MetadatenAuthor: | Laura IsigkeitORCiD, Apirat ChaikuadORCiD, Daniel MerkORCiDGND |
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URN: | urn:nbn:de:hebis:30:3-795795 |
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DOI: | https://doi.org/10.3390/molecules27082513 |
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ISSN: | 1420-3049 |
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Parent Title (English): | Molecules |
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Publisher: | MDPI |
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Place of publication: | Basel |
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Document Type: | Article |
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Language: | English |
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Date of Publication (online): | 2022/04/13 |
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Date of first Publication: | 2022/04/13 |
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Publishing Institution: | Universitätsbibliothek Johann Christian Senckenberg |
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Release Date: | 2023/11/21 |
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Tag: | big data; data curation; de novo design; machine learning; medicinal chemistry |
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Volume: | 27 |
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Issue: | 8, art. 2513 |
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Article Number: | 2513 |
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Page Number: | 13 |
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First Page: | 1 |
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Last Page: | 13 |
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Note: | This project has received funding from the Innovative Medicines Initiative 2 Joint Undertaking (JU) under grant agreement No. 875510. The JU receives support from the European Union’s Horizon 2020 research and innovation program, EFPIA, Ontario Institute for Cancer Research, Royal Institution for the Advancement of Learning McGill University, Kungliga Tekniska Hoegskolan, and Diamond Light Source Limited. |
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Note: | The consensus dataset generated and analyzed in this study is freely available from Zenodo (https://zenodo.org), with the doi:10.5281/zenodo.6398019. |
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HeBIS-PPN: | 516701916 |
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Institutes: | Biochemie, Chemie und Pharmazie |
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| Fachübergreifende Einrichtungen / Buchmann Institut für Molekulare Lebenswissenschaften (BMLS) |
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Dewey Decimal Classification: | 5 Naturwissenschaften und Mathematik / 54 Chemie / 540 Chemie und zugeordnete Wissenschaften |
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Sammlungen: | Universitätspublikationen |
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Licence (German): | Creative Commons - CC BY - Namensnennung 4.0 International |
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