Computer Science Technology in Natural Products Research: A Review of Its Applications and Implications

Abstract

Computational approaches to drug development are rapidly growing in popularity and have been usedto produce significant results. Recent developments in information science have expanded databases and chemical informatics knowledge relating to natural products. Natural products have long been well-studied, and a large number of unique structures and remarkable active substances have been reported. Analyzing accumulated natural product knowledge using emerging computational science techniques is expected to yield more new discoveries. In this article, we discuss the current state of natural product research using machine learning. The basic concepts and frameworks of machine learning are summarized. Natural product research that utilizes machine learning is described in terms of the exploration of active compounds, automatic compound design, and application to spectral data. In addition, efforts to develop drugs for intractable diseases will be addressed. Lastly, we discuss key considerations for applying machine learning in this field. This paper aims to promote progress in natural product research by presenting the current state of computational science and chemoinformatics approaches in terms of its applications, strengths, limitations, and implications for the field.

Twenty-five years of natural products research in NuBBE

“…Artificial intelligence, allied with data science and machine learning algorithms are an asset to recognize molecular features and establish correlations within large data sets, helping to identify structure-activity relationships and predict biological activities (Shi et al, 2023). Together with this, in silico simulations are progressively being used for the investigation of complex molecular interactions in a virtual environment (Ogawa et al, 2023). Combined with the laboratory tests to which the extracts are submitted, it is possible to explore interactions between compounds and biological targets, study thermodynamic and kinetic properties, as well as predict the stability and toxicity of potential candidates from natural products (Gaudêncio et al, 2023).…”