Abstract for Oxford talk (eur26+ox)

TITLE: AI in Structural Bioinformatics: ABSTRACT : This talk covers AI methods in structural bioinformatics, with a focus on modeling protein flexibility and disorder. It introduces DreamFold, an AI "world model" that learns folding pathways in latent space, replacing a slower classical sampling approach (discard-and-restart). It presents machine-learning improvements to Kohn-Sham Hamiltonian estimation for faster DFT calculations on larger molecules. It also shows that ensembles of sequence-based deep learning models outperform individual predictors and 3D docking for drug screening. Finally, it addresses protein aggregation in disease (e.g., AD) via liquid-liquid phase separation (LLPS), using LLM embeddings and graph neural networks to predict LLPS-prone regions, intrinsically disordered regions (IDRs), and the effects of specific mutations.