Generating 3D Molecules Conditional on Receptor Binding Sites with Deep Generative Models
This work addresses structure-based drug discovery by enabling deep learning to generate bioactive molecules from protein structures, representing a novel application in the field.
The authors tackled the problem of generating 3D molecular structures conditioned on receptor binding sites for drug discovery, using a conditional variational autoencoder trained on protein-ligand data, and demonstrated that generated molecules change significantly when conditioned on mutated receptors.
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.