QSPR modeling for enthalpies of formation of organometallic compounds by means of SMILES-based optimal descriptors

Abstract A quantitative structure–property relationship (QSPR) model for a predicting gas-phase enthalpy of formation have been developed, using as chemical information descriptors based on the simplified molecular input line entry system (SMILES). The model is one-variable equation. The SMILES-based descriptors calculated with correlation weights of SMILES attributes which are obtained by the Monte Carlo method. The model addressed organometallic compounds. Statistical characteristics of the model are the following: n  = 104, R 2  = 0.9943, Q 2  = 0.9940, s  = 19.9 (kJ/mol), F  = 17701 (training set); n  = 28, R 2  = 0.9908, Q 2  = 0.9892, s  = 29.4 (kJ/mol), F  = 2788 (test set).

[1]  I. Tetko,et al.  In silico approaches to prediction of aqueous and DMSO solubility of drug-like compounds: trends, problems and solutions. , 2006, Current medicinal chemistry.

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

[3]  David Weininger,et al.  SMILES, 3. DEPICT. Graphical depiction of chemical structures , 1990, J. Chem. Inf. Comput. Sci..

[4]  Kunal Roy,et al.  Exploring molecular shape analysis of styrylquinoline derivatives as HIV-1 integrase inhibitors. , 2008, European journal of medicinal chemistry.

[5]  P. Roy,et al.  On Some Aspects of Variable Selection for Partial Least Squares Regression Models , 2008 .

[6]  E. Benfenati,et al.  Optimisation of correlation weights of SMILES invariants for modelling oral quail toxicity. , 2007, European journal of medicinal chemistry.

[7]  J. M. Simoes,et al.  Estimation of enthalpies of formation of organometallic compounds from their molecular structures , 2008 .

[8]  Igor V. Tetko,et al.  Internet Software for the Calculation of the Lipophilicity and Aqueous Solubility of Chemical Compounds , 2001, J. Chem. Inf. Comput. Sci..

[9]  I. Zenkevich,et al.  Using new structurally related additive schemes in the precalculation of gas chromatographic retention indices of polychlorinated hydroxybiphenyls on HP-5 stationary phase. , 2004, Journal of chromatography. A.

[10]  David Weininger,et al.  SMILES. 2. Algorithm for generation of unique SMILES notation , 1989, J. Chem. Inf. Comput. Sci..

[11]  Emilio Benfenati,et al.  SMILES as an alternative to the graph in QSAR modelling of bee toxicity , 2007, Comput. Biol. Chem..

[12]  Jerzy Leszczynski,et al.  Multiplicative SMILES-based optimal descriptors: QSPR modeling of fullerene C60 solubility in organic solvents , 2007 .

[13]  A. Toropova,et al.  QSPR Modeling of Complex Stability by Optimization of Correlation Weights of the Hydrogen Bond Index and the Local Graph Invariants , 2002 .

[14]  A. Toropova,et al.  QSPR Modeling of Stability of Complexes of Adenosine Phosphate Derivatives with Metals Absent from the Complexes of the Teaching Access , 2001 .

[15]  A. Tropsha,et al.  Beware of q2! , 2002, Journal of molecular graphics & modelling.

[16]  David Vidal,et al.  LINGO, an Efficient Holographic Text Based Method To Calculate Biophysical Properties and Intermolecular Similarities , 2005, J. Chem. Inf. Model..

[17]  G. G. Stokes.  "J." , 1890, The New Yale Book of Quotations.

[18]  A. Talevi,et al.  Application of descriptors based on Lipinski's rules in the QSPR study of aqueous solubilities. , 2007, Bioorganic & medicinal chemistry.

[19]  Francisco Torrens,et al.  Universal model for the calculation of all organic solvent–water partition coefficients , 1998 .

[20]  E Benfenati,et al.  Additive SMILES-based optimal descriptors in QSAR modelling bee toxicity: Using rare SMILES attributes to define the applicability domain. , 2008, Bioorganic & medicinal chemistry.