Shape-Based Generative Modeling for de Novo Drug Design

In
this work, we propose a machine learning approach to generate
novel molecules starting from a seed compound, its three-dimensional
(3D) shape, and its pharmacophoric features. The pipeline draws inspiration
from generative models used in image analysis and represents a first
example of the de novo design of lead-like molecules guided by shape-based
features. A variational autoencoder is used to perturb the 3D representation
of a compound, followed by a system of convolutional and recurrent
neural networks that generate a sequence of SMILES tokens. The generative
design of novel scaffolds and functional groups can cover unexplored
regions of chemical space that still possess lead-like properties.