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Predicting chemical reactions with a neural network

  • Track 13: Biological Information And Neural Network
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Computing in the 90's (Great Lakes CS 1989)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 507))

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Abstract

This project is an attempt to use neural networks to extract relevant features from data about the reactivity and product distribution in a chemical reaction in order to predict the percentages of products formed in the reaction. Electrophilic aromatic substitution reactions were chosen because they have been well studied and a fairly large amount of data is available about the percent of each of three resultant isomers formed when various chemical substituents were present. Different representation schemes and network architectures were used, and results achieved are comparable to results obtained from existing programs and from the intuition of chemists.

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Author information

Authors and Affiliations

  1. Computational Chemistry, The Upjohn Company, 49001, Kalamazoo, Michigan

    David W. Elrod & Gerry M. Maggiora

  2. Computer Science Department, Western Michigan University, 49008, Kalamazoo, Michigan

    Robert G. Trenary

Authors
  1. David W. Elrod
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  2. Gerry M. Maggiora
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  3. Robert G. Trenary
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Editor information

Naveed A. Sherwani Elise de Doncker John A. Kapenga

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© 1991 Springer-Verlag Berlin Heidelberg

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Elrod, D.W., Maggiora, G.M., Trenary, R.G. (1991). Predicting chemical reactions with a neural network. In: Sherwani, N.A., de Doncker, E., Kapenga, J.A. (eds) Computing in the 90's. Great Lakes CS 1989. Lecture Notes in Computer Science, vol 507. Springer, New York, NY. https://doi.org/10.1007/BFb0038520

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  • DOI: https://doi.org/10.1007/BFb0038520

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  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-0-387-97628-0

  • Online ISBN: 978-0-387-34815-5

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