Recent research in molecular discovery has primarily been devoted to small, drug-like molecules, leaving many similarly important applications in material design without adequate technology. These applications often rely on more complex molecular structures with fewer examples that are carefully designed using known substructures. We propose a data-efficient and interpretable model for representing and reasoning over such molecules in terms of graph grammars that explicitly describe the hierarchical design space featuring motifs to be the design basis. We present a novel representation in the form of random walks over the design space, which facilitates both molecule generation and property prediction. We demonstrate clear advantages over existing methods in terms of performance, efficiency, and synthesizability of predicted molecules, and we provide detailed insights into the method's chemical interpretability.

Advances in computational design and manufacturing (CDaM) have already permeated and transformed numerous industries, including aerospace, architecture, electronics, dental, and digital media, among others. Nevertheless, the full potential of the CDaM workflow is still limited by a number of barriers, such as the extensive domainspecific knowledge that is often required to use CDaM software packages or integrate CDaM solutions into existing workflows. Generative AI tools such as Large Language Models (LLMs) have the potential to remove these barriers, by expediting the CDaM process and providing an intuitive, unified, and user-friendly interface that connects each stage of the pipeline. However, to date, generative AI and LLMs have predominantly been applied to non-engineering domains. In this study, we show how these tools can also be used to develop new design and manufacturing workflows.