A Generally Applicable Computer Algorithm Based on the Group Additivity Method for the Calculation of Seven Molecular Descriptors: Heat of Combustion, LogPO/W, LogS, Refractivity, Polarizability, Toxicity and LogBB of Organic Compounds; Scope and Limits of Applicability.

نویسنده

  • Rudolf Naef
چکیده

A generally applicable computer algorithm for the calculation of the seven molecular descriptors heat of combustion, logPoctanol/water, logS (water solubility), molar refractivity, molecular polarizability, aqueous toxicity (protozoan growth inhibition) and logBB (log (cblood/cbrain)) is presented. The method, an extendable form of the group-additivity method, is based on the complete break-down of the molecules into their constituting atoms and their immediate neighbourhood. The contribution of the resulting atom groups to the descriptor values is calculated using the Gauss-Seidel fitting method, based on experimental data gathered from literature. The plausibility of the method was tested for each descriptor by means of a k-fold cross-validation procedure demonstrating good to excellent predictive power for the former six descriptors and low reliability of logBB predictions. The goodness of fit (Q²) and the standard deviation of the 10-fold cross-validation calculation was >0.9999 and 25.2 kJ/mol, respectively, (based on N = 1965 test compounds) for the heat of combustion, 0.9451 and 0.51 (N = 2640) for logP, 0.8838 and 0.74 (N = 1419) for logS, 0.9987 and 0.74 (N = 4045) for the molar refractivity, 0.9897 and 0.77 (N = 308) for the molecular polarizability, 0.8404 and 0.42 (N = 810) for the toxicity and 0.4709 and 0.53 (N = 383) for logBB. The latter descriptor revealing a very low Q² for the test molecules (R² was 0.7068 and standard deviation 0.38 for N = 413 training molecules) is included as an example to show the limits of the group-additivity method. An eighth molecular descriptor, the heat of formation, was indirectly calculated from the heat of combustion data and correlated with published experimental heat of formation data with a correlation coefficient R² of 0.9974 (N = 2031).

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

QSAR models to predict physico-chemical Properties of some barbiturate derivatives using molecular descriptors and genetic algorithm- multiple linear regressions

In this study the relationship between choosing appropriate descriptors by genetic algorithm to the Polarizability (POL), Molar Refractivity (MR) and Octanol/water Partition Coefficient (LogP) of barbiturates is studied. The chemical structures of the molecules were optimized using ab initio 6-31G basis set method and Polak-Ribiere algorithm with conjugated gradient within HyperChem 8.0 environ...

متن کامل

QSRR Study of Organic Dyes by Multiple Linear Regression Method Based on Genetic Algorithm (GA–MLR

Quantitative structure-retention relationships (QSRRs) are used to correlate paper chromatographic retention factors of disperse dyes with theoretical molecular descriptors. A data set of 23 compounds with known RF values was used. The genetic algorithm-multiple linear regression analysis (GA-MLR) with three selected theoretical descriptors was obtained. The stability and predictability of the ...

متن کامل

Quantitative Modeling for Prediction of Critical Temperature of Refrigerant Compounds

The quantitative structure-property relationship (QSPR) method is used to develop the correlation between structures of refrigerants (198 compounds) and their critical temperature. Molecular descriptors calculated from structure alone were used to represent molecular structures. A subset of the calculated descriptors selected using a genetic algorithm (GA) was used in the QSPR model development...

متن کامل

UNDERSTANDING BEHAVIOR OF ANTINEOPLASTIC MOLECULES BASED ON MLR MODELS

New statistic based models provide a wide area of prediction equipments for different science areas. Among these fields biology have just entered the contest of interdisciplinary sciences. Drug discovery is a long and expensive process which could be decreased with theoretical approaches. In this study, 500 reported assayed anti cancer molecules were extracted from Science Direct articles, sket...

متن کامل

Prediction of Drug Lipophilicity Using Back Propagation Artificial Neural Network Modeling

A quantitative structure–property relationship (QSPR) study was performed to develop models those relate the structures of 150 drug organic compounds to their n-octanol–water partition coefficients (logPo/w). Molecular descriptors derived solely from 3D structures of the molecular drugs. A genetic algorithm was also applied as a variable selection tools in QSPR analysis. The models were constru...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:
  • Molecules

دوره 20 10  شماره 

صفحات  -

تاریخ انتشار 2015