Fluorescent molecules are widely used for bio-imaging. They are attached to specific cell organelles and/or proteins, enabling observation of detailed structure and dynamics in the cell. Efficient fluorescent molecules must have a high quantum yield for effective bio-imaging. Here, we present a systematic approach to discovering novel fluorescent molecules that combines machine-learning and global optimization algorithms. We recast the problem of discovering novel fluorescent molecules with high-intensity emission light into a global optimization problem by using the oscillator strength of a molecule as an objective function for optimization.<br/>A statistical machine that predicts excitation energies and associated oscillator strengths, the probability of absorption or emission of light in transitions between different energy states, of a molecule were trained using the random forest algorithm. The PubchemQC database, which contains TD-DFT calculation results of 3.8 million known molecules, was used as a training set. The extended connectivity fingerprints of molecules were used as input vectors. To optimize the oscillator strength of a molecule, a highly efficient global optimization algorithm called CSA was used. For CSA global optimization, SMILES representation of a molecule was mapped to a 200-dimensional integer vector by using Natural Language Toolkit. After CSA global optimization calculation converged, we assessed the validity of our approach by performing quantum mechanical calculations. TD-DFT calculations were carried out to verify whether novel molecules obtained by this procedure actually have high oscillator strength.

Discovering novel fluorescent molecules via global optimization of molecular properties

ACS Fall 2020 Virtual Meeting

The official archive and repository for all ACS Meetings & Expositions abstracts and select posters or presentations.