Extending the small-molecule similarity principle to all levels of biology with the Chemical Checker (vol 34, pg 591, 2020)

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

作者机构：Joint IRB-BSC-CRG Program in Computational Biology Institute for Research in Biomedicine (IRB Barcelona) The Barcelona Institute of Science and Technology Barcelona Spain Institució Catalana de Recerca i Estudis Avançats (ICREA) Barcelona Spain 

出 版 物：《NATURE BIOTECHNOLOGY》 (自然生物技术)

年 卷 期：2020年第38卷第9期

页      面：1098-1098页

核心收录：

学科分类：0710[理学-生物学] 071010[理学-生物化学与分子生物学] 07[理学] 0836[工学-生物工程] 

基　　金：European Commission, EC H2020 European Research Council, ERC CEI CER ERBN, (614944) H2020 European Research Council, ERC CEI CER ERBN Agencia Estatal de Investigación, AEI, (BIO2016-77038-R) Agencia Estatal de Investigación, AEI 

主　　题：Chemical biology Chemical genetics Chemical libraries Cheminformatics Drug discovery 

摘      要：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.