2024 : 4 : 14
Amir Azizi

Amir Azizi

Academic rank: Assistant Professor
ORCID: https://orcid.org/0000-0003-2741-6797
Education: PhD.
ScopusId: 56318653900
Faculty: Science
Address: Arak University


QSAR Prediction of Aqueous Solubility's Of Some Pharmaceutical Compounds by Chemometrics Methods
Solubility, Pharmaceutical, PLS, OSC-PLS, LS-SVM, WHIM, GETAWAY
Journal Pakistan Journal of Chemistry
Researchers Amir Azizi ، Ali Niazi ، sadaf Mahmoudzadeh ، vahid najafi


A quantitative structure–activity relationships (QSAR) study is suggested for the prediction of solubility of pharmaceutical compounds in aqueous solution by using chemometrics methods. Ab initio theory was used to calculate some quantum chemical descriptors including electrostatic potentials and local charges at each atom, HOMO and LUMO energies, etc. Also, Dragon software was used to calculate some descriptors such as WIHM and GETAWAY. QSAR studies are mathematical quantification of relations between structure and activity or property. These are extensively used in pharmaceutical and agricultural chemistry for screening potential compounds for specific biological activity. Computable molecular descriptors are preferred to experimental properties in QSAR analyses because require molecular structure as the only input and can be in expensively calculated for a chemical in less than a millisecond. By multivariate calibration methods such as partial least squares (PLS) regression and least squares support vector analysis (LS-SVM), it is possible to obtain a model adjusted to the concentration values of the mixtures used in the calibration range. Orthogonal signal/descriptor correction (OSC/ODC) is a preprocessing technique used for removing the information unrelated to the target variables based on constrained principal component analysis. OSC is a suitable preprocessing method for PLS calibration of mixtures without loss of prediction capacity using cited descriptors. The root mean square error of prediction (RMSEP) was also quite acceptable for OSC-PLS (0.0095) and LS-SVM (0.0023)