Unified Guidance for Geometry-Conditioned Molecular Generation • UniGuide introduces a unified framework for geometry-conditioned molecular generation using diffusion models. It provides a flexible method for controlled molecular design, eliminating the need for additional training or specialized networks. • Key innovation: UniGuide employs “condition maps” to translate geometric constraints into actionable guidance, enabling applications in structure-based, fragment-based, and ligand-based drug design with a single model. • Unlike traditional conditional models, UniGuide excels in handling diverse geometric constraints like surfaces, densities, and volumes. This versatility allows it to generalize across a range of tasks without the limitations of task-specific models. • Performance benchmarks show UniGuide’s superior or comparable results to specialized approaches in drug discovery tasks, demonstrating its adaptability and efficiency. • Applications include ligand-based drug design (LBDD) for generating novel molecules that match predefined shapes, structure-based drug design (SBDD) for ligand-pocket interactions, and fragment-based drug design (FBDD) for linking and growing molecular fragments. • UniGuide’s method decouples training from conditioning, making it robust in data-scarce scenarios. It demonstrates state-of-the-art performance across ligand similarity, binding affinity, and drug-likeness metrics. @guennemann @fabian_theis @j_m_sommer @leon_het @sirine_ayadi1 📜Paper: openreview.net/pdf/704d9702f… #MolecularDesign #DrugDiscovery #GeometryConditioning #AIinBiology #GenerativeModels