Literature DB >> 26215084

A Priori Estimation of Organic Reaction Yields.

Fateme S Emami1, Amir Vahid1, Elizabeth K Wylie1, Sara Szymkuć2, Piotr Dittwald2, Karol Molga2, Bartosz A Grzybowski3,4.   

Abstract

A thermodynamically guided calculation of free energies of substrate and product molecules allows for the estimation of the yields of organic reactions. The non-ideality of the system and the solvent effects are taken into account through the activity coefficients calculated at the molecular level by perturbed-chain statistical associating fluid theory (PC-SAFT). The model is iteratively trained using a diverse set of reactions with yields that have been reported previously. This trained model can then estimate a priori the yields of reactions not included in the training set with an accuracy of ca. ±15 %. This ability has the potential to translate into significant economic savings through the selection and then execution of only those reactions that can proceed in good yields.
© 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Keywords:  optimization; reaction yields; thermodynamics

Year:  2015        PMID: 26215084     DOI: 10.1002/anie.201503890

Source DB:  PubMed          Journal:  Angew Chem Int Ed Engl        ISSN: 1433-7851            Impact factor:   15.336


  4 in total

1.  Predicting the outcomes of organic reactions via machine learning: are current descriptors sufficient?

Authors:  G Skoraczyński; P Dittwald; B Miasojedow; S Szymkuć; E P Gajewska; B A Grzybowski; A Gambin
Journal:  Sci Rep       Date:  2017-06-15       Impact factor: 4.379

2.  Machine Learning May Sometimes Simply Capture Literature Popularity Trends: A Case Study of Heterocyclic Suzuki-Miyaura Coupling.

Authors:  Wiktor Beker; Rafał Roszak; Agnieszka Wołos; Nicholas H Angello; Vandana Rathore; Martin D Burke; Bartosz A Grzybowski
Journal:  J Am Chem Soc       Date:  2022-03-08       Impact factor: 15.419

3.  Computational design of syntheses leading to compound libraries or isotopically labelled targets.

Authors:  Karol Molga; Piotr Dittwald; Bartosz A Grzybowski
Journal:  Chem Sci       Date:  2019-08-16       Impact factor: 9.825

4.  Design of Organic Electronic Materials With a Goal-Directed Generative Model Powered by Deep Neural Networks and High-Throughput Molecular Simulations.

Authors:  H Shaun Kwak; Yuling An; David J Giesen; Thomas F Hughes; Christopher T Brown; Karl Leswing; Hadi Abroshan; Mathew D Halls
Journal:  Front Chem       Date:  2022-01-17       Impact factor: 5.221

  4 in total

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