Molexar is a unified multimodal molecular foundation model built on Fragment-SELFIES that uses pretraining followed by supervised fine-tuning with in-place condition embedding to handle scalar properties, pharmacophores, proteins, and pockets in one autoregressive path.
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Sesame introduces spatial density-map conditioning and a pairformer module in a diffusion framework to enable de novo and scaffold-conditioned molecular generation for drug design.
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Molexar: A Unified Multimodal Molecular Foundation Model for Drug Design
Molexar is a unified multimodal molecular foundation model built on Fragment-SELFIES that uses pretraining followed by supervised fine-tuning with in-place condition embedding to handle scalar properties, pharmacophores, proteins, and pockets in one autoregressive path.
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Sesame: Structure-Aware Molecular Generation via Spatial Density-Map Conditioning
Sesame introduces spatial density-map conditioning and a pairformer module in a diffusion framework to enable de novo and scaffold-conditioned molecular generation for drug design.