Representing Molecules as Random Walks Over Interpretable Grammars
<|reference_start|>Representing Molecules as Random Walks Over Interpretable Grammars: 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.<|reference_end|>
@article{sun2024representing,
    title={Representing Molecules as Random Walks Over Interpretable Grammars},
    author={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 and Wojciech
  Matusik},
    journal={ICML 2024},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.08147},
    primaryClass={cs.LG q-bio.BM}
}