Latent-Guided Equivariant Diffusion for Controlled Structure-Based De Novo Ligand Generation

Tuan Le, Julian Cremer, Djork-Arné Clevert, Kristof T Schütt

Keywords: Equivariance, Diffusion, Generative Chemistry, Molecule Design, Structure-based Drug Discovery, Graph Neural Networks

Abstract:

We propose PoLiGenX for de novo ligand design using latent-conditioned, target-aware equivariant diffusion. Our model leverages the conditioning of the generation process on reference molecules within a protein pocket to produce shape-similar de novo ligands that can be used for target-aware hit expansion and hit optimization.
The results of our study showcase the efficacy of PoLiGenX in ligand design. Docking scores indicate that the generated ligands exhibit superior binding affinity compared to the reference molecule while preserving the shape. At the same time, our model maintains chemical diversity, ensuring the exploration of diverse chemical space. The evaluation of Lipinski's rule of five suggests that the sampled molecules possess a higher drug-likeness than the reference data. This constitutes an important step towards the controlled generation of therapeutically relevant de novo ligands tailored to specific protein targets.

Submission Number: 94