Golmohammadi, Hassan published the artcileQuantitative structure-property relationship prediction of gas-to-chloroform partition coefficient using artificial neural network, Safety of Ethyl pyrimidine-2-carboxylate, the main research area is gas chloroform QSPR partition coefficient artificial neural network.
A quant. structure-property relationship (QSPR) study based on an artificial neural network (ANN) was carried out for the prediction of the gas-to-chloroform partition coefficients of a set of 338 compounds of a very different chem. nature. The genetic algorithm-partial least squares (GA-PLS) method was used as a variable selection tool. A PLS method was used to select the best descriptors and the selected descriptors were used as input neurons in neural network model. These descriptors are Gravitation index for all bonded pairs of atoms (G 2), Final heat of formation (ΔH f), Total hybridization components of the mol. dipole (μ h), DPSA-3 Difference in CPSAs (DPSA-3) and Structural Information content (order 1) (1SIC). The results obtained showed the ability of developed artificial neural networks to predict of gas-to-chloroform partition coefficients of various compounds Also this demonstrates the advantages of ANN.
Microchemical Journal published new progress about Formation enthalpy. 42839-08-7 belongs to class pyrimidines, name is Ethyl pyrimidine-2-carboxylate, and the molecular formula is C7H8N2O2, Safety of Ethyl pyrimidine-2-carboxylate.
Referemce:
Pyrimidine | C4H4N2 – PubChem,
Pyrimidine – Wikipedia