Machine Learning Driven Design of Spiropyran Photoswitches

Abstract

This study presents the development and application of a generative machine learning model for the design of novel spiropyran photoswitches with enhanced switching speed and absorption bands with small spectral overlap between the open and closed form (i.e. high addressability). Leveraging a scaffold decoration approach, we fine-tuned a general chemical recurrent neural network (RNN) model on a curated dataset of photoswitches. The fine-tuned model was evaluated against both the pretrained baseline and literature-reported spiropyran compounds, demonstrating superior performance in generating diverse and novel candidates. Notably, the fine-tuned model effectively mitigates common biases in decoration patterns and functional group selection observed in the literature. The study also outlines the synthesis and experimental characterization of several newly designed spiropyran photoswitches, validating the design principles derived from the generative model. These findings highlight the potential of generative models in accelerating the discovery of advanced molecular photoswitches with tailored properties.