Mark B. Gerstein

Efficient privacy-preserving training of quantum neural networks through ensemble encoding of quantum states

Learning Disentangled Representations with Quantum Variational Autoencoders

Variational autoencoders are powerful representation learning models that map complex data into low-dimensional latent spaces, enabling the discovery of interpretable and disentangled factors. Such representations can facilitate the interpretation and controllable generation of data describing complex scientific system...

DreamFold: A World Model to efficiently generate protein folding pathways in the latent space

Overall, DreamFold facilitates the study of protein dynamics by generative AI with applications in structure‐based drug discovery, and allows us to identify folding intermediates with cryptic binding sites that are therapeutically valuable.