RELATION: A Deep Generative Model for Structure-Based De Novo Drug Design

Deep
learning (DL)-based de novo molecular design has recently
gained considerable traction. Many DL-based generative models have
been successfully developed to design novel molecules, but most of
them are ligand-centric and the role of the 3D geometries of target
binding pockets in molecular generation has not been well-exploited.
Here, we proposed a new 3D-based generative model called RELATION.
In the RELATION model, the BiTL algorithm was specifically designed
to extract and transfer the desired geometric features of the protein–ligand
complexes to a latent space for generation. The pharmacophore conditioning
and docking-based Bayesian sampling were applied to efficiently navigate
the vast chemical space for the design of molecules with desired geometric
properties and pharmacophore features. As a proof of concept, the
RELATION model was used to design inhibitors for two targets, AKT1
and CDK2. The calculation results demonstrated that the RELATION model
could efficiently generate novel molecules with favorable binding
affinity and pharmacophore features.