The only input this model requires is the SMILES representation of candidate molecules. The predictions are available almost instantaneously (very much unlike for a physics-based model via the Lorentz-Lorenz equation parametrized by inputs from quantum chemistry and molecular dynamics calculations). The results of this ML model are comparable with those of other data-derived prediction models in terms of diversity of molecular candidates and the accuracy of predictions. The current implementation also enables the user to retrain each model for a better or more generalizable prediction power. Moreover, a trained model can leverage other relevant ML models through the concept of transfer learning design methodologies.