Dataset Information

Challenges in the Conversion of Manual Processes to Machine-Assisted Syntheses: Activation of Thioglycoside Donors with Aryl(trifluoroethyl)iodonium Triflimide.

ABSTRACT: The steps needed to adapt a stable iodonium promoter for use in automated fluorous-assisted solution-phase oligosaccharide synthesis are described. Direct adaptation of the originally reported batch procedure resulted in the formation of an orthoester or protecting group transfer to the glycosyl acceptor. Fortunately, the addition of inexpensive ?-pinene as an acid scavenger avoided both of these side reactions. The utility of this newly developed protocol was applied to the automated solution-phase synthesis of a ?-glucan fragment.

SUBMITTER: Saliba RC

PROVIDER: S-EPMC5898625 | biostudies-other | 2018 Feb

REPOSITORIES: biostudies-other

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Publications

Challenges in the Conversion of Manual Processes to Machine-Assisted Syntheses: Activation of Thioglycoside Donors with Aryl(trifluoroethyl)iodonium Triflimide.

Saliba Regis C RC Wooke Zachary J ZJ Nieves Gabriel A GA Chu An-Hsiang Adam AA Bennett Clay S CS Pohl Nicola L B NLB

Organic letters 20180116 3

The steps needed to adapt a stable iodonium promoter for use in automated fluorous-assisted solution-phase oligosaccharide synthesis are described. Direct adaptation of the originally reported batch procedure resulted in the formation of an orthoester or protecting group transfer to the glycosyl acceptor. Fortunately, the addition of inexpensive β-pinene as an acid scavenger avoided both of these side reactions. The utility of this newly developed protocol was applied to the automated solution-p ...[more]

PMID: 29336575

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Project description:Organic molecules and polymers have a broad range of applications in biomedical, chemical, and materials science fields. Traditional design approaches for organic molecules and polymers are mainly experimentally-driven, guided by experience, intuition, and conceptual insights. Though they have been successfully applied to discover many important materials, these methods are facing significant challenges due to the tremendous demand of new materials and vast design space of organic molecules and polymers. Accelerated and inverse materials design is an ideal solution to these challenges. With advancements in high-throughput computation, artificial intelligence (especially machining learning, ML), and the growth of materials databases, ML-assisted materials design is emerging as a promising tool to flourish breakthroughs in many areas of materials science and engineering. To date, using ML-assisted approaches, the quantitative structure property/activity relation for material property prediction can be established more accurately and efficiently. In addition, materials design can be revolutionized and accelerated much faster than ever, through ML-enabled molecular generation and inverse molecular design. In this perspective, we review the recent progresses in ML-guided design of organic molecules and polymers, highlight several successful examples, and examine future opportunities in biomedical, chemical, and materials science fields. We further discuss the relevant challenges to solve in order to fully realize the potential of ML-assisted materials design for organic molecules and polymers. In particular, this study summarizes publicly available materials databases, feature representations for organic molecules, open-source tools for feature generation, methods for molecular generation, and ML models for prediction of material properties, which serve as a tutorial for researchers who have little experience with ML before and want to apply ML for various applications. Last but not least, it draws insights into the current limitations of ML-guided design of organic molecules and polymers. We anticipate that ML-assisted materials design for organic molecules and polymers will be the driving force in the near future, to meet the tremendous demand of new materials with tailored properties in different fields.

Dataset Information

Challenges in the Conversion of Manual Processes to Machine-Assisted Syntheses: Activation of Thioglycoside Donors with Aryl(trifluoroethyl)iodonium Triflimide.

Publications

Challenges in the Conversion of Manual Processes to Machine-Assisted Syntheses: Activation of Thioglycoside Donors with Aryl(trifluoroethyl)iodonium Triflimide.

Similar Datasets

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