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Sesame model advances structure-aware molecular generation for drug design

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced Sesame, a novel diffusion-based molecular generation model designed for drug discovery. This model uniquely conditions on partial molecular structures and protein-ligand interactions, expressed as spatial density maps. Sesame supports both *de novo* generation and fragment-conditioned lead optimization, allowing chemists to guide molecule growth from a starting scaffold. AI

IMPACT Enhances AI capabilities in drug discovery by enabling more precise and guided molecular generation.

RANK_REASON The cluster describes a new research paper detailing a novel AI model for molecular generation. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Sesame model advances structure-aware molecular generation for drug design

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The cluster describes a new research paper detailing a novel AI model for molecular generation. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Konstantin Yatsenko, Arvind Thiagarajan ·

    Sesame: Structure-Aware Molecular Generation via Spatial Density-Map Conditioning

    arXiv:2606.23856v1 Announce Type: new Abstract: 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 interac…

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