Automated Molecular Identity Disambiguator (AutoMID)

自动分子身份消歧器 (AutoMID)

• 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
• 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

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 全世界数亿人死亡。小分子也是研究的宝贵工具 生物学通常是旨在验证新的靶标，以开发未来的治疗药物。 实验结果的可重复性以及结果数据集的互操作性和可重复性取决于 准确的相关研究对象的描述，最严格地对小的正确表示 在生物测定中测试的分子。例如，不可能开发 蛋白质靶标 - 小分子相互作用如果其化学结构表示不正确。许多 因素导致小分子筛选中报道的化学结构的错误和OMICS参考 数据库，科学出版物以及许多其他基于Web的资源和文档。因为 表示小分子化学结构图和缺乏彻底策划的复杂性，误差 经常是由非专家引入的，并且在不同的数字研究资产之间的错误传播是 普遍的问题。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问题 分子身份剥离剂（汽车）。汽车将通过API大规模批处理模式可用，用于 示例为化学结构标准化和数字研究资产维护者注册的示例， 并通过互动（UI）模式，供日常研究人员快速，轻松地验证或纠正其小型 分子表示。汽车将利用广泛的高标准化化学数据库 结构和相关信息，包括名称，同义词，生物活动和物理特性 及其来源 /出处和利用专家规则和AI来实现可靠的化学歧义 结构身份按规模规模。