HybridLinker: Topology-Guided Posterior Sampling for Enhanced Diversity and Validity in 3D Molecular Linker Generation
Journal:
arXiv
Published Date:
Feb 24, 2025
Abstract
Linker generation is critical in drug discovery applications such as lead
optimization and PROTAC design, where molecular fragments are assembled into
diverse drug candidates. Existing methods fall into PC-Free and PC-Aware
categories based on their use of 3D point clouds (PC). PC-Free models
prioritize diversity but suffer from lower validity due to overlooking PC
constraints, while PC-Aware models ensure higher validity but restrict
diversity by enforcing strict PC constraints. To overcome these trade-offs
without additional training, we propose HybridLinker, a framework that enhances
PC-Aware inference by providing diverse bonding topologies from a pretrained
PC-Free model as guidance. At its core, we propose LinkerDPS, the first
diffusion posterior sampling (DPS) method operating across PC-Free and PC-Aware
spaces, bridging molecular topology with 3D point clouds via an energy-inspired
function. By transferring the diverse sampling distribution of PC-Free models
into the PC-Aware distribution, HybridLinker significantly and consistently
surpasses baselines, improving both validity and diversity in foundational
molecular design and applied property optimization tasks, establishing a new
DPS framework in the molecular and graph domains beyond imaging.
