Representing Molecules as Random Walks Over Interpretable Grammars
This work addresses material design applications that rely on complex molecular structures with limited data, offering an interpretable and data-efficient solution.
The paper tackled the problem of representing complex molecules for material design by proposing a model based on graph grammars and random walks, which improved performance, efficiency, and synthesizability over existing methods.
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.