Representing Molecules as Random Walks Over Interpretable Grammars
Michael Sun Minghao Guo Weize Yuan Veronika Thost Crystal Elaine Owens Aristotle Franklin Grosz Sharvaa Selvan Katelyn Zhou Hassan Mohiuddin Benjamin J Pedretti Zachary P Smith Jie Chen Wojciech Matusik
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.