Design in Voxel Space Decode in SMILES Space: Plixer Generates Drug-Like Molecules for Protein Pockets

Jude Wells, Brooks Paige

Keywords: structure-based drug design, generative models, machine learning for molecules, drug discovery, atomic density grids, language models

TL;DR: Generate small-molecule binders for protein pockets using 3D atomic density grids (voxels) and SMILES string decoder

Abstract: We introduce Plixer, a two-stage generative model for de novo drug design that generates small-molecule ligand binders conditioned on an empty protein pocket. Plixer combines a conditional voxel inpainting network to generate 3D ligand hypotheses with an independently trained voxel-to-SMILES decoder that translates these voxel representations into valid chemical structures. By decoupling the learning of spatial protein–ligand interactions from the learning of chemical grammar, our approach leverages large libraries of 3D ligand conformers to augment the limited data available for protein–ligand complexes. We show that this approach generates molecules with higher predicted binding affinity than recent methods.

Submission Number: 143