Chem 193: (ML \(\cup\) MD) \(\cap\) Biophysics#
Description: This course is an introduction to machine learning (ML), molecular dynamics (MD), and their applications to biophysics. The course is designed for students with a background in chemistry, physics, biology or computer science who are interested in learning how to use computational tools to solve biophysical problems. The course will cover the basics of ML and MD, and their applications to important biophysical problems such as protein structure prediction, understanding protein folding and computational drug design among others. The course will include lectures on theory, hands-on tutorials, homework assignments, research article presentations, and a final project. Python will be used as main programming language for tutorials and homework assignments. OpenMM and JAX will be used for MD simulations and ML, respectively.
Prerequisites: thermodynamics (Chem 31 or equivalent), organic chemistry (Chem 51 or equivalent), linear algebra (Math 70 or equivalent), and basis programming skills.
Instructor: Dr. Xinqiang Ding (Xinqiang.Ding@tufts.edu)
Time and Location: Tuesday and Thursday 10:30AM -11:45AM, Pearson 112
Office Hours: Tuesday 2:30PM - 3:30PM, Pearson 005X
Grading: homework (40%), research article presentation (20%), final project (40%)
Textbook: There is no required textbook for this course. However, the following books are recommended for students who want to learn more about the topics covered in this course:
The Elements of Statistical Learning by Trevor Hastie, Robert Tibshirani, and Jerome Friedman
Pattern Recognition and Machine Learning by Christopher Bishop
Understanding Molecular Simulation: From Algorithms to Applications by Daan Frenkel and Berend Smit