PhAME introduces compositional classifier-free guidance in a latent diffusion model for phenotype-aware molecular editing, claiming SOTA performance on docking and phenotypic benchmarks.
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SmilesGEN uses dual VAEs to jointly model drug structures and transcriptional responses, generating molecules with higher validity, novelty, and similarity to known ligands than prior methods.
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PhAME: Phenotype-Aware Molecular Editing via Latent Diffusion
PhAME introduces compositional classifier-free guidance in a latent diffusion model for phenotype-aware molecular editing, claiming SOTA performance on docking and phenotypic benchmarks.
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Bridging the phenotype-target gap for molecular generation via multi-objective reinforcement learning
SmilesGEN uses dual VAEs to jointly model drug structures and transcriptional responses, generating molecules with higher validity, novelty, and similarity to known ligands than prior methods.