An Attempt to Boost 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 wavelength of organic compounds. The predictions are based on structure descriptors and fingerprints widely applied in cheminformatics. In an attempt 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.