@inproceedings{
ayadi2024unified,
title={Unified Guidance for Geometry-Conditioned Molecular Generation},
author={Sirine Ayadi and Leon Hetzel and Johanna Sommer and Fabian J Theis and Stephan G{\"u}nnemann},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=HeoRsnaD44}
}
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.
Unified Guidance for Geometry-Conditioned Molecular Generation
Sirine Ayadi Leon Hetzel Johanna Sommer Fabian J Theis Stephan Günnemann