Prediction of pKa of Various Chemicals Using QSPR Models

Prediction of the acid dissociation constant (pKa) is useful for titrations of acid-base, extraction using a solvent, formation of complexes, etc. Quantitative structure property relationship (QSPR) relates descriptors to a property such as pKa. In this research, most of the studies for the prediction pKa are summarized for different chemical groups including different organic and drug materials in different solvents. Various mathematical methods including the GA-MLR, ANN, and SVR were used for the QSPR modeling of pKa. The data sets include ۱۳ to ۵۱۹ pKa values. The number of descriptors varies from ۱ to ۷ other than a research work that uses the pixel data of the molecular ۲D image as the descriptor. Different researchers recommend various descriptors for different data sets that cannot be used for other molecular classes. However, most of them are useful for the practical prediction of pKa of its molecular family without performing an experimental test.