AI-Empowered Universal Workflow for Molecular Design of Performant Photoswitches

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The design of molecules with tailored properties and functions is central to a wide range of applications from agriculture and medicine to materials, energy, and information. This design process is traditionally rather an Edisonian-type discovery in which incomplete human understanding and working hypotheses are used to prioritize restricted search spaces. Thanks to high-throughput experimentation and descriptor-based predictive-quality computations these search spaces could gradually be extended. However, they are still insignificant compared to the vastness of the chemical space. This proposal therefore aims at leveraging modern artificial intelligence (AI) methodology to establish a purposeful, actual design process. Provided with sufficient amounts of data, generative models can implicitly learn the underlying structure-property relationships and then directly propose innovative molecular designs that fulfill targeted properties. A fundamental challenge is the huge amount of data generally required by such deep learning. To this end, we will exploit transfer learning concepts to reduce the amount of domain-specific data needed, develop computationally most efficient descriptors to increase the availability of synthetic data, and generate extended experimental chemical libraries by adopting one-pot strategies and automatized workflows. Visualization and explainable AI analysis tools will finally be employed to convert the implicitly learned structure-property relationships into chemically interpretable knowledge. This will not least increase trust and acceptance, will provide important validation and feedback for the established AI framework, and establish transferable insight into governing principles that can be applied across a wider space of molecules and functionalities. As a highly challenging, but equally rewarding and pressing design problem, we specifically endeavor to design high-performance photoswitchable molecules. Optimizing often contradictory performance parameters like addressability, efficiency and robustness, the corresponding development of molecular photoswitches is a non-trivial and hitherto slow empirical process that thus constitutes a prototypical application case that will heavily benefit from an AI-empowered molecular design.