Literature DB >> 33123418

Linking Molecular Structure via Functional Group to Chemical Literature for Establishing a Reaction Lineage for Application to Alternatives Assessment.

William M Barrett1, Sudhakar Takkellapati1, Kidus Tadele2, Todd M Martin1, Michael A Gonzalez1.   

Abstract

The evaluation of potential alternatives for chemicals of concern (CoC) requires an understanding of their potential human health and environmental impacts during the manufacture, use, recycle and disposal life stages. During the manufacturing phase, the processes used to produce a desired chemical are defined based on the sequence of chemical reactions and unit operations required to produce the molecule and separate it from other materials used or produced during its manufacture. This paper introduces and demonstrates a tool that links a chemical's structure to information about its synthesis route and the manufacturing process for that chemical. The structure of the chemical is entered using either a SMILES string or the molecule MOL file, and the molecule is searched to identify functional groups present. Based on those functional groups present, the respective named reactions that can be used in its synthesis routes are identified. This information can be used to identify input and output materials for each named reaction, along with reaction conditions, solvents, and catalysts that participate in the reaction. Additionally, the reaction database contains links to internet references and appropriate reaction-specific keywords, further increasing its comprehensiveness. The tool is designed to facilitate the cataloging and use of the chemical literature in a way that allows user to identify and evaluate information about the reactions, such as alternative solvents, catalysts, reaction conditions and other reaction products which enable the comparison of various reaction pathways for the manufacture of the subject chemical. The chemical manufacturing processing steps can be linked to a chemical process ontology to estimate releases and exposures occurring during the manufacturing phase of a chemical.

Entities:  

Keywords:  Alternatives Assessment; Life Cycle Inventory; Sustainable Chemistry; Toxic Substances Control Act

Year:  2019        PMID: 33123418      PMCID: PMC7592719          DOI: 10.1021/acssuschemeng.8b05983

Source DB:  PubMed          Journal:  ACS Sustain Chem Eng        ISSN: 2168-0485            Impact factor:   8.198


  22 in total

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5.  Toward Automated Inventory Modeling in Life Cycle Assessment: The Utility of Semantic Data Modeling to Predict Real-World Chemical Production.

Authors:  Vinit K Mittal; Sidney C Bailin; Michael A Gonzalez; David E Meyer; William M Barrett; Raymond L Smith
Journal:  ACS Sustain Chem Eng       Date:  2017-12-06       Impact factor: 8.198

6.  Coupling Computer-Aided Process Simulation and Estimations of Emissions and Land Use for Rapid Life Cycle Inventory Modeling.

Authors:  Raymond L Smith; Gerardo J Ruiz-Mercado; David E Meyer; Michael A Gonzalez; John P Abraham; William M Barrett; Paul M Randall
Journal:  ACS Sustain Chem Eng       Date:  2017       Impact factor: 8.198

7.  Mining Available Data from the United States Environmental Protection Agency to Support Rapid Life Cycle Inventory Modeling of Chemical Manufacturing.

Authors:  Sarah A Cashman; David E Meyer; Ashley N Edelen; Wesley W Ingwersen; John P Abraham; William M Barrett; Michael A Gonzalez; Paul M Randall; Gerardo Ruiz-Mercado; Raymond L Smith
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8.  Framework towards more Sustainable Chemical Synthesis Design - A Case Study of Organophosphates.

Authors:  Michael A Gonzalez; Sudhakar Takkellapati; Kidus Tadele; Tao Li; Rajender S Varma
Journal:  ACS Sustain Chem Eng       Date:  2019-02-25       Impact factor: 8.198

9.  The CompTox Chemistry Dashboard: a community data resource for environmental chemistry.

Authors:  Antony J Williams; Christopher M Grulke; Jeff Edwards; Andrew D McEachran; Kamel Mansouri; Nancy C Baker; Grace Patlewicz; Imran Shah; John F Wambaugh; Richard S Judson; Ann M Richard
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10.  Prediction of Organic Reaction Outcomes Using Machine Learning.

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  1 in total

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