Abstract
Since predicting hydrolysis rates is necessary for estimating fates of chemical compounds in environment, computational or chemometrical procedures of predicting such velocities have been expected to construct. Recently, some category approaches were published in practical use. However, some kinds of chemical compounds which are difficult to classify into the one category in the case of those approaches. In addition, there are many compounds which cannot be classified into a certain category in the case. Thus, we thought a novel model, which uses no or a few categories of chemicals, was needed for estimating hydrolysis rate using many descriptors instead of category approaches. In order to construct such a model, before, “Chance Correlation Problem” was unavoidable. In this study, using Lasso and other our techniques, we tried to alleviate the problem and obtain more predictive models of hydrolysis rates. We used two criteria, Cross-Validation and Basian Information Criteria (BIC) for Lasso regularization. Relatively, BIC provided more predictive results. And when the chemicals were classified into a category, esters, better results were obtained. Now, better models are being under construction for eliminating more chance correlation descriptors.
