Developing non-linear rate constant QSPR using decision trees and multi-gene genetic programming. (4th August 2019)

• Record Type: Journal Article
• Authors: Datta, Shounak; Dev, Vikrant A.; Eden, Mario R.
• Is Part Of: Computers & chemical engineering. Volume 127(2019)
• Page Start: 150
• Page End: 157
• Publication Date: 2019-08-04
• Subjects: Multi-gene genetic programming -- Hybrid algorithm -- Nonlinear regression -- Machine learning -- Stochastic optimization; Chemical engineering -- Data processing -- Periodicals; 660.0285
• DOI: 10.1016/j.compchemeng.2019.05.013
• Languages: English
• ISSNs: 0098-1354

Abstract

Highlights: Development of non-linear QSPR model to predict diels alder reaction rate coefficient. Connectivity indices coupled with multivariate statistical methods used in model development. Multi-gene genetic programming (MGGP) was employed. Modifications of GPTIPS 2.0 toolbox was suggested for model generation with lowest predictive error possible. Abstract: Developing a QSPR model, which not only captures the influence of reactant structures but also the solvent effect on reaction rate, is of significance. Such QSPR models will serve as a prerequisite for the simultaneous computer-aided molecular design (CAMD) of reactants, products and solvents. They will also be useful in predicting the rate constant without entirely relying on experiments. To develop such a QSPR, recently, Datta et al. (2017) used the Diels-Alder reaction as a case study. Their model displayed great promise, but there is scope for improvement in the model's prediction metrics. In our work, we improve upon their model by introducing non-linearity. This is achieved using multi-gene genetic programming (MGGP). In our methodology, a combination of genetic algorithm (GA) and directed trees was used to develop a branched version of chromosomes, allowing increased possibility of generation of models with high prediction metrics. In our work, prior to model development through MGGP, principal component analysis (PCA) was conducted. Lastly, models were evaluated based on metrics such as R 2, Q 2, and RMSE.