Better prediction of aqueous solubility of chlorinated hydrocarbons using support vector machine modeling
作者： Behnoosh Bahadori , Morteza Atabati , Kobra Zarei
DOI: 10.1007/S10311-016-0561-7
关键词: Feature selection 、 Aqueous solubility 、 Support vector machine 、 Organic chemistry 、 Pollutant 、 Partial least squares regression 、 Principal component regression 、 Computational chemistry 、 Chemistry 、 Quantitative Structure Property Relationship
摘要: Remediation of water contaminated by organic pollutants is a major challenge, which could be improved better knowledge on the aqueous solubility compounds. Indeed, controls fate and toxicity pollutants. Here we performed structure–property study based genetic algorithm for prediction chlorinated hydrocarbons. 1497 descriptors were calculated with Dragon software. The variable selection method was used to select an optimal subset that have significant contribution overall solubility, from large pool descriptors. support vector machine then employed model possible quantitative relationships between selected solubility. Our results show total size, polarizability electronegativity modify We also found gave than other methods such as principal component regression partial least squares.
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