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Investigating Protein Sequence-structure-dynamics Relationships with Bio3D-web
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Opening up connectivity between documents, structures and bioactivity.

Christopher Southan1,2

  • 1Deanery of Biomedical Sciences, University of Edinburgh, Edinburgh, EH8 9XD, UK.

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|April 14, 2020
PubMed
Summary
This summary is machine-generated.

Scientists extract drug-target relationships (DARCP) from research papers to advance drug discovery. Current methods are inefficient, necessitating better data integration into open databases for improved data mining and chemical biology insights.

Keywords:
activity datachemical structuresdatabasesdrug discoveryprotein targets

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Area of Science:

  • Bioinformatics and Cheminformatics
  • Drug Discovery and Development
  • Scientific Data Management

Background:

  • Bioscientists routinely identify relationships between chemical structures (C), bioactivities (A), quantitative results (R), and protein targets (P), often abbreviated as DARCP.
  • Vast amounts of DARCP information are embedded in unstructured PDF documents, posing a significant challenge for data retrieval and analysis.
  • Manual extraction of DARCP data is resource-intensive, with commercial efforts extracting data from hundreds of thousands of documents.

Purpose of the Study:

  • To highlight the critical need for improved methods to extract and integrate DARCP information into structured, open databases.
  • To discuss the potential benefits of enhanced data accessibility for drug discovery and chemical biology research.
  • To review current progress and challenges in automated DARCP extraction from scientific literature and patents.

Main Methods:

  • Analysis of current manual and semi-automated DARCP extraction efforts by commercial entities and open-access initiatives (e.g., Guide to Pharmacology, BindingDB, ChEMBL).
  • Review of automated document-to-compound (D-C) extraction from patents and publisher submissions.
  • Assessment of emerging trends like open science, FAIR data principles, and linked data (RDF, Wikidata) for potential DARCP capture solutions.

Main Results:

  • Significant progress has been made in extracting document-to-compound (D-C) connectivity, particularly from patents, yielding millions of compound entries.
  • Open databases like PubChem have integrated DARCP data from various sources, though expert-curated datasets are smaller than commercial ones.
  • Fully automated extraction of the complete DARCP relationship remains an unmet challenge, contrasting with the efficiency of manual biocurator extraction.

Conclusions:

  • There is a pressing need to transition from PDF-based data to structured, open databases to unlock the full potential of DARCP information.
  • While automated D-C extraction is advancing, the complete DARCP extraction requires further development for efficient data mining in drug discovery.
  • Future progress may depend on adopting open science and FAIR data principles, alongside technical advancements in DARCP capture from scientific publications.