Here, we present the AA-QSPR Db (Amino Acid Quantitative Structure Property Relationship Database): a novel, freely available web-resource of data pertaining to amino acids, both engineered and naturally occurring. In addition to presenting fundamental molecular descriptors of size, charge and hydrophobicity, it also includes online visualization tools for users to perform instant, interactive ...

A benchmark of several popular methods, Associative Neural Networks (ANN), Support Vector Machines (SVM), k Nearest Neighbors (kNN), Maximal Margin Linear Programming (MMLP), Radial Basis Function Neural Network (RBFNN), and Multiple Linear Regression (MLR), is reported for quantitative-structure property relationships (QSPR) of stability constants logK1 for the 1:1 (M:L) and logbeta2 for 1:2 c...

To date, most reported quantitative structure-property relationship (QSPR) methods to predict vapor pressure rely on, at least, some empirical data, such as boiling points, critical pressures, and critical temperatures. This limits their usefulness to available chemicals and incurs the time and expense of experimentation. A model to predict vapor pressure from only computationally derived molec...

A volunteer computing approach is presented for the purpose of screening a large number of molecular structures with respect to their suitability as new battery electrolyte solvents. Collective properties like melting, boiling and flash points are evaluated using COSMOtherm and quantitative structure-property relationship (QSPR) based methods, while electronic structure theory methods are used ...

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...

A quantitative structure-property relationship (QSPR) study is suggested for the prediction of retention times of volatile organic compounds. Various kinds of molecular descriptors were calculated to represent the molecular structure of compounds. Modeling of retention times of these compounds as a function of the theoretically derived descriptors was established by multiple linear regression (...

A quantitative structure-property relationship (QSPR) study is suggested for the prediction of retention times of volatile organic compounds. Various kinds of molecular descriptors were calculated to represent the molecular structure of compounds. Modeling of retention times of these compounds as a function of the theoretically derived descriptors was established by multiple linear regression (...

Quantitative structure-property relationships (QSPRs) aid in understanding molecular properties as a function of structure. When the correlation between structure and property weakens, dataset is described ``rough,'' but...

The paper describe the main problems concerning the creating of a client-server application using Borland Delphi environment which are used to find Quantitative Structure – Activity and Structure – Property Relationships using structure descriptors and measured activities/properties for molecules sets stored into a MySQL database server. The described application was used on a set of organic ph...

a robust and reliable quantitative structure-property relationship (qspr) study was established to forecast the melting points (mps)  of a diverse and long set including 250 drug-like compounds. based on the calculated descriptors by dragon software package, to detect homogeneities and to split the whole dataset into training and test sets, a principal component analysis (pca) approach was used...