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Navigating with chemometrics and machine learning in chemistry.

Payal B Joshi1

  • 1Operations and Method Development, Shefali Research Laboratories, Ambernath (East), Thane, Maharashtra 421501 India.

Artificial Intelligence Review
|January 30, 2023
PubMed
Summary

Chemometrics and machine learning (ML) are revolutionizing chemistry, aiding organic synthesis, drug discovery, and analytical techniques. Challenges like data availability and reproducibility remain key concerns for ML applications in chemistry.

Keywords:
AutomationChemometricsExpert systemsMachine learningRetrosynthesis

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Area of Science:

  • * Artificial intelligence, specifically chemometrics and machine learning (ML), is driving significant advancements across various chemical disciplines.
  • * Focus areas include organic synthesis, drug discovery, and analytical techniques, highlighting the broad impact of these computational methods.

Background:

  • * The integration of ML into chemistry is accelerating, yet complex data relationships pose significant challenges.
  • * Data availability and reproducibility are critical hurdles for the successful application of ML in chemical research.
  • * Despite growing literature, applying ML effectively in chemistry is not always straightforward.

Purpose of the Study:

  • * To review chemometric methods, expert systems, and ML techniques applied to organic synthesis and drug discovery.
  • * To discuss the deployment of chemometrics and ML in analytical techniques like spectroscopy, microscopy, and chromatography.
  • * To explore the challenges, opportunities, and future outlook of ML and automation in chemistry.

Main Methods:

  • * Review of existing literature on chemometrics, expert systems, and machine learning applications in chemistry.
  • * Analysis of selected examples in organic synthesis and drug discovery.
  • * Discussion of ML applications in analytical chemistry, including spectroscopy, microscopy, and chromatography.

Main Results:

  • * Chemometrics and ML offer powerful tools for addressing complex problems in organic synthesis and drug discovery.
  • * These AI methods are increasingly utilized in analytical techniques, enhancing data interpretation and efficiency.
  • * The review highlights both the successes and the persistent challenges in applying ML to chemical problems.

Conclusions:

  • * Machine learning and automation present significant opportunities for advancing chemical research and development.
  • * Addressing data availability, reproducibility, and interpretability is crucial for the future of ML in chemistry.
  • * The review concludes by posing critical questions regarding the broader implementation and navigation of ML across diverse chemical fields.