In silico prediction of dermal penetration rate of chemicals from their molecular structural descriptors.
The dermal penetration rate of some volatile and non-volatile organic compounds was estimated by quantitative structure-activity relationship approaches by using interpretable molecular descriptors. Linear and nonlinear models were developed using multiple linear regressions (MLR) and artificial neural network (ANN) methods. Robustness and reliability of the constructed MLR and ANN models were evaluated by using the leave-one-out cross-validation method, which produces the statistics of Q(MLR)(2)=0.786, Q( ANN)(2)=0.833 for non-volatiles and Q(MLR)(2)=0.639, Q( ANN)(2)=0.712 for volatile compounds. Furthermore, the chemical applicability domains of these models were determined via leverage approach. The results of this study indicated the ability of developed QSAR models in the prediction of dermal penetration rate of various chemicals from their calculated molecular descriptors.
Laboratory of Chemometrics, Faculty of Chemistry, University of Mazandaran, Babolsar, Iran. Electronic address: mhfatemi@umz.ac.ir.
Mohammad H
MH
Fatemi
Laboratory of Chemometrics, Faculty of Chemistry, University of Mazandaran, Babolsar, Iran. Electronic address: h.malekzadeh@stu.umz.ac.ir.
Hanieh
H
Malekzadeh
D003198:Computer Simulation; D016014:Linear Models; D008954:Models, Biological; D015394:Molecular Structure; D016571:Neural Networks, Computer; D009930:Organic Chemicals / Q000737:chemistry / Q000378:metabolism*; D021281:Quantitative Structure-Activity Relationship; D012867:Skin / Q000378:metabolism; D012869:Skin Absorption
D009930:Organic Chemicals