A study of boosting molecular descriptors with quantum-derived features in prediction of maximum emission wavelengths of chromophores.

The following research assesses the capability of machine learning in predicting maximum emission wavelengths of organic compounds. The predictions are based on molecular descriptors and fingerprints widely applied in cheminformatics. In an effort to further improve accuracy, developed machine learning models were enriched with quantum mechanics derived features. Multi linear, gradient boosting and random forest regressions were applied. Computers were trained and tested with database of experimental data of optical properties.