{"slug": "sesame-structure-aware-molecular-generation-via-spatial-density-map-conditioning", "title": "Sesame: Structure-Aware Molecular Generation via Spatial Density-Map Conditioning", "summary": "Researchers introduced Sesame, a diffusion-based molecular generation model that conditions on partial molecular structure and protein pockets via spatial density maps, enabling both de novo drug design and fragment-based lead optimization. The model uses a novel spatial pairformer module and joint denoising of atom types, bonds, and positions, with trajectory finetuning to improve generation quality.", "body_md": "arXiv:2606.23856v1 Announce Type: new\nAbstract: Generative molecular models for drug design are a promising direction with much active research. In the next phase of computational drug design, such models will need to understand small molecule structure and protein-ligand interactions, and they will need to possess the machinery to generate molecules \\textit{de novo}. Incorporating each feature poses a critical challenge. Equally important, yet often treated as secondary, is the ability to grow a molecule from a partial starting point -- a scaffold or fragment supplied by a chemist -- which is the central operation of lead optimization. We present Sesame (Spatial Evoformer for a Structure-Aware Molecular Engine), a diffusion-based molecular generation model that leverages a novel spatial pairformer module to condition on partial molecular structure and the surrounding protein pocket, both expressed as continuous spatial density maps. This single conditioning mechanism supports both \\textit{de novo} generation and fragment-conditioned lead optimization, letting a medicinal chemist prune a hit to a scaffold and have Sesame grow it in productive ways. In addition to this module, we also introduce a diffusion framework for joint denoising of atom types, bond types, and positions, along with a trajectory finetuning scheme that trains on the model's own sampling rollouts to improve generation quality. Sesame is trained on a large corpus of ligand-only and protein-ligand datasets.", "url": "https://wpnews.pro/news/sesame-structure-aware-molecular-generation-via-spatial-density-map-conditioning", "canonical_source": "https://arxiv.org/abs/2606.23856", "published_at": "2026-06-24 04:00:00+00:00", "updated_at": "2026-06-24 04:29:06.159323+00:00", "lang": "en", "topics": ["machine-learning", "generative-ai", "ai-research"], "entities": ["Sesame", "Spatial Evoformer for a Structure-Aware Molecular Engine"], "alternates": {"html": "https://wpnews.pro/news/sesame-structure-aware-molecular-generation-via-spatial-density-map-conditioning", "markdown": "https://wpnews.pro/news/sesame-structure-aware-molecular-generation-via-spatial-density-map-conditioning.md", "text": "https://wpnews.pro/news/sesame-structure-aware-molecular-generation-via-spatial-density-map-conditioning.txt", "jsonld": "https://wpnews.pro/news/sesame-structure-aware-molecular-generation-via-spatial-density-map-conditioning.jsonld"}}