Laboratory of Regulatory Science, College of Pharmaceutical Sciences, Ritsumeikan University.
Chem Pharm Bull (Tokyo). 2023;71(7):486-494. doi: 10.1248/cpb.c23-00039.
Computational approaches to drug development are rapidly growing in popularity and have been used to 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.
计算药物开发方法越来越受欢迎，并已被用于产生重大成果。信息科学的最新发展扩大了与天然产物相关的数据库和化学信息学知识。天然产物长期以来一直受到广泛研究，已经报道了大量独特的结构和显著的活性物质。利用新兴的计算科学技术分析积累的天然产物知识，预计会有更多的新发现。本文讨论了使用机器学习进行天然产物研究的现状。总结了机器学习的基本概念和框架。根据探索活性化合物、自动化合物设计以及应用于光谱数据的情况，描述了利用机器学习进行的天然产物研究。此外，还将探讨针对难治性疾病开发药物的努力。最后，我们讨论了在该领域应用机器学习的关键考虑因素。本文旨在通过介绍计算科学和化学信息学方法在该领域的应用、优势、局限性以及对该领域的影响，促进天然产物研究的进展。
