Quantitative structure-property relationship prediction of gas-to-chloroform partition coefficient using artificial neural network was written by Golmohammadi, Hassan;Safdari, Majid. And the article was included in Microchemical Journal in 2010.Application In Synthesis of Ethyl pyrazine-2-carboxylate This article mentions the following:
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. In the experiment, the researchers used many compounds, for example, Ethyl pyrazine-2-carboxylate (cas: 6924-68-1Application In Synthesis of Ethyl pyrazine-2-carboxylate).
Ethyl pyrazine-2-carboxylate (cas: 6924-68-1) belongs to pyrazine derivatives. Pyrazine is an N-heterocyclic moiety, and it can be easily prepared from ethylenediamine and 1,2-diketone, α-hydroxyketone, α-methyl ketone. Pyrazine heterocycles and their benzo derivatives possess many interesting properties, including chemical reactivity profiles, and have diverse applications in total synthesis, medicine, chemical biology, materials, dyes, and imaging.Application In Synthesis of Ethyl pyrazine-2-carboxylate