2016
DOI: 10.1002/poc.3540
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Prediction of photolysis half-lives of dihydroindolizines by genetic algorithm-multiple linear regression (GA-MLR)

Abstract: Quantitative structure-property relationship study was carried out for the prediction of photolysis half-lives of dihydroindolizines. Genetic algorithm variable selection was used in the multiple linear regression modeling. Four descriptors including topological (AAC), 2D-Autocorrelations (GATS7m), 3D-MoRSE (Mor19p), and GETAWAY (HATS3p) descriptors were selected by the algorithm, revealing that a hybrid of topologic, geometric, and electronic features of the molecule affects photolysis half-life of studied ph… Show more

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Cited by 5 publications
(2 citation statements)
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“…By changing the method of neighborhood clustering in the improved algorithm in the process of experiment 171 verification method using neighborhood linear decreasing, can increase the calculation efficiency, this research will decrease to 4 from the 12 linear neighborhood [10].…”
Section: Methodsmentioning
confidence: 98%
“…By changing the method of neighborhood clustering in the improved algorithm in the process of experiment 171 verification method using neighborhood linear decreasing, can increase the calculation efficiency, this research will decrease to 4 from the 12 linear neighborhood [10].…”
Section: Methodsmentioning
confidence: 98%
“…In order to find out the optimal chromosome hidden in optimal chromosome under, through repeated use of genetic algorithms, the multiple optimization results and obtain a set of candidate features with frequency of SNP (Candidate SNP CSNP), according to the characteristics of SNP need to select a certain frequency, for the second phase of the research use. The author proposed multiple genetic algorithm based on mutual information (MGA) [9] to get a small number of CSNP collection, this collection as much as possible to remove the disease and unrelated to SNP, at the same time as much as possible to retain the true disease associated with SNP (GroundTruthSNP, GTSNP), to reduce the scale of SNP set objective. In the same way as the author's work, Miller proposes a method of finding the characteristic SNP set with the maximum entropy method (ME) [10] under certain constraints.…”
Section: Introductionmentioning
confidence: 99%