SynLaD uses a latent diffusion transformer with dual decoders to generate 3D molecules and their synthesis pathways conditioned on pharmacophore profiles, outperforming baselines on analogue generation tasks.
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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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SynLaD: Latent Diffusion for Generating Synthesizable Molecules Conditioned on 3D Pharmacophore Profiles
SynLaD uses a latent diffusion transformer with dual decoders to generate 3D molecules and their synthesis pathways conditioned on pharmacophore profiles, outperforming baselines on analogue generation tasks.
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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.