UV/visible absorption maxima prediction of water-soluble organic compounds and generation of library of new organic compounds.

In this study, UV/visible absorption maxima of organic compounds are predicted with the help of machine learning (ML). Four ML models are evaluated, the gradient boosting model has performed best. We also analyzed feature importance. Using Python-based tools, we generated and visualized a new set of 5,000 organic compounds. These compounds were screened based on their predicted UV/visible absorption maxima, selecting those with red-shifted absorption. The assessment of synthetic accessibility indicated that most of the chosen compounds are relatively easy to synthesize.