| Autor: |
Simm, Gregor N. C., Hernández-Lobato, José Miguel |
| Rok vydání: |
2019 |
| Předmět: |
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| Zdroj: |
Proceedings of the 37th International Conference on Machine Learning, Vienna, Austria, PMLR 119, 2020 |
| Druh dokumentu: |
Working Paper |
| Popis: |
Great computational effort is invested in generating equilibrium states for molecular systems using, for example, Markov chain Monte Carlo. We present a probabilistic model that generates statistically independent samples for molecules from their graph representations. Our model learns a low-dimensional manifold that preserves the geometry of local atomic neighborhoods through a principled learning representation that is based on Euclidean distance geometry. In a new benchmark for molecular conformation generation, we show experimentally that our generative model achieves state-of-the-art accuracy. Finally, we show how to use our model as a proposal distribution in an importance sampling scheme to compute molecular properties. |
| Databáze: |
arXiv |
| Externí odkaz: |
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