Unified Guidance for Geometry-Conditioned Molecular Generation

TL;DR

UniGuide is a versatile framework that enables controlled, geometry-conditioned molecular generation using diffusion models, without additional training, improving adaptability across various drug design applications.

Contribution

The paper introduces UniGuide, a novel framework for flexible geometric guidance in diffusion-based molecular generation, eliminating the need for extra training or networks.

Findings

Achieves on-par or superior performance compared to specialized models

Enables flexible conditioning for various drug design tasks

Streamlines development of adaptable molecular generative models

Abstract

Effectively designing molecular geometries is essential to advancing pharmaceutical innovations, a domain, which has experienced great attention through the success of generative models and, in particular, diffusion models. However, current molecular diffusion models are tailored towards a specific downstream task and lack adaptability. We introduce UniGuide, a framework for controlled geometric guidance of unconditional diffusion models that allows flexible conditioning during inference without the requirement of extra training or networks. We show how applications such as structure-based, fragment-based, and ligand-based drug design are formulated in the UniGuide framework and demonstrate on-par or superior performance compared to specialised models. Offering a more versatile approach, UniGuide has the potential to streamline the development of molecular generative models, allowing them to be readily used in diverse application scenarios.