Delete: Deep Lead Optimization Enveloped in Protein Pocket through Unified Deleting Strategies and a Structure-aware Network

TL;DR

This paper introduces Delete, a deep learning tool for lead optimization in drug discovery that unifies multiple subtasks and incorporates protein-ligand interactions to generate molecules with improved binding and drug-like properties.

Contribution

The paper presents a novel, unified deep learning framework that handles various lead optimization subtasks and models 3D protein-ligand interactions, advancing beyond existing methods.

Findings

Delete outperforms existing models in binding affinity improvement

Generates molecules with better drug-likeness and binding properties

Effective in fragment growing, linking, and replacement tasks

Abstract

Drug discovery is a highly complicated process, and it is unfeasible to fully commit it to the recently developed molecular generation methods. Deep learning-based lead optimization takes expert knowledge as a starting point, learning from numerous historical cases about how to modify the structure for better drug-forming properties. However, compared with the more established de novo generation schemes, lead optimization is still an area that requires further exploration. Previously developed models are often limited to resolving one (or few) certain subtask(s) of lead optimization, and most of them can only generate the two-dimensional structures of molecules while disregarding the vital protein-ligand interactions based on the three-dimensional binding poses. To address these challenges, we present a novel tool for lead optimization, named Delete (Deep lead optimization enveloped in protein pocket). Our model can handle all subtasks of lead optimization involving fragment growing, linking, and replacement through a unified deleting (masking) strategy, and is aware of the intricate pocket-ligand interactions through the geometric design of networks. Statistical evaluations and case studies conducted on individual subtasks demonstrate that Delete has a significant ability to produce molecules with superior binding affinities to protein targets and reasonable drug-likeness from given fragments or atoms. This feature may assist medicinal chemists in developing not only me-too/me-better products from existing drugs but also hit-to-lead for first-in-class drugs in a highly efficient manner.