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      Now the future, we see our dreams: artificial intelligence in drug discovery

      editorial
      * , 1 ,
      Future Drug Discovery
      Newlands Press Ltd
      artificial intelligence, computational chemistry, drug design, drug discovery, machine learning

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          Most cited references8

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          PubChem 2019 update: improved access to chemical data

          Abstract PubChem (https://pubchem.ncbi.nlm.nih.gov) is a key chemical information resource for the biomedical research community. Substantial improvements were made in the past few years. New data content was added, including spectral information, scientific articles mentioning chemicals, and information for food and agricultural chemicals. PubChem released new web interfaces, such as PubChem Target View page, Sources page, Bioactivity dyad pages and Patent View page. PubChem also released a major update to PubChem Widgets and introduced a new programmatic access interface, called PUG-View. This paper describes these new developments in PubChem.
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            Activity, assay and target data curation and quality in the ChEMBL database

            The emergence of a number of publicly available bioactivity databases, such as ChEMBL, PubChem BioAssay and BindingDB, has raised awareness about the topics of data curation, quality and integrity. Here we provide an overview and discussion of the current and future approaches to activity, assay and target data curation of the ChEMBL database. This curation process involves several manual and automated steps and aims to: (1) maximise data accessibility and comparability; (2) improve data integrity and flag outliers, ambiguities and potential errors; and (3) add further curated annotations and mappings thus increasing the usefulness and accuracy of the ChEMBL data for all users and modellers in particular. Issues related to activity, assay and target data curation and integrity along with their potential impact for users of the data are discussed, alongside robust selection and filter strategies in order to avoid or minimise these, depending on the desired application.
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              An automated curation procedure for addressing chemical errors and inconsistencies in public datasets used in QSAR modelling$

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                Author and article information

                Journal
                FDD
                Future Drug Discovery
                Future Drug. Discov.
                Future Drug Discovery
                Newlands Press Ltd (London, UK )
                2631-3316
                21 October 2019
                October 2019
                : 1
                : 2
                : FDD22
                Affiliations
                1Drug Discovery Unit, Cancer Research UK Manchester Institute, The University of Manchester, Alderley Park, SK10 4TG, UK
                Author notes
                [* ]Author for correspondence: Tel.: +44 161 306 6280; rae.lawrence@ 123456cruk.manchester.ac.uk
                Article
                10.4155/fdd-2019-0027
                7eb9fde3-cf21-406b-bbf1-31cac9465909
                © 2019 Rae Lawrence

                This work is licensed under the Attribution-NonCommercial-NoDerivatives 4.0 Unported License

                History
                : 21 August 2019
                : 10 October 2019
                : 21 October 2019
                Page count
                Pages: 3
                Categories
                Editorial

                Biochemistry,Molecular medicine,Pharmaceutical chemistry,Bioinformatics & Computational biology,Biotechnology,Pharmacology & Pharmaceutical medicine
                drug discovery,computational chemistry,machine learning,drug design,artificial intelligence

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