PEMT: a patent enrichment tool for drug discovery.
PEMT: a patent enrichment tool for drug discovery.
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DOI：
10.1093/bioinformatics/btac716
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发表时间：
2023-01-01
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工业界和学术界的药物发现从业者使用语义工具从在线科学文献中提取信息，以产生对靶点、治疗方法和疾病的新见解。然而，由于获取和分析的复杂性，基于专利的文献作为信息来源经常被忽视。由于药物发现是一个竞争激烈的领域，自然地，利用专利文献的工具可以为该领域的任何参与者提供更明智的决策方面的优势。因此，我们的目标是通过创建一个自动工具来从现有公共资源中描述的专利中提取信息，从而促进专利文献的获取。在这里，我们介绍 PEMT，一种新颖的专利富集工具，它利用 ChEMBL 和 SureChEMBL 等公共数据库来提取与通过 FAIR 原则和元数据注释描述的化学结构和/或基因名称相关的相关专利信息。 PEMT 旨在通过围绕感兴趣的基因建立专利格局来支持药物发现和研究。该工具的制药重点主要是由于国际专利分类代码的分选，但原则上，只要研究概念和化学结构之间的联系，它就可以用于其他专利领域。最后，我们通过根据这些疾病的流行病学患病率生成基因专利列表并探索其潜在的专利格局，展示了罕见疾病的用例。 PEMT 是一个开源 Python 工具，其源代码和 PyPi 包分别可在 https://github.com/Fraunhofer-ITMP/PEMT 和 https://pypi.org/project/PEMT/ 获取。 补充数据可在生物信息学在线获取。
Drug discovery practitioners in industry and academia use semantic tools to extract information from online scientific literature to generate new insights into targets, therapeutics and diseases. However, due to complexities in access and analysis, patent-based literature is often overlooked as a source of information. As drug discovery is a highly competitive field, naturally, tools that tap into patent literature can provide any actor in the field an advantage in terms of better informed decision-making. Hence, we aim to facilitate access to patent literature through the creation of an automatic tool for extracting information from patents described in existing public resources. Here, we present PEMT, a novel patent enrichment tool, that takes advantage of public databases like ChEMBL and SureChEMBL to extract relevant patent information linked to chemical structures and/or gene names described through FAIR principles and metadata annotations. PEMT aims at supporting drug discovery and research by establishing a patent landscape around genes of interest. The pharmaceutical focus of the tool is mainly due to the subselection of International Patent Classification codes, but in principle, it can be used for other patent fields, provided that a link between a concept and chemical structure is investigated. Finally, we demonstrate a use-case in rare diseases by generating a gene-patent list based on the epidemiological prevalence of these diseases and exploring their underlying patent landscapes. PEMT is an open-source Python tool and its source code and PyPi package are available at https://github.com/Fraunhofer-ITMP/PEMT and https://pypi.org/project/PEMT/, respectively. Supplementary data are available at Bioinformatics online.
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