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MGM_v3.pdf (1.68 MB)

Masked Graph Modeling for Molecule Generation

preprint
revised on 31.03.2021, 21:04 and posted on 01.04.2021, 09:06 by Omar Mahmood, Elman Mansimov, Richard Bonneau, Kyunghyun Cho
De novo, in-silico design of molecules is a challenging problem with applications in drug discovery and material design. We introduce a masked graph model, which learns a distribution over graphs by capturing conditional distributions over unobserved nodes (atoms) and edges (bonds) given observed ones. We train and then sample from our model by iteratively masking and replacing different parts of initialized graphs.
We evaluate our approach on the QM9 and ChEMBL datasets using the GuacaMol distribution-learning benchmark. We find that validity, KL-divergence and Fréchet ChemNet Distance scores are anti-correlated with novelty, and that we can trade off between these metrics more effectively than existing models. On distributional metrics, our model outperforms previously proposed graph-based approaches and is competitive with SMILES-based approaches. Finally, we show our model generates molecules with desired values of specified properties while maintaining physiochemical similarity to the
training distribution.

History

Email Address of Submitting Author

onm217@nyu.edu

Institution

New York University

Country

USA

ORCID For Submitting Author

0000-0002-4437-5416

Declaration of Conflict of Interest

No conflict of interest

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