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Automated Molecular Identity Disambiguator (AutoMID)

自动分子身份消歧器 (AutoMID)

基本信息

批准号：
10569639
负责人：
BARRY A BUNIN
金额：
$ 28万
依托单位：
UNIVERSITY OF MIAMI SCHOOL OF MEDICINE
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-05-01 至 2025-02-28
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10569639
关键词：
Address Adoption Artificial Intelligence platform Biological Biological Assay Biology Categories Cessation of life Chemical Structure Chemicals Classification Complex Data Data Element Data Set Data Sources Data Store Databases Deposition Detection Development FAIR principles Future Goals Graph Hand Hybrids In Vitro Individual Knowledge Legal patent Link Literature Machine Learning Manuals Maps Metadata Modeling Molecular Molecular Structure Names Persons Pharmaceutical Chemistry Pharmaceutical Preparations Privatization Property Proteins Publications Records Reporting Reproducibility Research Research Personnel Resources Semantics Source Standardization Structure Testing Therapeutic Time Training chemical association cheminformatics data harmonization data modeling data repository design digital high throughput screening improved in silico in vivo interoperability knowledge curation machine learning classification novel online resource physical property predictive modeling public database relational database screening small molecule software systems tool usability user-friendly

项目摘要

PROJECT SUMMARY Small molecules are one of the most important classes of therapeutics alleviating suffering and in many cases death for hundreds of millions of people worldwide. Small molecules also serve as invaluable tools to study biology, often with the goal to validate novel targets for the development of future therapeutic drugs. Reproducibility of experimental results and the interoperability and reusability of resulting datasets depend on accurate descriptions of associated research objects, and most critically on correct representations of small molecules that are tested in biological assays. For example, it is not possible to develop predictive models of protein target - small molecule interactions if their chemical structure representations are not correct. Many factors contribute to errors in reported chemical structures in small molecule screening and omics reference databases, scientific publications, and many other web-based resources and documents. Because of the complexity of representing small molecules chemical structure graphs and the lack of thorough curation, errors are frequently introduced by non-experts and error propagation across different digital research assets is a pervasive problem. To address this challenging problem via a scalable approach, we propose the Automated Molecular Identity Disambiguator (AutoMID). AutoMID will be usable in batch mode at scale via an API, for example to assist chemical structure standardization and registration by maintainers of digital research assets, and also via interactive (UI) mode for everyday researchers to quickly and easily validate or correct their small molecule representations. AutoMID will leverage extensive highly standardized linked databases of chemical structures and associated information including names, synonyms, biological activity and physical properties and their sources / provenance and leverage expert rules and AI to enable reliable disambiguation of chemical structure identities at scale.

项目摘要小分子是减轻痛苦的最重要的治疗方法之一，在许多情况下，全世界数亿人的死亡。小分子也是研究的宝贵工具生物学，通常的目标是验证未来治疗药物开发的新靶点。实验结果的再现性以及结果数据集的互操作性和可重用性取决于准确的描述相关的研究对象，最关键的是正确的代表性小在生物检测中被检测的分子。例如，不可能开发以下内容的预测模型蛋白质靶-小分子相互作用，如果它们的化学结构表示不正确。许多小分子筛选和组学参考中报告的化学结构错误的因素数据库、科学出版物和许多其他基于网络的资源和文件。因为表示小分子化学结构图的复杂性和缺乏彻底的管理，错误经常由非专家引入，并且不同数字研究资产之间的错误传播是一个普遍存在的问题。为了通过可扩展的方法解决这个具有挑战性的问题，我们提出了自动化的分子同一性消歧器（AutoMID）。AutoMID将通过API以批量模式大规模使用，示例以帮助数字研究资产的维护者进行化学结构标准化和注册，还可以通过交互式（UI）模式，让日常研究人员快速轻松地验证或纠正他们的小错误。分子表征AutoMID将利用广泛的高度标准化的化学品链接数据库，结构和相关信息，包括名称、同义词、生物活性和物理性质及其来源/出处，并利用专家规则和人工智能，大规模地构建身份。