Unified Guidance for Geometry-Conditioned Molecular Generation
This work addresses the need for adaptable molecular generation in pharmaceutical innovations, offering a versatile approach that could streamline development, though it appears incremental as it builds on existing diffusion models.
The authors tackled the problem of inflexible molecular diffusion models by introducing UniGuide, a framework for controlled geometric guidance that enables flexible conditioning during inference without extra training, achieving on-par or superior performance compared to specialized models in tasks like structure-based and ligand-based drug design.
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.