PubChem Substance and Compound databases
Sunghwan Kim,Paul A. Thiessen,Evan E Bolton,Jie Chen,Gang Fu,Asta Gindulyte,Lianyi Han,Jane He,Siqian He,Benjamin A. Shoemaker,Jiyao Wang,Bo Yu,Jian-Jian Zhang,Stephen H. Bryant +13 more
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TLDR
An overview of the PubChem Substance and Compound databases is provided, including data sources and contents, data organization, data submission using PubChem Upload, chemical structure standardization, web-based interfaces for textual and non-textual searches, and programmatic access.Abstract:
PubChem (https://pubchem.ncbi.nlm.nih.gov) is a public repository for information on chemical substances and their biological activities, launched in 2004 as a component of the Molecular Libraries Roadmap Initiatives of the US National Institutes of Health (NIH). For the past 11 years, PubChem has grown to a sizable system, serving as a chemical information resource for the scientific research community. PubChem consists of three inter-linked databases, Substance, Compound and BioAssay. The Substance database contains chemical information deposited by individual data contributors to PubChem, and the Compound database stores unique chemical structures extracted from the Substance database. Biological activity data of chemical substances tested in assay experiments are contained in the BioAssay database. This paper provides an overview of the PubChem Substance and Compound databases, including data sources and contents, data organization, data submission using PubChem Upload, chemical structure standardization, web-based interfaces for textual and non-textual searches, and programmatic access. It also gives a brief description of PubChem3D, a resource derived from theoretical three-dimensional structures of compounds in PubChem, as well as PubChemRDF, Resource Description Framework (RDF)-formatted PubChem data for data sharing, analysis and integration with information contained in other databases.read more
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SwissADME: A free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules
TL;DR: The new SwissADME web tool is presented that gives free access to a pool of fast yet robust predictive models for physicochemical properties, pharmacokinetics, drug-likeness and medicinal chemistry friendliness, among which in-house proficient methods such as the BOILED-Egg, iLOGP and Bioavailability Radar are presented.
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MetaboAnalyst 4.0: towards more transparent and integrative metabolomics analysis.
Jasmine Chong,Othman Soufan,Carin Li,Iurie Caraus,Shuzhao Li,Guillaume Bourque,David S. Wishart,Jianguo Xia +7 more
TL;DR: The user interface of MetaboAnalyst 4.0 has been reengineered to provide a more modern look and feel, as well as to give more space and flexibility to introduce new functions.
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HMDB 4.0: the human metabolome database for 2018.
David S. Wishart,Yannick Djoumbou Feunang,Ana Marcu,An Chi Guo,Kevin Y. H. Liang,Rosa Vázquez-Fresno,Tanvir Sajed,Daniel Johnson,Carin Li,Naama Karu,Zinat Sayeeda,Elvis J. Lo,Nazanin Assempour,Mark V. Berjanskii,Sandeep Singhal,David Arndt,Yongjie Liang,Hasan Badran,Jason R. Grant,Arnau Serra-Cayuela,Yifeng Liu,Rupa Mandal,Vanessa Neveu,Allison Pon,Craig Knox,Michael Wilson,Claudine Manach,Augustin Scalbert +27 more
TL;DR: This year's update to the HMDB, HMDB 4.0, represents the most significant upgrade to the database in its history and should greatly enhance its ease of use and its potential applications in nutrition, biochemistry, clinical chemistry, clinical genetics, medicine, and metabolomics science.
Journal ArticleDOI
PubChem 2019 update: improved access to chemical data
Sunghwan Kim,Jie Chen,Tiejun Cheng,Asta Gindulyte,Jia He,Siqian He,Qingliang Li,Benjamin A. Shoemaker,Paul A. Thiessen,Bo Yu,Leonid Zaslavsky,Jian Zhang,Evan E Bolton +12 more
TL;DR: This paper describes the new developments in PubChem, a key chemical information resource for the biomedical research community, which released new web interfaces, such as PubChem Target View page, Sources page, Bioactivity dyad pages and Patent View page.
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Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules
Rafael Gómez-Bombarelli,Jennifer N. Wei,David Duvenaud,José Miguel Hernández-Lobato,Benjamin Sanchez-Lengeling,Dennis Sheberla,Jorge Aguilera-Iparraguirre,Timothy D. Hirzel,Ryan P. Adams,Alán Aspuru-Guzik,Alán Aspuru-Guzik +10 more
TL;DR: In this article, a deep neural network was trained on hundreds of thousands of existing chemical structures to construct three coupled functions: an encoder, a decoder, and a predictor, which can generate new molecules for efficient exploration and optimization through open-ended spaces of chemical compounds.
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