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Conformational sampling in protein structure prediction models, dynamics-driven de novo binder design, and latent space manipulation for exploring protein conformational landscapes.

Building and applying genomic foundation models such as Nucleotide Transformer and Evo to learn the regulatory grammar of genomes, enabling functional sequence prediction and rational genome design across organisms.

Applying modern deep learning techniques to diverse biological problems, bridging computational methods with experimental validation.