No Electron Left Behind: A Rule-Based Expert System To Predict Chemical Reactions and Reaction Mechanisms.

Saved in:
Bibliographic Details
Title: No Electron Left Behind: A Rule-Based Expert System To Predict Chemical Reactions and Reaction Mechanisms.
Authors: Jonathan H. Chen1, Pierre Baldi1
Source: Journal of Chemical Information & Modeling. Sep2009, Vol. 49 Issue 9, p2034-2043. 10p.
Subjects: Chemical reactions, Organic reaction mechanisms, Electrons, Computer assisted research, Drug design, Drug development
Abstract: Predicting the course and major products of arbitrary reactions is a fundamental problem in chemistry, one that chemists must address in a variety of tasks ranging from synthesis design to reaction discovery. Described here is an expert system to predict organic chemical reactions based on a knowledge base of over 1500 manually composed reaction transformation rules. Novel rule extensions are introduced to enable robust predictions and describe detailed reaction mechanisms at the level of electron flows in elementary reaction steps, ensuring that all reactions are properly balanced and atom-mapped. The core reaction prediction functionalities of this expert system are illustrated with applications including: (1) prediction of detailed reaction mechanisms; (2) computer-based learning in organic chemistry; (3) retrosynthetic analysis; and (4) combinatorial library design. Select applications are available via http://cdb.ics.uci.edu. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Chemical Information & Modeling is the property of American Chemical Society and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
FullText Text:
  Availability: 0
Header DbId: egs
DbLabel: Engineering Source
An: 44778990
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: No Electron Left Behind: A Rule-Based Expert System To Predict Chemical Reactions and Reaction Mechanisms.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Jonathan+H%2E+Chen%22">Jonathan H. Chen</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Pierre+Baldi%22">Pierre Baldi</searchLink><relatesTo>1</relatesTo>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Journal+of+Chemical+Information+%26+Modeling%22">Journal of Chemical Information & Modeling</searchLink>. Sep2009, Vol. 49 Issue 9, p2034-2043. 10p.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Chemical+reactions%22">Chemical reactions</searchLink><br /><searchLink fieldCode="DE" term="%22Organic+reaction+mechanisms%22">Organic reaction mechanisms</searchLink><br /><searchLink fieldCode="DE" term="%22Electrons%22">Electrons</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+assisted+research%22">Computer assisted research</searchLink><br /><searchLink fieldCode="DE" term="%22Drug+design%22">Drug design</searchLink><br /><searchLink fieldCode="DE" term="%22Drug+development%22">Drug development</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Predicting the course and major products of arbitrary reactions is a fundamental problem in chemistry, one that chemists must address in a variety of tasks ranging from synthesis design to reaction discovery. Described here is an expert system to predict organic chemical reactions based on a knowledge base of over 1500 manually composed reaction transformation rules. Novel rule extensions are introduced to enable robust predictions and describe detailed reaction mechanisms at the level of electron flows in elementary reaction steps, ensuring that all reactions are properly balanced and atom-mapped. The core reaction prediction functionalities of this expert system are illustrated with applications including: (1) prediction of detailed reaction mechanisms; (2) computer-based learning in organic chemistry; (3) retrosynthetic analysis; and (4) combinatorial library design. Select applications are available via http://cdb.ics.uci.edu. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Journal of Chemical Information & Modeling is the property of American Chemical Society and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=44778990
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1021/ci900157k
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 10
        StartPage: 2034
    Subjects:
      – SubjectFull: Chemical reactions
        Type: general
      – SubjectFull: Organic reaction mechanisms
        Type: general
      – SubjectFull: Electrons
        Type: general
      – SubjectFull: Computer assisted research
        Type: general
      – SubjectFull: Drug design
        Type: general
      – SubjectFull: Drug development
        Type: general
    Titles:
      – TitleFull: No Electron Left Behind: A Rule-Based Expert System To Predict Chemical Reactions and Reaction Mechanisms.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Jonathan H. Chen
      – PersonEntity:
          Name:
            NameFull: Pierre Baldi
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 28
              M: 09
              Text: Sep2009
              Type: published
              Y: 2009
          Identifiers:
            – Type: issn-print
              Value: 15499596
          Numbering:
            – Type: volume
              Value: 49
            – Type: issue
              Value: 9
          Titles:
            – TitleFull: Journal of Chemical Information & Modeling
              Type: main
ResultId 1