Optimization of correlation weights of local graph invariants is an approach to model molecular properties and/or activities of chemical or/and biological interest. The essence of the approach may be described by means of three main steps: first, a descriptor which is a function of the weights of local graph invariants must be defined by the suitable choice among the different possibilities from the pool of molecular descriptors; second, correlation weights values which produce as large as possible correlation coefficient value between the selected property values and the descriptor data under consideration are calculated by Monte Carlo optimization procedure (the correlation coefficient is used as the quality objective function); third, a ...
We report the results derived from the use of molecular descriptors calculated with the correlation ...
Non conformational QSPR models were built for the aqueous solubility (mol/L) at 25 °C of 5610 struct...
Random Forest regression (RF), Partial-Least-Squares (PLS) regression, Support Vector Machines (SVM)...
This report presents the best possible QSPR models for predicting the solubility of aliphatic alcoho...
Aqueous solubilities of polychlorinated biphenyls have been correlated with topological molecular de...
We report the calculation of boiling points for several alkyl alcohols through the use of improved m...
Correlation for estimation of the aqueous solubility (logSw) of chlorinatedhydrocarbons molecules is...
A simple QSPR model, based on seven 1D and 2D descriptors and artificial neural network, was develop...
ABSTRACT The weighted holistic invariant molecular-three dimensional-quantitative structure property...
International audienceThe solubility parameter is considered to be a significant parameter for the ch...
We present an extended QSPR modeling of solubilities of about 500 substances in series of up to 69 d...
This paper attempts to elucidate differences in QSPR models of aqueous solubility (Log S), melting p...
The development of QSPR models to predict aqueous solubility (logS) is presented. A structurally div...
Quantitative Structure-Activity Relationships based on molecular descriptors calculated with Correla...
A Quantitative Structure-Retention Relationship (QSRR) is proposed to estimate the chromatographic r...
We report the results derived from the use of molecular descriptors calculated with the correlation ...
Non conformational QSPR models were built for the aqueous solubility (mol/L) at 25 °C of 5610 struct...
Random Forest regression (RF), Partial-Least-Squares (PLS) regression, Support Vector Machines (SVM)...
This report presents the best possible QSPR models for predicting the solubility of aliphatic alcoho...
Aqueous solubilities of polychlorinated biphenyls have been correlated with topological molecular de...
We report the calculation of boiling points for several alkyl alcohols through the use of improved m...
Correlation for estimation of the aqueous solubility (logSw) of chlorinatedhydrocarbons molecules is...
A simple QSPR model, based on seven 1D and 2D descriptors and artificial neural network, was develop...
ABSTRACT The weighted holistic invariant molecular-three dimensional-quantitative structure property...
International audienceThe solubility parameter is considered to be a significant parameter for the ch...
We present an extended QSPR modeling of solubilities of about 500 substances in series of up to 69 d...
This paper attempts to elucidate differences in QSPR models of aqueous solubility (Log S), melting p...
The development of QSPR models to predict aqueous solubility (logS) is presented. A structurally div...
Quantitative Structure-Activity Relationships based on molecular descriptors calculated with Correla...
A Quantitative Structure-Retention Relationship (QSRR) is proposed to estimate the chromatographic r...
We report the results derived from the use of molecular descriptors calculated with the correlation ...
Non conformational QSPR models were built for the aqueous solubility (mol/L) at 25 °C of 5610 struct...
Random Forest regression (RF), Partial-Least-Squares (PLS) regression, Support Vector Machines (SVM)...