DIFACQUIM Research Group, Department of Pharmacy, School of Chemistry, Universidad Nacional Autónoma de México, Avenida Universidad 3000, Mexico City 04510, Mexico.
Biomolecules. 2020 Nov 17;10(11):1566. doi: 10.3390/biom10111566.
Natural products have a significant role in drug discovery. Natural products have distinctive chemical structures that have contributed to identifying and developing drugs for different therapeutic areas. Moreover, natural products are significant sources of inspiration or starting points to develop new therapeutic agents. Natural products such as peptides and macrocycles, and other compounds with unique features represent attractive sources to address complex diseases. Computational approaches that use chemoinformatics and molecular modeling methods contribute to speed up natural product-based drug discovery. Several research groups have recently used computational methodologies to organize data, interpret results, generate and test hypotheses, filter large chemical databases before the experimental screening, and design experiments. This review discusses a broad range of chemoinformatics applications to support natural product-based drug discovery. We emphasize profiling natural product data sets in terms of diversity; complexity; acid/base; absorption, distribution, metabolism, excretion, and toxicity (ADME/Tox) properties; and fragment analysis. Novel techniques for the visual representation of the chemical space are also discussed.
天然产物在药物发现中具有重要作用。天然产物具有独特的化学结构,有助于鉴定和开发用于不同治疗领域的药物。此外,天然产物是开发新治疗剂的重要灵感来源或起点。天然产物,如肽和大环化合物,以及其他具有独特特征的化合物,是解决复杂疾病的有吸引力的来源。使用化学信息学和分子建模方法的计算方法有助于加快基于天然产物的药物发现。最近,几个研究小组使用计算方法来组织数据、解释结果、生成和测试假设、在实验筛选之前筛选大型化学数据库以及设计实验。本文综述了广泛的化学信息学应用,以支持基于天然产物的药物发现。我们强调根据多样性、复杂性、酸碱、吸收、分布、代谢、排泄和毒性(ADME/Tox)特性以及片段分析对天然产物数据集进行分析。还讨论了用于化学空间可视化表示的新技术。