may apply. We emphasize that there are currently few tools with a low barrier of entry for non-experts, which may limit widespread integration into the researcher’s workflow.

Funding data

CHIMIA Vol. 77 No. 1/2 (2023): Artificial Intelligence in Chemical Research

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Published

2023-02-22

Issue

Vol. 77 No. 1/2 (2023): Artificial Intelligence in Chemical Research

Section

Scientific Articles

License

Copyright (c) 2023 Miruna Cretu, Marvin Alberts, Anubhab Chakraborty, Artem Leonov, Amol Thakkar, Teodoro Laino

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
M. Cretu, M. Alberts, A. Chakraborty, A. Leonov, A. Thakkar, T. Laino, Chimia 2023, 77, 17, DOI: 10.2533/chimia.2023.17.