Unified Guidance for Geometry-Conditioned Molecular Generation

Sirine Ayadi, Leon Hetzel, Johanna Sommer, Fabian J Theis, Stephan Günnemann

Keywords: generative models, conditional diffusion

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.

Primary Area: Machine learning for physical sciences (for example: climate, physics)

Submission Number: 11066