Correlation for estimation of the aqueous solubility (logSw) of chlorinatedhydrocarbons molecules is proposed. The MCI based quantitative structure-propertyrelationship (QSPR) model proposed is predictive and requires only three connectivityindices in the calculation. The correlation equation obtained which is based on a training setof 50 chlorinated hydrocarbons has a correlation coefficient of 0.9670 and a standard errorof 0.44 log10 units. Application of the developed model to a testing set of 73 chlorinatedhydrocarbons demonstrates that the new model is reliable with good predictive accuracy andsimple formulation. Besides, the model does not require any experimental physicochemicalproperties in the calculation, so it is easy to apply, e...
We present an extended QSPR modeling of solubilities of about 500 substances in series of up to 69 d...
Non conformational QSPR models were built for the aqueous solubility (mol/L) at 25 °C of 5610 struct...
Accurate solubility prediction is crucial across a range of scientific disciplines including drug ...
ABSTRACT The weighted holistic invariant molecular-three dimensional-quantitative structure property...
The aqueous solubilities of a set of 109 hydrocarbons and 132 halogenated hydrocarbons (total 241) a...
QSPR correlation equations were developed for the prediction of the solubilities of organic gases an...
Aqueous solubilities of polychlorinated biphenyls have been correlated with topological molecular de...
A simple QSPR model, based on seven 1D and 2D descriptors and artificial neural network, was develop...
This thesis focuses on the development of quantitative structure-activity relationship (QSPR) models...
It was the aim of the present work to develop a quantitative structure-property relationship (QSPR) ...
Two estimation methods for water solubility are compared for several sets of compounds. One method i...
Physico-chemical properties of chemicals are important data for exposure analysis. They can be estim...
Optimization of correlation weights of local graph invariants is an approach to model molecular prop...
Models are important tools for designing or redesigning water treatment processes and technologies t...
The relationship between aqueous activity coefficients (log γ(w)) and different physico-chemical pro...
We present an extended QSPR modeling of solubilities of about 500 substances in series of up to 69 d...
Non conformational QSPR models were built for the aqueous solubility (mol/L) at 25 °C of 5610 struct...
Accurate solubility prediction is crucial across a range of scientific disciplines including drug ...
ABSTRACT The weighted holistic invariant molecular-three dimensional-quantitative structure property...
The aqueous solubilities of a set of 109 hydrocarbons and 132 halogenated hydrocarbons (total 241) a...
QSPR correlation equations were developed for the prediction of the solubilities of organic gases an...
Aqueous solubilities of polychlorinated biphenyls have been correlated with topological molecular de...
A simple QSPR model, based on seven 1D and 2D descriptors and artificial neural network, was develop...
This thesis focuses on the development of quantitative structure-activity relationship (QSPR) models...
It was the aim of the present work to develop a quantitative structure-property relationship (QSPR) ...
Two estimation methods for water solubility are compared for several sets of compounds. One method i...
Physico-chemical properties of chemicals are important data for exposure analysis. They can be estim...
Optimization of correlation weights of local graph invariants is an approach to model molecular prop...
Models are important tools for designing or redesigning water treatment processes and technologies t...
The relationship between aqueous activity coefficients (log γ(w)) and different physico-chemical pro...
We present an extended QSPR modeling of solubilities of about 500 substances in series of up to 69 d...
Non conformational QSPR models were built for the aqueous solubility (mol/L) at 25 °C of 5610 struct...
Accurate solubility prediction is crucial across a range of scientific disciplines including drug ...