Course Information

Description: This course will focus on computational techniques used to study the structure and dynamics of biomolecules, cells, and everything in between. For example, what is the structure of proteins, DNA, and RNA? How do changes in their shape contribute to their function? How do they bind to one another and to other molecules? How are molecules distributed and compartmentalized within a cell, and how do they move around? How might one modify the behavior of these systems using drugs or other therapeutics? How can structural information and associated computational methods contribute to the design of drugs, vaccines, proteins, or other important molecules?

Computation can contribute to addressing such questions in at least two distinct ways. First, computational analysis is required to extract useful information from experimental measurements. Second, one can use computational techniques to predict folded structures, dynamics, and important biochemical properties.

This field has advanced dramatically in recent years thanks to breakthroughs on multiple fronts, including AI, computing power, and experimental methods.

The course will cover (1) atomic-level molecular modeling methods for proteins and other biomolecules, including structure prediction, molecular dynamics simulation, docking, protein design, and drug discovery, (2) computational methods involved in solving molecular structures by x-ray crystallography and cryo-electron microscopy, and (3) computational methods for studying spatial organization of cells, including computational analysis of microscopy data, and simulations at the cellular scale. The course will cover both foundational material and cutting-edge research in each of these areas, including dramatic recent advances in AI (machine learning) for structural biology.

Coursework: Students will be expected to complete three assignments, each of which will involve a combination of theoretical questions and computer work. Additionally, students will be expected to complete a project. The project will involve about as much work as an assignment, but it will be more open-ended and will allow students to delve into a topic of their choosing in more depth. Finally, students will be expected to complete an exam at the end of the quarter. More details regarding content of the exam will be released toward the second half of the quarter.

Prerequisites: Elementary programming background (at the level of CS 106A) and introductory course in biology.

Class: Tuesdays and Thursdays, 3:00 PM - 4:20 PM in Packard 101.

Materials: There is no required textbook. We will suggest a variety of optional reading material throughout the course.

Live Streams and Recordings: All lectures will be recorded this year and will be available to enrolled students on Canvas (linked here). After navigating to the CS279 Course Page on Canvas, click on the Panopto Course Videos tab on the left side of the screen. The live lecture will be available to view on Canvas in real-time. Lectures will also publish under this tab thirty minutes after class ends. We expect real-time attendance (either in-person or virtually) from students who are able to do so; please note our participation policy. All TA-led tutorials will be recorded (attendance is not required) and posted to Canvas (in the Kickstarts and Tutorials folder in the Panopto Course Videos tab).

Instructor: Ron Dror

• Professor Dror's Office Hours: Tues. & Thurs. 4:20 - 4:45 PM, outside Packard 101 (i.e., right after each class, outside the classroom). If you are participating in lecture remotely, you can join these office hours through the zoom room located at https://stanford.zoom.us/j/93647915180 (password: 338179). A TA will be monitoring this Zoom and will be able to add you to a queue so Professor Dror can answer your questions.

Contact and Questions:
Please use Ed Discussion for questions related to assignments, lectures, and course logistics. If you have issues that cannot be resolved on Ed, please contact us at cs279-aut2627-staff@lists.stanford.edu. For instructions on how to get set up on Ed, please see the Getting Set Up handout.

TA: Aditri Patil

TA: Artemis Xu

TA: Ayush Pandit

TA: Chiho Im

TA: Jay Shenoy

TA: Keira Maria Cornwell

Office Hours:

• Some office hours will be held in-person and others will be held virtually over Zoom. The same Zoom link will be used for all virtual office hours (password: 338179).
• Queuestatus will be used to manage the queue for both virtual and in-person office hours. Please see the Getting Set Up handout for further instructions on QueueStatus.
• The weekly office hour schedule can be viewed through the Google Calendar below. We will hold office hours beginning Week 2 (i.e., as of September 28).

Announcements:
All announcements will be made on Ed Discussion. For instructions on how to get set up on Ed, please see the Getting Set Up handout.

Handouts

• Getting Set Up
• Course Policies and Evaluation Criteria
• Working on LTS Machines

Lectures

• Introduction (Sept. 22) [slides]


• Biomolecular structure (Sept. 24 and 29) [slides]

Optional Reading:
• Michael Levitt's Molecular Architecture Lecture from former course SB228 [slides]
• Thoughts on how to think (and talk) about RNA structure [Public] [Stanford Only]


• [optional] Python and Terminal tutorial (Thursday, October 1, 11 AM - 12 PM, over Zoom) [slides] [recording]


• [optional] Assignment 1 kickstart (Friday, October 2, 11 AM - 12 PM, over Zoom)


• Energy functions & their relationship to molecular conformation (Sept. 29 and Oct. 1) [slides]

Optional Reading:
• Molecular Simulations with a Pretrained Neural Network and Universal Pairwise Force Fields [Public] [Stanford Only]
• Biomolecular Dynamics with Machine-Learned Quantum-Mechanical Force Fields Trained on Diverse Chemical Fragments [Public] [Stanford Only]
• ANI-1: An Extensible Neural Network Potential with DFT Accuracy at Force Field Computational Cost [Public] [Stanford Only]


• Molecular dynamics simulation (Oct. 1 and 6) [slides]

Optional Reading:
• Molecular Dynamics Simulation for All [Public] [Stanford Only]
• Molecular mechanism of biased signaling in a prototypical G protein–coupled receptor [Public] [Stanford Only]
• Scalable emulation of protein equilibrium ensembles with generative deep learning [Public] [Stanford Only]
• Unlocking hidden biomolecular conformational landscapes in diffusion models at inference time [Public] [Stanford Only]


• Predicting structures of proteins and other biomolecules, including machine learning methods such as AlphaFold (Oct. 6, 8 and 13)


• [optional] Assignment 2 kickstart (time TBD, over Zoom)


• Protein design (Oct. 15)


• Fourier transforms and convolution (Oct. 20)


• Image analysis (Oct. 22 and 27)


• Microscopy (Oct. 27)


• [optional] Assignment 3 kickstart (time TBD, over Zoom)


• Diffusion and cellular-level simulation (Oct. 29 and Nov. 5)


• Nov. 3 - Democracy Day, no class


• Project topics (Nov. 10)


• Ligand docking and virtual screening (Nov. 12 and 19)


• Nov. 17 - no class (instructor away)


• X-ray crystallography (Nov. 19)


• Nov. 24 & Nov. 26 - Thanksgiving Recess, no class


• Cryo-electron microscopy (Dec. 1)


• Review (Dec. 3)

Assignments

Please note that the following dates are approximate. When an assignment is released, the PDF and starter code will be available to download here. An optional LaTeX template will also be provided specifically for students who wish to typeset their solutions in LaTeX, but you are not expected or required to do so.

• Assignment 1 – Biomolecular Structure and Visualization [handout] [starter code]
  Out: Monday, September 28, 2026
  Due: Thursday, October 15, 2026 at 1:00 PM
  
  • LaTeX template (optional)
  • Software guide
  • PyMOL commands


• Assignment 2 – Atomic-Level Molecular Modeling
  Out by: Thursday, October 15, 2026
  Due: Thursday, October 29, 2026 at 1:00 PM
  
  
• Assignment 3 – Cellular Structure and Dynamics
  Out by: Thursday, October 29, 2026
  Due: Thursday, November 12, 2026 at 1:00 PM
  
  
• Project
  Out by: Tuesday, November 10, 2026
  Due: Friday, December 4, 2026 at 11:59 PM
  
  

Submission: All assignments and the project writeup should be submitted to Gradescope. If you are not already added to Gradescope, see instructions for accessing Gradescope in the Getting Set Up handout.

Exam

Python Resources

• This class's "Python and Terminal Tutorial" tutorial will give a brief introduction to Python programming.
• Codecademy is a site that does a good job of introducing the basics of Python, organized by topic. If you're just getting started with Python or if you want to brush up on specific issues, this may be helpful.
• Check out CME 193: Introduction to Scientific Python! It's a 1-unit course that runs for eight weeks. It is recommended for students who want to use Python in math, science, or engineering courses and for students who want to learn the basics of Python programming.