ENAR 2026 Spring Meeting

Generative Models for Protein Structures and Biomedical Data

SC3 short course covering core generative modeling methods and their use in protein structures, drug design, single-cell trajectories, and biomedical data with practical demos.

Instructors: Anru Zhang, Alex Tong, Fred Peng

View Full Syllabus View Course Materials

Interactive PDB structure · 1HV4 · HIV-1 Capsid

Protein ribbon structure diagram

People

Instructors

Portrait of Anru Zhang

Anru Zhang

Duke University

Website

Eugene Anson Stead, Jr. M.D. Associate Professor at Duke University with appointments spanning biostatistics, computer science, statistical science, and electrical and computer engineering.

Read more

Anru Zhang is Eugene Anson Stead, Jr. M.D. Associate Professor, Associate Professor of Biostatistics & Bioinformatics, Associate Professor of Computer Science, Associate Professor of Statistical Science, and Associate Professor in the Department of Electrical and Computer Engineering.

Portrait of Alex Tong

Alex Tong

Aithyra, Vienna

Website

Principal Investigator at Aithyra, formerly (briefly) assistant professor at Duke University.

Read more

Previously, Alex was briefly an assistant professor at Duke University. He completed a postdoc with Yoshua Bengio at Mila and earned his PhD from Yale University in 2021. His research spans generative modeling, deep learning, optimal transport, and causal discovery for cell development, molecular biology, and protein design. He co-founded Dreamfold.

Portrait of Fred Peng

Fred Peng

Duke University

Website

PhD student at Duke University focused on generative modeling and protein design.

Read more

Fred works on practical and theoretical advances in generative models for protein structures and biomedical data applications.

Course structure

Syllabus

Expanded outline for quick access during and after the course.

Section A: Foundations of Generative Models

  • Classic generative models (Anru)
  • Variational Autoencoder (VAE) (Anru)
  • Generative Adversarial Network (GAN) (Anru)
  • Normalizing flow (Anru)
  • Diffusion model (Alex)
  • Flow matching (Alex)
  • Autoregressive models (Fred)
  • Discrete diffusion model (Fred)
  • Code demo: vibe coding (Fred)

Section B: Applications of Generative Models

  • Protein sequence (Fred)
  • Protein structure & drug design (Alex)
  • Single cell trajectory (Alex)
  • Synthetic Data for Imbalanced Learning (Anru)
  • Synthetic Biomedical data (Anru)

Resources

Course Materials

Course materials currently include the shared slides for Anru and Alex, plus Fred's slide deck.

Anru Zhang and Alex Tong

Shared slide deck for the Anru and Alex portions of the course.

Open Shared Slides