In this study we present a simple algorithm based on the Partial Order Ranking (POR) technique which allows to rank a series of compounds according to their molecular descriptor values. A training set composed of 82 normal boiling points for structurally diverse organic compounds is analyzed by considering a pool of 1202 molecular descriptors obtained from the Dragon 5 software and two “flexible” type of variables. The predictive performance of the proposed approach is assessed by means of a test set of 82 “unknown” structurally related molecules.Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicada
We report the results of a calculation of the normal boiling points of a representative set of 200 o...
This thesis develops an integrated methodology based on the desirability index and QSAR models to vi...
In this paper we describe the application in QSPR/QSAR studies of a newgroup of molecular descriptor...
A new predictive method based on Partial Order Ranking is introduced in the realm of the QSPR–QSAR T...
We report the results of a calculation of the normal boiling points of a representative set of 200 o...
QSPR methods represent a useful approach in the drug discovery process, since they allow to predic...
Quantitative structure-property relationship (QSPR) models were derived for predicting boiling point...
The main objective of this paper is todescribe briefly the applications and methodologies involved i...
AbstractBasic chemometric methods for making empirical regression models for QSPR/QSAR are briefly d...
A systematic methodology for quantitative structure-activity relationship (QSAR) development in envi...
The often observed scarcity of physical-chemical and well as toxicological data hampers the assessme...
We predict the soil sorption coefficient for a heterogeneous set of 643 organic non-ionic compounds ...
The interplay between ‘noise-deficient’ QSAR and Partial Order Ranking, including analysis of averag...
We present the application of Cascade Correlation for structures to QSPR (quantitative structure-pro...
Quantitative Structure-Property Relationship models (QSPR) are predictive methods based on correlati...
We report the results of a calculation of the normal boiling points of a representative set of 200 o...
This thesis develops an integrated methodology based on the desirability index and QSAR models to vi...
In this paper we describe the application in QSPR/QSAR studies of a newgroup of molecular descriptor...
A new predictive method based on Partial Order Ranking is introduced in the realm of the QSPR–QSAR T...
We report the results of a calculation of the normal boiling points of a representative set of 200 o...
QSPR methods represent a useful approach in the drug discovery process, since they allow to predic...
Quantitative structure-property relationship (QSPR) models were derived for predicting boiling point...
The main objective of this paper is todescribe briefly the applications and methodologies involved i...
AbstractBasic chemometric methods for making empirical regression models for QSPR/QSAR are briefly d...
A systematic methodology for quantitative structure-activity relationship (QSAR) development in envi...
The often observed scarcity of physical-chemical and well as toxicological data hampers the assessme...
We predict the soil sorption coefficient for a heterogeneous set of 643 organic non-ionic compounds ...
The interplay between ‘noise-deficient’ QSAR and Partial Order Ranking, including analysis of averag...
We present the application of Cascade Correlation for structures to QSPR (quantitative structure-pro...
Quantitative Structure-Property Relationship models (QSPR) are predictive methods based on correlati...
We report the results of a calculation of the normal boiling points of a representative set of 200 o...
This thesis develops an integrated methodology based on the desirability index and QSAR models to vi...
In this paper we describe the application in QSPR/QSAR studies of a newgroup of molecular descriptor...