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Expanding opportunities for mining bioactive chemistry from patents.

ABSTRACT: Bioactive structures published in medicinal chemistry patents typically exceed those in papers by at least twofold and may precede them by several years. The Big-Bang of open automated extraction since 2012 has contributed to over 15 million patent-derived compounds in PubChem. While mapping between chemical structures, assay results and protein targets from patent documents is challenging, these relationships can be harvested using open tools and are beginning to be curated into databases.

SUBMITTER: Southan C

PROVIDER: S-EPMC4548146 | biostudies-literature | 2015 Jul

REPOSITORIES: biostudies-literature

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Expanding opportunities for mining bioactive chemistry from patents.

Southan Christopher C

Drug discovery today. Technologies 20150211

Bioactive structures published in medicinal chemistry patents typically exceed those in papers by at least twofold and may precede them by several years. The Big-Bang of open automated extraction since 2012 has contributed to over 15 million patent-derived compounds in PubChem. While mapping between chemical structures, assay results and protein targets from patent documents is challenging, these relationships can be harvested using open tools and are beginning to be curated into databases. ...[more]

PMID: 26194581

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Project description:Current research and development approaches to drug discovery have become less fruitful and more costly. One alternative paradigm is that of drug repositioning. Many marketed examples of repositioned drugs have been identified through serendipitous or rational observations, highlighting the need for more systematic methodologies to tackle the problem. Systems level approaches have the potential to enable the development of novel methods to understand the action of therapeutic compounds, but requires an integrative approach to biological data. Integrated networks can facilitate systems level analyses by combining multiple sources of evidence to provide a rich description of drugs, their targets and their interactions. Classically, such networks can be mined manually where a skilled person is able to identify portions of the graph (semantic subgraphs) that are indicative of relationships between drugs and highlight possible repositioning opportunities. However, this approach is not scalable. Automated approaches are required to systematically mine integrated networks for these subgraphs and bring them to the attention of the user. We introduce a formal framework for the definition of integrated networks and their associated semantic subgraphs for drug interaction analysis and describe DReSMin, an algorithm for mining semantically-rich networks for occurrences of a given semantic subgraph. This algorithm allows instances of complex semantic subgraphs that contain data about putative drug repositioning opportunities to be identified in a computationally tractable fashion, scaling close to linearly with network data. We demonstrate the utility of our approach by mining an integrated drug interaction network built from 11 sources. This work identified and ranked 9,643,061 putative drug-target interactions, showing a strong correlation between highly scored associations and those supported by literature. We discuss the 20 top ranked associations in more detail, of which 14 are novel and 6 are supported by the literature. We also show that our approach better prioritizes known drug-target interactions, than other state-of-the art approaches for predicting such interactions.

Dataset Information

Expanding opportunities for mining bioactive chemistry from patents.

Publications

Expanding opportunities for mining bioactive chemistry from patents.

Similar Datasets

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