2019

Molecular geometry prediction using a deep generative graph neural network

Mansimov, Elman, Mahmood, Omar, Kang, Seokho et al.

Understand

A molecule's geometry, also known as conformation, is one of a molecule's most important properties, determining the reactions it participates in, the bonds it forms, and the interactions it has with other molecules.

Conventional conformation generation methods minimize hand-designed molecular force field energy functions that are often not well correlated with the true energy function of a molecule observed in nature.
They generate geometrically diverse sets of conformations, some of which are very similar to the lowest-energy conformations and others of which are very different.
In this paper, we propose a conditional deep generative graph neural network that learns an energy function by directly learning to generate molecular conformations that are energetically favorable and more likely to be observed experimentally in data-driven manner.

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Then

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Molecular geometry prediction using a deep generative graph neural network
Original
Mansimov, E., Mahmood, O., Kang, S. & Cho, K · 2019
Closest in time.

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