Abstract
The bottom-up design and synthesis of organic molecular species with tailored photophysical properties stands as an important challenge to both computational and experimental chemical science. Overcoming this challenge presents the potential to usher in new tools and approaches to improve our ability to develop new technologies, such as molecular sensors and attuned molecular switches. Here, we report the bottom-up design and characterisation of new fluorescent maleimide derivatives using coupled computational and experimental insights. Using an extensive set of experimentally-measured UV/visible spectra for different functionalized maleimides in different solvents, we train an artificial neural network (ANN) to rapidly correlate maleimide structure (and solvent) with emission spectra characteristics. We subsequently use this computational predictor to identify design principles for novel functionalised maleimide structures with targeted photophysical properties; synthesis and characterisation of several new maleimides demonstrates how this combined strategy can offer new directions for tuning photochemistry, for example opening new routes to designing tailor-made fluorescent probes.