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Digital representation of chemical mixtures to aid drug discovery and formulation

Digital representation of chemical mixtures to aid drug discovery and formulation
化学混合物的数字表示以帮助药物发现和配制
批准号:
9902210
负责人:
BARRY A BUNIN
金额:
$74.87万
依托单位国家:
美国
项目类别:
财政年份:
2019
资助国家:
美国
项目状态:
已结题
起止时间:
2019-12-19 至 2021-11-30

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中文摘要
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英文摘要
PROJECT SUMMARY Collaborative Drug Discovery, Inc. (CDD) proposes to develop a suite of software modules to enable scientists to unambiguously represent chemical mixtures in standard machine-readable formats, filling an urgent and widely-recognized need. Chemicals are typically formulated as mixtures. Recording and communicating infor- mation about chemical mixtures is essential for scientists and support staff in the pharmaceutical industry, in academia, in non-profit research organizations, in government, at specialty chemical vendors, and at commer- cial manufacturers to: • discover, develop, formulate, manufacture and regulate drugs; • manage reagent inventories; comply with laboratory safety requirements; inform first responders; • describe and reproduce biomedical experiments; and • assess and disseminate information about toxicity risks of chemical reagents and consumer products. A working committee of the International Union of Pure and Applied Chemistry (IUPAC) is close to for- malizing “Mixtures InChI” (or MInChI), which will extend the International Chemical Identifier (InChI) to be- come the first standard to encompass mixtures. MInChI will effectively index mixtures in the same way that InChI indexes individual compounds. In Phase 1 CDD developed the data structures and software necessary to enable adoption and utilization of MInChI and create the first general-purpose system for recording information about chemical mixtures that is computable and interoperable. In Phase 2 CDD will continue to develop a sophisticated automated transla- tion tool that will accurately convert legacy catalogs of chemical mixtures from plaintext descriptions or ad hoc formats so that they are properly represented in a machine readable format that can in turn be easily rendered into MInChI identifiers. The broad vision is to help industry to overcome the barriers to adoption so that ma- chine readable mixture descriptions can quickly deliver benefits for drug discovery, chemical safety, and toxi- cology.
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