Machine learning study of the molecular drivers of natural product prices.
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| Title: | Machine learning study of the molecular drivers of natural product prices. |
|---|---|
| Authors: | Blay, Vincent1,2 (AUTHOR) vroger@ucsc.edu, Dong, Jie3 (AUTHOR), Moya, Andrés1,4,5 (AUTHOR) |
| Source: | Biofuels, Bioproducts & Biorefining. Nov/Dec2021, Vol. 15 Issue 6, p1820-1834. 15p. |
| Subject Terms: | *Natural products, *Machine learning, *DNA fingerprinting, *Synthetic biology, *Chemical amplification, *Complex variables |
| Company/Entity: | Society of Chemical Industry (Great Britain) |
| Abstract: | The price of chemicals is a very complex variable. It can be impacted by production costs but also by market and managerial factors, which may have complex relationships with molecular characteristics and the state of technology and society. In this work, we explore the extent to which molecular characteristics can help explain natural product prices with the aid of machine learning tools. We interpret models trained on molecular descriptors and molecular fingerprints. These models can explain a notable proportion of the variation in prices, suggesting that production and separation costs are a major contributor to current natural product prices. Some molecular properties stand out as key price drivers across the chemical space, including hydrophobicity and the presence of certain heteroatoms. On the other hand, we demonstrate how the application of cliff analysis to prices allows the identification of small chemical transformations that have a remarkable impact on prices. Overall, the work suggests that machine learning could help achieve more consistent and fairer pricing and provides specific examples of chemical transformations in which synthetic biology could add significant value. © 2021 Society of Chemical Industry and John Wiley & Sons, Ltd [ABSTRACT FROM AUTHOR] |
| Database: | Energy & Power Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: enr DbLabel: Energy & Power Source An: 153383605 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Machine learning study of the molecular drivers of natural product prices. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Blay%2C+Vincent%22">Blay, Vincent</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> vroger@ucsc.edu</i><br /><searchLink fieldCode="AR" term="%22Dong%2C+Jie%22">Dong, Jie</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Moya%2C+Andrés%22">Moya, Andrés</searchLink><relatesTo>1,4,5</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Biofuels%2C+Bioproducts+%26+Biorefining%22">Biofuels, Bioproducts & Biorefining</searchLink>. Nov/Dec2021, Vol. 15 Issue 6, p1820-1834. 15p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Natural+products%22">Natural products</searchLink><br />*<searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br />*<searchLink fieldCode="DE" term="%22DNA+fingerprinting%22">DNA fingerprinting</searchLink><br />*<searchLink fieldCode="DE" term="%22Synthetic+biology%22">Synthetic biology</searchLink><br />*<searchLink fieldCode="DE" term="%22Chemical+amplification%22">Chemical amplification</searchLink><br />*<searchLink fieldCode="DE" term="%22Complex+variables%22">Complex variables</searchLink> – Name: SubjectCompany Label: Company/Entity Group: Su Data: <searchLink fieldCode="DE" term="%22Society+of+Chemical+Industry+%28Great+Britain%29%22">Society of Chemical Industry (Great Britain)</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The price of chemicals is a very complex variable. It can be impacted by production costs but also by market and managerial factors, which may have complex relationships with molecular characteristics and the state of technology and society. In this work, we explore the extent to which molecular characteristics can help explain natural product prices with the aid of machine learning tools. We interpret models trained on molecular descriptors and molecular fingerprints. These models can explain a notable proportion of the variation in prices, suggesting that production and separation costs are a major contributor to current natural product prices. Some molecular properties stand out as key price drivers across the chemical space, including hydrophobicity and the presence of certain heteroatoms. On the other hand, we demonstrate how the application of cliff analysis to prices allows the identification of small chemical transformations that have a remarkable impact on prices. Overall, the work suggests that machine learning could help achieve more consistent and fairer pricing and provides specific examples of chemical transformations in which synthetic biology could add significant value. © 2021 Society of Chemical Industry and John Wiley & Sons, Ltd [ABSTRACT FROM AUTHOR] |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=enr&AN=153383605 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1002/bbb.2281 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 15 StartPage: 1820 Subjects: – SubjectFull: Natural products Type: general – SubjectFull: Machine learning Type: general – SubjectFull: DNA fingerprinting Type: general – SubjectFull: Synthetic biology Type: general – SubjectFull: Chemical amplification Type: general – SubjectFull: Complex variables Type: general – SubjectFull: Society of Chemical Industry (Great Britain) Type: general Titles: – TitleFull: Machine learning study of the molecular drivers of natural product prices. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Blay, Vincent – PersonEntity: Name: NameFull: Dong, Jie – PersonEntity: Name: NameFull: Moya, Andrés IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 11 Text: Nov/Dec2021 Type: published Y: 2021 Identifiers: – Type: issn-print Value: 1932104X Numbering: – Type: volume Value: 15 – Type: issue Value: 6 Titles: – TitleFull: Biofuels, Bioproducts & Biorefining Type: main |
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