A Generative Model for Molecular Distance Geometry
This work addresses the computational challenge of generating molecular conformations for researchers in computational chemistry and drug discovery, representing an incremental improvement with a novel method for a known bottleneck.
The paper tackles the problem of generating equilibrium states for molecular systems by introducing a probabilistic model that learns a low-dimensional manifold preserving local atomic geometry, achieving state-of-the-art accuracy on a new benchmark for molecular conformation generation.
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.