Opening up connectivity between documents, structures and bioactivity

Abstract

Bioscientists reading papers or patents strive to discern the key relationships reported within a document “D“ where a bioactivity “A” with a quantitative result “R” (e.g., an IC50) is reported for chemical structure “C” that modulates (e.g., inhibits) a protein target “P”. A useful shorthand for this connectivity thus becomes DARCP. The problem at the core of this article is that the community has spent millions effectively burying these relationships in PDFs over many decades but must now spend millions more trying to get them back out. The key imperative for this is to increase the flow into structured open databases. The positive impacts will include expanded data mining opportunities for drug discovery and chemical biology. Over the last decade commercial sources have manually extracted DARCP from ≈300,000 documents encompassing ≈7 million compounds interacting with ≈10,000 targets. Over a similar time, the Guide to Pharmacology, BindingDB and ChEMBL have carried out analogues DARCP extractions. Although their expert-curated numbers are lower (i.e., ≈2 million compounds against ≈3700 human proteins), these open sources have the great advantage of being merged within PubChem. Parallel efforts have focused on the extraction of document-to-compound (D-C-only) connectivity. In the absence of molecular mechanism of action (mmoa) annotation, this is of less value but can be automatically extracted. This has been significantly accomplished for patents, (e.g., by IBM, SureChEMBL and WIPO) for over 30 million compounds in PubChem. These have recently been joined by 1.4 million D-C submissions from three major chemistry publishers. In addition, both the European and US PubMed Central portals now add chemistry look-ups from abstracts and full-text papers. However, the fully automated extraction of DARCLP has not yet been achieved. This stands in contrast to the ability of biocurators to discern these relationships in minutes. Unfortunately, no journals have yet instigated a flow of author-specified DARCP directly into open databases. Progress may come from trends such as open science, open access (OA), findable, accessible, interoperable and reusable (FAIR), resource description framework (RDF) and WikiData. However, we will need to await the technical applicability in respect to DARCP capture to see if this opens up connectivity.

Will the Chemical Probes Please Stand Up?

“…However, the rate of data extraction from these publications and flow into databases remains slow. 71 6. We are considering how to address the mismatched and some completely missing probes in PubChem but we need to iterate with the originating sources in the first instance.…”

The iPPI-DB initiative: a community-centered database of protein–protein interaction modulators

“…These persistent limitations stand in sharp contrast to community curation approaches whereby biocurators have the ability to discern such relationships from a paper in minutes ( Southan, 2020 ). The fact a significant portion of the iPPI-DB compounds are missing from major public generic databases (13% are missing from PubChem and up to 51% are missing from ChemBL) is also a good illustration of the importance of combining different data sources and that community curation of specialized database like iPPI-DB is a strong strategy.…”

“…The pharmacological results that are valuable for specialized databases like iPPI-DB are retrieved from peer-reviewed publications including scientific articles and patents. This type of information has been recently defined as DARCLP relationships in a very interesting review ( Southan, 2020 ). This definition illustrates the relationships that bioscientists reading papers or patents have to establish in order for the data to become tractable: a document (D) in which bioactivities (A) have to be associated with quantitative results (R), chemical compounds (C), explicit location references (L) for the compounds within the document (e.g.…”

“…Two complementary solutions can be adopted to ensure the sustainable maintenance of databases such as iPPI-DB: a fully automated process relying on automated chemical and biomedical entity recognition from text (e.g. via Natural Language Processing and artificial intelligence) ( Southan, 2020 ); or a community curation based on a cohort of experts.…”

Will the chemical probes please stand up?

“…However, the rate of data extraction from these publications and flow into databases remains slow. 72 6. We are considering how to address the mismatched and missing probes in PubChem but we need to iterate with the originating sources in the first instance.…”