Using similarity between molecular structures for property prediction

M. Shacham, N. Brauner, G. St Cholakov, R. P. Stateva

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Recently, the authors proposed a new approach for predicting a wide range of physical and thermodynamic properties, based on similarity between molecular structures. This presentation includes the rigorous QS2PR (Quantitative Structure - Structure - Property Relationship) method and its short-cut version for members of homologous series, and the Targeted QSPR (TQSPR) method. Some latest, yet unpublished results, on their application for prediction of interaction parameters between pure components in thermodynamic models, needed for phase equilibria calculations, are also presented. All methods advocated are based on the assumption that similarity between molecular structures can be expressed as a relation between vectors of molecular descriptors, describing these structures. Their main advantage is that the properties of a target compound are predicted from the properties of compounds, the structures of which are proven to be similar to that of the target. They also provide for estimation through consistency analysis of the error with which the properties are predicted.

Original languageEnglish
Title of host publicationCHISA 2006 - 17th International Congress of Chemical and Process Engineering
StatePublished - 1 Dec 2006
EventCHISA 2006 - 17th International Congress of Chemical and Process Engineering - Prague, Czech Republic
Duration: 27 Aug 200631 Aug 2006

Publication series

NameCHISA 2006 - 17th International Congress of Chemical and Process Engineering

Conference

ConferenceCHISA 2006 - 17th International Congress of Chemical and Process Engineering
Country/TerritoryCzech Republic
CityPrague
Period27/08/0631/08/06

Keywords

  • Computational chemistry
  • Process design
  • Product design
  • Property prediction
  • QSPR

ASJC Scopus subject areas

  • Chemical Engineering (all)

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