A novel approach to identify shared fragments in drugs and natural products

Akshay Balasubramanya, I. Thapa, D. Bastola, D. Ghersi
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Abstract

Fragment-based approaches have now become an important component of the drug discovery process. At the same time, pharmaceutical chemists are more often turning to the natural world and its extremely large and diverse collection of natural compounds to discover new leads that can potentially be turned into drugs. In this study we introduce and discuss a computational pipeline to automatically extract statistically overrepresented chemical fragments in therapeutic classes, and search for similar fragments in a large database of natural products. By systematically identifying enriched fragments in therapeutic groups, we are able to extract and focus on few fragments that are likely to be active or structurally important as scaffolds. We show that several therapeutic classes (including antibacterial, antineoplastic, and drugs active on the cardiovascular system, among others) have enriched fragments that are also found in many natural compounds. Further, our method is able to detect fragments shared by a drug and a natural product even when the global similarity between the two molecules is generally low. A further development of this computational pipeline is to help predict putative therapeutic activities of natural compounds, and to help identify novel leads for drug discovery.
一种鉴定药物和天然产物共享片段的新方法
基于片段的方法现在已成为药物发现过程的重要组成部分。与此同时,药物化学家更多地转向自然界及其极其庞大和多样化的天然化合物集合,以发现可能转化为药物的新线索。在这项研究中,我们介绍并讨论了一种计算管道,用于自动提取治疗类中统计上代表性过高的化学片段,并在大型天然产物数据库中搜索相似的片段。通过系统地识别治疗组中富集的片段,我们能够提取并专注于可能具有活性或作为支架结构重要的少数片段。我们发现,一些治疗类(包括抗菌、抗肿瘤和心血管系统活性药物等)含有丰富的片段,这些片段也存在于许多天然化合物中。此外,我们的方法能够检测药物和天然产物共享的片段,即使两个分子之间的全局相似性通常很低。这种计算管道的进一步发展是帮助预测天然化合物的假定治疗活性,并帮助确定药物发现的新线索。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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