From 2015 to 2023: How Machine Learning Aids Natural Product Analysis

TL;DR

This review discusses how machine learning techniques have evolved from 2015 to 2023 to enhance natural product analysis, addressing limitations of traditional chemistry methods and proposing a new research framework.

Contribution

It provides a comprehensive overview of computational strategies and introduces a novel perspective on integrating machine learning with natural product chemistry.

Findings

Machine learning models improve analysis of complex natural products.

Computational methods address limitations of conventional chemistry techniques.

A new research framework for qualitative and quantitative analysis is proposed.

Abstract

In recent years, conventional chemistry techniques have faced significant challenges due to their inherent limitations, struggling to cope with the increasing complexity and volume of data generated in contemporary research endeavors. Computational methodologies represent robust tools in the field of chemistry, offering the capacity to harness potent machine-learning models to yield insightful analytical outcomes. This review delves into the spectrum of computational strategies available for natural product analysis and constructs a research framework for investigating both qualitative and quantitative chemistry problems. Our objective is to present a novel perspective on the symbiosis of machine learning and chemistry, with the potential to catalyze a transformation in the field of natural product analysis.