Issue 9, 2022

Generating 3D molecules conditional on receptor binding sites with deep generative models

Abstract

The goal of structure-based drug discovery is to find small molecules that bind to a given target protein. Deep learning has been used to generate drug-like molecules with certain cheminformatic properties, but has not yet been applied to generating 3D molecules predicted to bind to proteins by sampling the conditional distribution of protein–ligand binding interactions. In this work, we describe for the first time a deep learning system for generating 3D molecular structures conditioned on a receptor binding site. We approach the problem using a conditional variational autoencoder trained on an atomic density grid representation of cross-docked protein–ligand structures. We apply atom fitting and bond inference procedures to construct valid molecular conformations from generated atomic densities. We evaluate the properties of the generated molecules and demonstrate that they change significantly when conditioned on mutated receptors. We also explore the latent space learned by our generative model using sampling and interpolation techniques. This work opens the door for end-to-end prediction of stable bioactive molecules from protein structures with deep learning.

Graphical abstract: Generating 3D molecules conditional on receptor binding sites with deep generative models

Supplementary files

Article information

Article type
Edge Article
Submitted
28 10 2021
Accepted
06 2 2022
First published
07 2 2022
This article is Open Access

All publication charges for this article have been paid for by the Royal Society of Chemistry
Creative Commons BY license

Chem. Sci., 2022,13, 2701-2713

Generating 3D molecules conditional on receptor binding sites with deep generative models

M. Ragoza, T. Masuda and D. R. Koes, Chem. Sci., 2022, 13, 2701 DOI: 10.1039/D1SC05976A

This article is licensed under a Creative Commons Attribution 3.0 Unported Licence. You can use material from this article in other publications without requesting further permissions from the RSC, provided that the correct acknowledgement is given.

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