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新技术实现小分子生物活性表征
作者:小柯机器人 发布时间:2020/5/20 23:24:24

西班牙巴塞罗那科技研究所Miquel Duran-Frigola等研究人员开发了一项新技术,可用于表征小分子化合物的生物活性。相关论文于2020年5月18日在线发表在《自然—生物技术》杂志上。

研究人员报道了化学检查器(CC),它提供了约800,000个小分子的生物活性数据。从化合物的化学性质到临床结果,CC将数据分为五个级别,每个级别的复杂性不断提高。
 
在两者之间,它包括靶标、脱靶标、网络和细胞级信息,例如组学数据、生长抑制和形态。生物活性数据以矢量格式表示,将化学相似性的概念扩展到生物活性标记之间的相似性。
 
研究人员展示了CC特征如何协助药物开发任务,包括靶标鉴定和文库表征。研究人员还验证了在单独使用化学信息无法解决的情况下如何来发现化合物。总体而言,CC特征有助于将生物活性数据转换为可用于机器学习的格式。
 
据了解,小分子一般通过其化学结构进行比较,但没有统一的分析框架来表示和比较其生物活性。
 
附:英文原文

Title: Extending the small-molecule similarity principle to all levels of biology with the Chemical Checker

Author: Miquel Duran-Frigola, Eduardo Pauls, Oriol Guitart-Pla, Martino Bertoni, Vctor Alcalde, David Amat, Teresa Juan-Blanco, Patrick Aloy

Issue&Volume: 2020-05-18

Abstract: Small molecules are usually compared by their chemical structure, but there is no unified analytic framework for representing and comparing their biological activity. We present the Chemical Checker (CC), which provides processed, harmonized and integrated bioactivity data on ~800,000 small molecules. The CC divides data into five levels of increasing complexity, from the chemical properties of compounds to their clinical outcomes. In between, it includes targets, off-targets, networks and cell-level information, such as omics data, growth inhibition and morphology. Bioactivity data are expressed in a vector format, extending the concept of chemical similarity to similarity between bioactivity signatures. We show how CC signatures can aid drug discovery tasks, including target identification and library characterization. We also demonstrate the discovery of compounds that reverse and mimic biological signatures of disease models and genetic perturbations in cases that could not be addressed using chemical information alone. Overall, the CC signatures facilitate the conversion of bioactivity data to a format that is readily amenable to machine learning methods.

DOI: 10.1038/s41587-020-0502-7

Source: https://www.nature.com/articles/s41587-020-0502-7

期刊信息

Nature Biotechnology:《自然—生物技术》,创刊于1996年。隶属于施普林格·自然出版集团,最新IF:31.864
官方网址:https://www.nature.com/nbt/
投稿链接:https://mts-nbt.nature.com/cgi-bin/main.plex