IAA-NCEA/EPA-0001; “Chemoinformatics of EPA’s EDSP Universe of Chemicals”

为两个使用dNTP的IAA/USEPA项目提供了化学信息支持；第一个项目使用了EPA的ECOTOX数据库，第二个项目使用了EPA的NCEA(Natl Ctr环境评估)。(1)生态毒理学知识库(ECOTOX)是一个全面的、公开可用的知识库，提供关于水生生物、陆地植物和野生动物的单一化学环境毒性数据。为了简化这一劳动密集的过程，Sciome评估了使用机器学习方法根据相关性自动对文档进行分类的可行性，并确定了排除那些被排除的参考文献的理由。在过去三十年的数据库增长和发展过程中，环境保护局在ECOTOX数据库中手动管理了大约45,000篇参考文献。目前的数据管理过程包括几个手动且耗时的步骤。通过使用人工智能(AI)软件工具，可以大大缩短这一进程，使其更加一致，并启动了一项进程，以编制ECOTOX词典和制定自动化管理战略。(2)对于与NCEA的第二个项目，对EDSP的化学品宇宙进行了分析，以形成一个数据库门户。完成了对大约700种化合物的初步分类工作，这些化合物都得到了充分和明确的筛选。进一步的聚类被扩展到通过在CompTox Dashboard中搜索EPA共享的文件中提供的CAS编号和名称而获得的化合物(~14K)的联合。每个聚类实验包括完整的数据清理和协调，通过几个指纹和描述符进行结构量化，然后调查几种距离测量和聚类方法。使用识别简约聚类集合的方法进行了一些特定类别的聚类实验。在权衡了几个可用于开发EDSP门户的平台的利弊之后，EPA和SCHOME同意在Python/Django平台上开发EDSP门户。该门户最初将存储一些物理化学数据和迄今为止已明确管理的化合物的一级生物分析数据。门户网站的功能可以通过与客户的定期展示和反馈周期不断得到加强。

Chemoinformatic support for two IAA/USEPA projects with DNTP were performed; first project with EPA's ECOTOX database and the second project with EPA's NCEA (Natl Ctr Environmental Assessment). (1) The ECOTOXicology Knowledgebase (ECOTOX) is a comprehensive, publicly available knowledgebase providing single chemical environmental toxicity data on aquatic life, terrestrial plants and wildlife. To streamline this labor-intensive process, Sciome evaluated the feasibility of using machine learning methods to automatically classify documents according to relevance, and to identify the exclusion rationale for those references that are excluded. There are ~45,000 references that have been manually curated by EPA in ECOTOX database over the course of the last thirty years of database growth and development. The current data curation process consists of several manual and time-consuming steps. The process could be greatly shortend and made more consistent by use of AI (artificial intelligence) software tools and a process was initiated to develop an ECOTOX lexicon and develop a strategy for automated curation. (2) For the 2nd project with NCEA, the EDSP Universe of Chemicals was analyzed towards forming a database portal. An initial clustering exercise was completed on ~700 compounds that were fully and unambiguously curated. Further clustering was extended to the union of compounds (~14K) obtained by searching the CompTox Dashboard for CAS numbers and Names provided in the file shared by EPA. Each clustering experiment involved complete data cleaning and harmonization, structure quantification by several fingerprints and descriptors followed by investigating several distance measurements and clustering methods. Some class-specific clustering experiments with methods to identify parsimonious set of clusters were pursued. After weighing pros and cons of several available platforms for developing the EDSP portal, EPA and Sciome agreed that the EDSP Portal would be developed on the Python/Django platform. The Portal would initially house some physical-chemistry data and Tier-1 bio-assay data for the compounds that have so far been unambiguously curated. The functionality of the Portal could be continually enhanced through a regular show-and-feedback cycle with the clients.