Journal
INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH
Volume 61, Issue 32, Pages 12052-12060Publisher
AMER CHEMICAL SOC
DOI: 10.1021/acs.iecr.2c01121
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Funding
- National Natural Science Foundation for Excellent Young Scientists of China [22122802]
- National Natural Science Foundation of China [21878028]
- Chongqing Joint Chinese Medicine Scientific Research Project [2020ZY023984]
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This study combines genetic algorithm with random forest algorithm to select suitable molecular feature combination, significantly reducing the number of descriptors required for model development, and utilizes a combination of backpropagation neural network and Bayesian optimization for intelligent tuning of relevant model parameters.
The octanol-water partition coefficient (K-ow) is an extremely important and widely used parameter for the study of the distribution and balance of organic pollutants in environmental media. Therefore, the theoretical determination and prediction of such a property are vital in environmental chemistry. The past research on the use of models based on the quantitative structure- property relationship (QSPR) for the estimation of K-ow has significant problems such as data redundancy and computational complexity in the molecular description. In this work, the genetic algorithm is coupled with the random forest algorithm to select the most suitable molecular feature combination from a vast number of features. As a consequence, the number of descriptors for developing the model recommended in available models is significantly reduced. Moreover, a combination of the backpropagation neural network and Bayesian optimization allows the development of an intelligent procedure for tuning the relevant model parameters. On the basis of comparison of the obtained estimations to the results of the available QSPR models in the literature, the developed model in this work shows considerably higher accuracy and predictability.
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