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Building and Validating Biomolecular Structure Models Using Deep Learning

MBZUAI ·

Daisuke Kihara from Purdue University presented a seminar at MBZUAI on using deep learning for biomolecular structure modeling. His lab is developing 3D structure modeling methods, especially for cryo-electron microscopy (cryo-EM) data. They are also working on RNA structure prediction and peptide docking using deep neural networks inspired by AlphaFold2. Why it matters: Applying advanced deep learning techniques to biomolecular structure prediction can accelerate drug discovery and our understanding of molecular functions.

Generative models, manifolds and symmetries: From QFT to molecules

MBZUAI ·

A DeepMind researcher presented work on incorporating symmetries into machine learning models, with applications to lattice-QCD and molecular dynamics. The work includes permutation and translation-invariant normalizing flows for free-energy estimation in molecular dynamics. They also presented U(N) and SU(N) Gauge-equivariant normalizing flows for pure Gauge simulations and its extensions to incorporate fermions in lattice-QCD. Why it matters: Applying symmetry principles to generative models could improve AI's ability to model complex physical systems relevant to materials science and other fields in the region.

MBZUAI and GenBio AI win UAE AI Award 2025 for their work to revolutionize drug discovery and biomedicine

MBZUAI ·

MBZUAI and GenBio AI have won the UAE AI Award 2025 in the AI Scientific Research category for their "Unified Protein Language Modeling Framework". The winning project enables AI to learn protein function, generate sequences, and predict 3D structures. The AI-driven simulation approach aims to accelerate drug development, reduce costs, and improve success rates. Why it matters: This award highlights the UAE's commitment to fostering AI innovation in biomedicine and drug discovery, positioning the region as a leader in AI-driven healthcare advancements.