Multiclass Molecular Knowledge Framework for Product and Process Design

Abstract Computer Aided Product Design (CAPD) is widely used in process system engineering as a powerful tool for searching novel chemicals. The crucial steps in CAPD are the generation of candidate molecules and the estimation of properties, especially when complex molecular structures like flavors are sought. In this paper, we present a multiclass molecular knowledge framework which is based on chemical graph theory and chemical knowledge. Three kinds of functional groups are defined: elementary, basic and composed groups. These serve to generate four classes of knowledge that can be useful for property estimation and molecular design. An Input/output structure basing on XML language is defined to favor the interoperability between softwares.

[1]  John L. Oscarson,et al.  Development of an Automated SMILES Pattern Matching Program To Facilitate the Prediction of Thermophysical Properties by Group Contribution Methods , 2001 .

[2]  Michael D. Frenkel,et al.  ThermoML-An XML-based approach for storage and exchange of experimental and critically evaluated thermophysical and thermochemical property data. 2. Uncertainties , 2003 .

[3]  Henry S. Rzepa,et al.  Chemical Markup, XML and the World-Wide Web. 2. Information Objects and the CMLDOM , 2001, J. Chem. Inf. Comput. Sci..

[4]  R. Gani,et al.  New group contribution method for estimating properties of pure compounds , 1994 .

[5]  Rafiqul Gani,et al.  A multi-step and multi-level approach for computer aided molecular design , 2000 .

[6]  David Weininger,et al.  SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules , 1988, J. Chem. Inf. Comput. Sci..

[7]  L. Pogliani.  From molecular connectivity indices to semiempirical connectivity terms: recent trends in graph theoretical descriptors. , 2000, Chemical reviews.

[8]  Luke E. K. Achenie,et al.  The design of blanket wash solvents with environmental considerations , 2004 .

[9]  K. Joback,et al.  ESTIMATION OF PURE-COMPONENT PROPERTIES FROM GROUP-CONTRIBUTIONS , 1987 .

[10]  Alexander P. Bünz,et al.  Application of quantitative structure-performance relationship and neural network models for the prediction of physical properties from molecular structure , 1998 .