Enhanced prediction of ionic liquid toxicity using a meta-ensemble learning framework with data augmentation

Abstract

Ionic liquids are unique in their properties and potential to be green solvents. Still, the toxicity concern remains, compelling the need for excellent predictive models for safe design and application. This work reports the introduction of a general, robust meta-ensemble learning framework for predicting the toxicity of ionic liquids using molecular descriptors and fingerprints. The proposed model incorporates the Random Forest, Support Vector Regression, Categorical Boosting, Chemical Convolutional Neural Network as a base classifier and an Extreme Gradient Boosting meta-classifier. The framework uses Recursive Feature Elimination for feature selection and GridSearchCV for tuning the best hyperparameters. Without augmentation of the data, the RMSE equals 0.38, MAE equals 0.29, coefficient of determination ($R^2$) equals 0.87, and Pearson correlation equals 0.94. Data augmentation further improved model performance: RMSE = 0.06, MAE = 0.024, $R^2$ = 0.99, and a Pearson correlation of 0.99. In addition, this indicates that the data-augmented model outperforms all existing models with prominence in its strength and prediction capacity. Thus, the present framework provides a superior tool for computer-aided molecular design of safer and more effective ionic liquids.