Learning a Continuous Representation of 3D Molecular Structures with Deep Generative Models

• URL: http://arxiv.org/abs/2010.08687v3
• Date: Sun, 15 Nov 2020 03:47:25 GMT
• Title: Learning a Continuous Representation of 3D Molecular Structures with Deep Generative Models
• Authors: Matthew Ragoza, Tomohide Masuda, David Ryan Koes
• Abstract summary: Generative models are an entirely different approach that learn to represent and optimize molecules in a continuous latent space. We describe deep generative models of three dimensional molecular structures using atomic density grids. We are also able to sample diverse sets of molecules based on a given input compound to increase the probability of creating valid, drug-like molecules.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Machine learning in drug discovery has been focused on virtual screening of molecular libraries using discriminative models. Generative models are an entirely different approach that learn to represent and optimize molecules in a continuous latent space. These methods have been increasingly successful at generating two dimensional molecules as SMILES strings and molecular graphs. In this work, we describe deep generative models of three dimensional molecular structures using atomic density grids and a novel fitting algorithm for converting continuous grids to discrete molecular structures. Our models jointly represent drug-like molecules and their conformations in a latent space that can be explored through interpolation. We are also able to sample diverse sets of molecules based on a given input compound and increase the probability of creating valid, drug-like molecules.

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