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...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Database : the journal of biological databases and curation 2022-02, Vol.2022
Hauptverfasser: Li, Qiang, Ma, Shiyong, Zhang, Xuelu, Zhai, Zhaoyu, Zhou, Lu, Tao, Haodong, Wang, Yachen, Pan, Jianbo
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page
container_issue
container_start_page
container_title Database : the journal of biological databases and curation
container_volume 2022
creator Li, Qiang
Ma, Shiyong
Zhang, Xuelu
Zhai, Zhaoyu
Zhou, Lu
Tao, Haodong
Wang, Yachen
Pan, Jianbo
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.
doi_str_mv 10.1093/database/baab083
format Article
fullrecord <record><control><sourceid>proquest_pubme</sourceid><recordid>TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_9245338</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2627480353</sourcerecordid><originalsourceid>FETCH-LOGICAL-c396t-ee42e44cbc271879a1f607e6e1f0292f98d29346e3f74a83974bc4011faab4453</originalsourceid><addsrcrecordid>eNpVUUtv1DAQthAVLQt3TshHLmnt2JvYHJBQl5dUqRzgbE2S8WJw4mA7K5Vf0Z-Mt92t2os9nvke1nyEvOHsnDMtLgbI0EHCiw6gY0o8I2e8XauKyUY8f1Sfkpcp_WasaZWSL8ipWHOhudJn5Haz-b6h_Jy9p0BHmBbw_ob2S4SMA4VpoCmXE-Lg_pXG0ZAGSwe3dRk8nWOYMWaHad-Fubx3e2hctonaEO-qyrs_OGFKFHfgF8guTHfy-yEdcIc-zCNO-RU5seATvj7cK_Lz86cfl1-rq-sv3y4_XlW90E2uEGWNUvZdX7dctRq4bViLDXLLal1brYZaC9mgsK0EJXQru14yzm3ZlJRrsSIf7nXnpRtx6It1BG_m6EaINyaAM08nk_tltmFndF3YQhWBdweBGP4umLIZXerRe5gwLMnUTd1KxUQBrwi7h_YxpBTRPthwZvZBmuNezSHIQnn7-HsPhGNy4j-qWqAK</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2627480353</pqid></control><display><type>article</type><title>DDPD 1.0: a manually curated and standardized database of digital properties of approved drugs for drug-likeness evaluation and drug development</title><source>PubMed Central Free</source><source>MEDLINE</source><source>DOAJ Directory of Open Access Journals</source><source>EZB Electronic Journals Library</source><source>Oxford Academic Journals (Open Access)</source><creator>Li, Qiang ; Ma, Shiyong ; Zhang, Xuelu ; Zhai, Zhaoyu ; Zhou, Lu ; Tao, Haodong ; Wang, Yachen ; Pan, Jianbo</creator><creatorcontrib>Li, Qiang ; Ma, Shiyong ; Zhang, Xuelu ; Zhai, Zhaoyu ; Zhou, Lu ; Tao, Haodong ; Wang, Yachen ; Pan, Jianbo</creatorcontrib><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.</description><identifier>ISSN: 1758-0463</identifier><identifier>EISSN: 1758-0463</identifier><identifier>DOI: 10.1093/database/baab083</identifier><identifier>PMID: 35139189</identifier><language>eng</language><publisher>England: Oxford University Press</publisher><subject>Databases, Factual ; Drug Development ; Drug Discovery ; Original ; Phenylenediamines</subject><ispartof>Database : the journal of biological databases and curation, 2022-02, Vol.2022</ispartof><rights>The Author(s) 2022. Published by Oxford University Press.</rights><rights>The Author(s) 2022. Published by Oxford University Press. 2022</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c396t-ee42e44cbc271879a1f607e6e1f0292f98d29346e3f74a83974bc4011faab4453</citedby><cites>FETCH-LOGICAL-c396t-ee42e44cbc271879a1f607e6e1f0292f98d29346e3f74a83974bc4011faab4453</cites><orcidid>0000-0001-6014-8160</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC9245338/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC9245338/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,315,728,781,785,865,886,27929,27930,53796,53798</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/35139189$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Li, Qiang</creatorcontrib><creatorcontrib>Ma, Shiyong</creatorcontrib><creatorcontrib>Zhang, Xuelu</creatorcontrib><creatorcontrib>Zhai, Zhaoyu</creatorcontrib><creatorcontrib>Zhou, Lu</creatorcontrib><creatorcontrib>Tao, Haodong</creatorcontrib><creatorcontrib>Wang, Yachen</creatorcontrib><creatorcontrib>Pan, Jianbo</creatorcontrib><title>DDPD 1.0: a manually curated and standardized database of digital properties of approved drugs for drug-likeness evaluation and drug development</title><title>Database : the journal of biological databases and curation</title><addtitle>Database (Oxford)</addtitle><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.</description><subject>Databases, Factual</subject><subject>Drug Development</subject><subject>Drug Discovery</subject><subject>Original</subject><subject>Phenylenediamines</subject><issn>1758-0463</issn><issn>1758-0463</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNpVUUtv1DAQthAVLQt3TshHLmnt2JvYHJBQl5dUqRzgbE2S8WJw4mA7K5Vf0Z-Mt92t2os9nvke1nyEvOHsnDMtLgbI0EHCiw6gY0o8I2e8XauKyUY8f1Sfkpcp_WasaZWSL8ipWHOhudJn5Haz-b6h_Jy9p0BHmBbw_ob2S4SMA4VpoCmXE-Lg_pXG0ZAGSwe3dRk8nWOYMWaHad-Fubx3e2hctonaEO-qyrs_OGFKFHfgF8guTHfy-yEdcIc-zCNO-RU5seATvj7cK_Lz86cfl1-rq-sv3y4_XlW90E2uEGWNUvZdX7dctRq4bViLDXLLal1brYZaC9mgsK0EJXQru14yzm3ZlJRrsSIf7nXnpRtx6It1BG_m6EaINyaAM08nk_tltmFndF3YQhWBdweBGP4umLIZXerRe5gwLMnUTd1KxUQBrwi7h_YxpBTRPthwZvZBmuNezSHIQnn7-HsPhGNy4j-qWqAK</recordid><startdate>20220209</startdate><enddate>20220209</enddate><creator>Li, Qiang</creator><creator>Ma, Shiyong</creator><creator>Zhang, Xuelu</creator><creator>Zhai, Zhaoyu</creator><creator>Zhou, Lu</creator><creator>Tao, Haodong</creator><creator>Wang, Yachen</creator><creator>Pan, Jianbo</creator><general>Oxford University Press</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0001-6014-8160</orcidid></search><sort><creationdate>20220209</creationdate><title>DDPD 1.0: a manually curated and standardized database of digital properties of approved drugs for drug-likeness evaluation and drug development</title><author>Li, Qiang ; Ma, Shiyong ; Zhang, Xuelu ; Zhai, Zhaoyu ; Zhou, Lu ; Tao, Haodong ; Wang, Yachen ; Pan, Jianbo</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c396t-ee42e44cbc271879a1f607e6e1f0292f98d29346e3f74a83974bc4011faab4453</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Databases, Factual</topic><topic>Drug Development</topic><topic>Drug Discovery</topic><topic>Original</topic><topic>Phenylenediamines</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Li, Qiang</creatorcontrib><creatorcontrib>Ma, Shiyong</creatorcontrib><creatorcontrib>Zhang, Xuelu</creatorcontrib><creatorcontrib>Zhai, Zhaoyu</creatorcontrib><creatorcontrib>Zhou, Lu</creatorcontrib><creatorcontrib>Tao, Haodong</creatorcontrib><creatorcontrib>Wang, Yachen</creatorcontrib><creatorcontrib>Pan, Jianbo</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Database : the journal of biological databases and curation</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Li, Qiang</au><au>Ma, Shiyong</au><au>Zhang, Xuelu</au><au>Zhai, Zhaoyu</au><au>Zhou, Lu</au><au>Tao, Haodong</au><au>Wang, Yachen</au><au>Pan, Jianbo</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>DDPD 1.0: a manually curated and standardized database of digital properties of approved drugs for drug-likeness evaluation and drug development</atitle><jtitle>Database : the journal of biological databases and curation</jtitle><addtitle>Database (Oxford)</addtitle><date>2022-02-09</date><risdate>2022</risdate><volume>2022</volume><issn>1758-0463</issn><eissn>1758-0463</eissn><abstract>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.</abstract><cop>England</cop><pub>Oxford University Press</pub><pmid>35139189</pmid><doi>10.1093/database/baab083</doi><orcidid>https://orcid.org/0000-0001-6014-8160</orcidid><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 1758-0463
ispartof Database : the journal of biological databases and curation, 2022-02, Vol.2022
issn 1758-0463
1758-0463
language eng
recordid cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_9245338
source PubMed Central Free; MEDLINE; DOAJ Directory of Open Access Journals; EZB Electronic Journals Library; Oxford Academic Journals (Open Access)
subjects Databases, Factual
Drug Development
Drug Discovery
Original
Phenylenediamines
title DDPD 1.0: a manually curated and standardized database of digital properties of approved drugs for drug-likeness evaluation and drug development
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-13T17%3A09%3A12IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_pubme&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=DDPD%201.0:%20a%20manually%20curated%20and%20standardized%20database%20of%20digital%20properties%20of%20approved%20drugs%20for%20drug-likeness%20evaluation%20and%20drug%20development&rft.jtitle=Database%20:%20the%20journal%20of%20biological%20databases%20and%20curation&rft.au=Li,%20Qiang&rft.date=2022-02-09&rft.volume=2022&rft.issn=1758-0463&rft.eissn=1758-0463&rft_id=info:doi/10.1093/database/baab083&rft_dat=%3Cproquest_pubme%3E2627480353%3C/proquest_pubme%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2627480353&rft_id=info:pmid/35139189&rfr_iscdi=true