Rational Design and Testing of Anti-Knock Additives

Ure, Andrew; Ghosh, Manik Kumer; Rappo, Maria; Dauphin, Roland; Dooley, Stephen

doi:10.3390/en13184923

Cited by 8 publications

(3 citation statements)

References 54 publications

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“… 22 , 39 These may be complex functions of other properties like vapor pressure, diffusivity, bond energies, thermodynamic properties, as well as the reactivity of the dozens of radical species and intermediates that may be generated from any given compound in the fuel combustion process. 40 A comparison of MACAW embeddings with other types of molecular features is presented in Section 3.3 .…”

Section: Resultsmentioning

confidence: 99%

“…These are very encouraging results despite the complex properties being modeled. Note that the flash point, cetane number, and octane number are all high-order properties of great interest for biofuels, for which accurate predictions from first principles are very challenging. , These may be complex functions of other properties like vapor pressure, diffusivity, bond energies, thermodynamic properties, as well as the reactivity of the dozens of radical species and intermediates that may be generated from any given compound in the fuel combustion process . A comparison of MACAW embeddings with other types of molecular features is presented in Section .…”

Section: Resultsmentioning

confidence: 99%

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MACAW: An Accessible Tool for Molecular Embedding and Inverse Molecular Design

Blay

Radivojević

Allen

et al. 2022

J. Chem. Inf. Model.

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The growing capabilities of synthetic biology and organic chemistry demand tools to guide syntheses toward useful molecules. Here, we present Molecular AutoenCoding Auto-Workaround (MACAW), a tool that uses a novel approach to generate molecules predicted to meet a desired property specification (e.g., a binding affinity of 50 nM or an octane number of 90). MACAW describes molecules by embedding them into a smooth multidimensional numerical space, avoiding uninformative dimensions that previous methods often introduce. The coordinates in this embedding provide a natural choice of features for accurately predicting molecular properties, which we demonstrate with examples for cetane and octane numbers, flash points, and histamine H1 receptor binding affinity. The approach is computationally efficient and well-suited to the small- and medium-size datasets commonly used in biosciences. We showcase the utility of MACAW for virtual screening by identifying molecules with high predicted binding affinity to the histamine H1 receptor and limited affinity to the muscarinic M2 receptor, which are targets of medicinal relevance. Combining these predictive capabilities with a novel generative algorithm for molecules allows us to recommend molecules with a desired property value (i.e., inverse molecular design). We demonstrate this capability by recommending molecules with predicted octane numbers of 40, 80, and 120, which is an important characteristic of biofuels. Thus, MACAW augments classical retrosynthesis tools by providing recommendations for molecules on specification.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

MACAW: An Accessible Tool for Molecular Embedding and Inverse Molecular Design

Blay

Radivojević

Allen

et al. 2022

J. Chem. Inf. Model.

View full text Add to dashboard Cite

show abstract

“…Note that the flash point, cetane number, and octane number are all high-order properties of great interest for biofuels, for which accurate predictions from first principles are very challenging 22,40 . These may be complex functions of other properties like vapor pressure, diffusivity, bond energies, thermodynamic properties, as well as the reactivity of the dozens of radical species and intermediates that may be generated from any given compound in the fuel combustion process 41 . A comparison of MACAW embeddings with other types of molecular features is presented in Section 3.3.…”

Section: Efficient Prediction Of Molecular Propertiesmentioning

confidence: 99%

MACAW: an accessible tool for molecular embedding and inverse molecular design

Blay

Radivojević

Allen

et al. 2022

Preprint

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The growing capabilities of synthetic biology and organic chemistry demand tools to guide syntheses towards useful molecules. Here, we present MACAW (Molecular AutoenCoding Auto-Workaround), a tool that uses a novel approach to generate molecules predicted to meet a desired property specification (e.g. a binding affinity of 50 nM or an octane number of 90). MACAW describes molecules by embedding them into a smooth multidimensional numerical space, avoiding uninformative dimensions that previous methods often introduce. The coordinates in this embedding provide a natural choice of features for accurately predicting molecular properties, which we demonstrate with examples for cetane and octane numbers, flash points, and histamine H1 receptor binding affinity. The approach is computationally efficient and well-suited to the small- and medium-size datasets commonly used in the biosciences. We showcase the utility of MACAW for virtual screening by identifying molecules with high predicted binding affinity to the histamine H1 receptor and limited affinity to the muscarinic M2 receptor, which are targets of medicinal relevance. Combining these predictive capabilities with a novel generative algorithm for molecules allows us to recommend molecules with a desired property value (i.e. inverse molecular design). We demonstrate this capability by recommending molecules with predicted octane numbers of 40, 80, and 120, which is an important characteristic for biofuels. Thus, MACAW augments classical retrosynthesis tools by providing recommendations for molecules on specification.

show abstract