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DDPD 1.0: a manually curated and standardized database of digital properties of approved drugs for drug-likeness evaluation and drug development
Drug-likeness is a vital consideration when selecting compounds in the early stage of drug discovery. A series of drug-like properties are needed to predict the drug-likeness of a given compound and provide useful guidelines to increase the likelihood of converting lead compounds into drugs. Experim...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9245338/ https://www.ncbi.nlm.nih.gov/pubmed/35139189 http://dx.doi.org/10.1093/database/baab083 |
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author | Li, Qiang Ma, Shiyong Zhang, Xuelu Zhai, Zhaoyu Zhou, Lu Tao, Haodong Wang, Yachen Pan, Jianbo |
author_facet | Li, Qiang Ma, Shiyong Zhang, Xuelu Zhai, Zhaoyu Zhou, Lu Tao, Haodong Wang, Yachen Pan, Jianbo |
author_sort | Li, Qiang |
collection | PubMed |
description | Drug-likeness is a vital consideration when selecting compounds in the early stage of drug discovery. A series of drug-like properties are needed to predict the drug-likeness of a given compound and provide useful guidelines to increase the likelihood of converting lead compounds into drugs. Experimental physicochemical properties, pharmacokinetic/toxicokinetic properties and maximum dosages of approved small-molecule drugs from multiple text-based unstructured data resources have been manually assembled, curated, further digitized and processed into structured data, which are deposited in the Database of Digital Properties of approved Drugs (DDPD). DDPD 1.0 contains 30 212 drug property entries, including 2250 approved drugs and 32 properties, in a standardized value/unit format. Moreover, two analysis tools are provided to examine the drug-likeness features of given molecules based on the collected property data of approved drugs. Additionally, three case studies are presented to demonstrate how users can utilize the database. We believe that this database will be a valuable resource for the drug discovery and development field. Database URL: http://www.inbirg.com/ddpd |
format | Online Article Text |
id | pubmed-9245338 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-92453382022-07-01 DDPD 1.0: a manually curated and standardized database of digital properties of approved drugs for drug-likeness evaluation and drug development Li, Qiang Ma, Shiyong Zhang, Xuelu Zhai, Zhaoyu Zhou, Lu Tao, Haodong Wang, Yachen Pan, Jianbo Database (Oxford) Original Article Drug-likeness is a vital consideration when selecting compounds in the early stage of drug discovery. A series of drug-like properties are needed to predict the drug-likeness of a given compound and provide useful guidelines to increase the likelihood of converting lead compounds into drugs. Experimental physicochemical properties, pharmacokinetic/toxicokinetic properties and maximum dosages of approved small-molecule drugs from multiple text-based unstructured data resources have been manually assembled, curated, further digitized and processed into structured data, which are deposited in the Database of Digital Properties of approved Drugs (DDPD). DDPD 1.0 contains 30 212 drug property entries, including 2250 approved drugs and 32 properties, in a standardized value/unit format. Moreover, two analysis tools are provided to examine the drug-likeness features of given molecules based on the collected property data of approved drugs. Additionally, three case studies are presented to demonstrate how users can utilize the database. We believe that this database will be a valuable resource for the drug discovery and development field. Database URL: http://www.inbirg.com/ddpd Oxford University Press 2022-02-07 /pmc/articles/PMC9245338/ /pubmed/35139189 http://dx.doi.org/10.1093/database/baab083 Text en © The Author(s) 2022. Published by Oxford University Press. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Original Article Li, Qiang Ma, Shiyong Zhang, Xuelu Zhai, Zhaoyu Zhou, Lu Tao, Haodong Wang, Yachen Pan, Jianbo DDPD 1.0: a manually curated and standardized database of digital properties of approved drugs for drug-likeness evaluation and drug development |
title | DDPD 1.0: a manually curated and standardized database of digital properties
of approved drugs for drug-likeness evaluation and drug development |
title_full | DDPD 1.0: a manually curated and standardized database of digital properties
of approved drugs for drug-likeness evaluation and drug development |
title_fullStr | DDPD 1.0: a manually curated and standardized database of digital properties
of approved drugs for drug-likeness evaluation and drug development |
title_full_unstemmed | DDPD 1.0: a manually curated and standardized database of digital properties
of approved drugs for drug-likeness evaluation and drug development |
title_short | DDPD 1.0: a manually curated and standardized database of digital properties
of approved drugs for drug-likeness evaluation and drug development |
title_sort | ddpd 1.0: a manually curated and standardized database of digital properties
of approved drugs for drug-likeness evaluation and drug development |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9245338/ https://www.ncbi.nlm.nih.gov/pubmed/35139189 http://dx.doi.org/10.1093/database/baab083 |
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