Chemical databases: curation or integration by user-defined equivalence?

Hersey, Anne; Chambers, Jon; Bellis, Louisa J.; Bento, A. Patrícia; Gaulton, Anna; Overington, John P.

doi:10.1016/j.ddtec.2015.01.005

Cited by 44 publications

(26 citation statements)

References 21 publications

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“…Chemical vocabularies presented the greatest mapping challenge ( Himmelstein, 2015b ), since these are poorly standardized ( Hersey et al, 2015 ). UniChem’s ( Chambers et al, 2013 ) Connectivity Search ( Chambers et al, 2014 ) was used to map compounds, which maps by atomic connectivity (based on First InChIKey Hash Blocks ( Heller et al, 2013 )) and ignores small molecular differences.…”

Section: Materials and Methodsmentioning

confidence: 99%

Systematic integration of biomedical knowledge prioritizes drugs for repurposing

Himmelstein

Lizée

Hessler

et al. 2017

eLife

395

387

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The ability to computationally predict whether a compound treats a disease would improve the economy and success rate of drug approval. This study describes Project Rephetio to systematically model drug efficacy based on 755 existing treatments. First, we constructed Hetionet (neo4j.het.io), an integrative network encoding knowledge from millions of biomedical studies. Hetionet v1.0 consists of 47,031 nodes of 11 types and 2,250,197 relationships of 24 types. Data were integrated from 29 public resources to connect compounds, diseases, genes, anatomies, pathways, biological processes, molecular functions, cellular components, pharmacologic classes, side effects, and symptoms. Next, we identified network patterns that distinguish treatments from non-treatments. Then, we predicted the probability of treatment for 209,168 compound–disease pairs (het.io/repurpose). Our predictions validated on two external sets of treatment and provided pharmacological insights on epilepsy, suggesting they will help prioritize drug repurposing candidates. This study was entirely open and received realtime feedback from 40 community members.

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Section: Materials and Methodsmentioning

confidence: 99%

Systematic integration of biomedical knowledge prioritizes drugs for repurposing

Himmelstein

Lizée

Hessler

et al. 2017

eLife

395

387

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“…Finally, there are tools for data mining in order to retrieve information from various resources such as patents. ChemCurator by ChemAxon retrieves structures and related data from the literature [83][84][85][86].…”

Section: Tools For Handling and Managing Database Resourcesmentioning

confidence: 99%

A primer on natural product-based virtual screening

Koulouridi

Valli

Ntie‐Kang

et al. 2019

Physical Sciences Reviews

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Databases play an important role in various computational techniques, including virtual screening (VS) and molecular modeling in general. These collections of molecules can contain a large amount of information, making them suitable for several drug discovery applications. For example, vendor, bioactivity data or target type can be found when searching a database. The introduction of these data resources and their characteristics is used for the design of an experiment. The description of the construction of a database can also be a good advisor for the creation of a new one. There are free available databases and commercial virtual libraries of molecules. Furthermore, a computational chemist can find databases for a general purpose or a specific subset such as natural products (NPs). In this chapter, NP database resources are presented, along with some guidelines when preparing an NP database for drug discovery purposes.

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“…See online supplementary material for annotation and curation guidelines that are available in Supplementary File 4 and at the ‘Help’ tab (http://drugtargetcommons.fimm.fi/annotation_guidlines/). These guidelines follow a curation standard developed in-house, based on experience from similar curation tasks ( 26 , 29 ). Previous user feedbacks are publicly available for the new users who can see the comments by the previous users and our responses to those comments.…”

Section: User Interfacementioning

confidence: 99%

Drug Target Commons 2.0: a community platform for systematic analysis of drug–target interaction profiles

et al. 2018

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Drug Target Commons (DTC) is a web platform (database with user interface) for community-driven bioactivity data integration and standardization for comprehensive mapping, reuse and analysis of compound–target interaction profiles. End users can search, upload, edit, annotate and export expert-curated bioactivity data for further analysis, using an application programmable interface, database dump or tab-delimited text download options. To guide chemical biology and drug-repurposing applications, DTC version 2.0 includes updated clinical development information for the compounds and target gene–disease associations, as well as cancer-type indications for mutant protein targets, which are critical for precision oncology developments.

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Chemical databases: curation or integration by user-defined equivalence?

Cited by 44 publications

References 21 publications

Systematic integration of biomedical knowledge prioritizes drugs for repurposing

Systematic integration of biomedical knowledge prioritizes drugs for repurposing

A primer on natural product-based virtual screening

Drug Target Commons 2.0: a community platform for systematic analysis of drug–target interaction profiles

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