Modern Methods for Structure-Based Drug Discovery

Course details

Teaching staff

Course description

This course introduces students to the modern computational approaches and governing physical and chemical principles that underpin structure-based drug discovery. The course explores how biomolecular structure and dynamics inform the rational design of therapeutics and how machine learning can be harnessed to improve drug discovery. Topics include molecular interactions, statistical mechanics and thermodynamics, generative modeling, molecular simulations, coarse-grained and enhanced sampling techniques, free energy calculations, protein structure prediction, protein design, molecular docking, de novo design, and virtual screening. Students will engage with methods for structure-based drug discovery in hands-on assignments and recitations.

Communication

Course material will be posted to the course website: https://lectures.compstruct.org/. Course communication will be through Slack (https://compstruct.slack.com/).

Lectures

Lectures will be 12:00pm-1:20pm on Mondays and Wednesdays in the 10th floor Fifth and Halket classroom. Students should bring a laptop or other device to lecture.

Recitations

Recitations will follow lecture, 1:30-2:30pm on Wednesdays in the 10th floor Fifth and Halket classroom. Students should bring a laptop or other device. Recitations are not optional as students will be graded on the work they perform during the recitation. The classroom is reserved for an additional hour to provide extra time and office hours time. Recitations will consist of practical, in-class projects or problem sets that students will work on in small groups. In-person attendance is required in order to receive credit for recitation work, unless previous approval is obtained from the instructor.

Class Recordings

Lectures will be recorded and available for asynchronous viewing on Panopto, but this is not intended as a substitute for attending class nor will instruction be tailored to ensure usefulness of the recording (e.g., the whiteboard may not be visible in the recording). Students should make every reasonable effort to attend class in real-time as in-class group work and discussion is an important part of lecture.

Assignments

There will be 7 assignments that will involve a mix of programming (Python) and analytical thinking. Assignments will be turned in using GradeScope.

Journal Club

Towards the end of the course there will be a Journal Club where each student critically and clearly presents a recent or seminal paper in computational structural biology. One of the objectives of the Journal Club is to identify possible project topics. Masters students and undergraduates may present in pairs, but PhD students must present individually.

Project

Students will propose and implement a small research project in computational structural biology. They may propose to answer a biological question using an established technique, perform a comparative assessment of different approaches to the same problem, or suggest and implement an improvement to an existing technique. Projects may be done in groups of 1-3, with the scope of the project scaling with the size of the group.

Exams

There will be two in-class exams.

Grades

The instructors reserve the right to modify grade distributions and cutoffs to most accurately reflect student performance, but we anticipate that the final grade will be:

Component	Percentage
Assignments (7)	45%
Recitation	10%
Exams	20%
Journal Club	10%
Project	15%

Standard grading scales will be applied:

Letter Grade	Percentage
A+	97–100%
A	93–96%
A−	90–92%
B+	87–89%
B	83–86%
B−	80–82%

Lateness

Assignments should be handed in on-time. When this is not possible, course instructors should be contacted with as much advance notice as possible. In general, requests for a single late day per an assignment will be automatically granted. Requests beyond that will require substantial justification and/or be subject to additional grade penalties. Late assignments will have a maximum possible score of 95%.

Academic Honesty

You must do all your own work. You are encouraged to discuss general concepts, strategies for debugging, and the particulars of a specific software package with other class members. However, specifics of individual assignments should not be discussed, and you should not show your code to fellow classmates. You are expected to understand and be able to explain any code you submit. Any attempt to "hack" the autograder will result in expulsion from the class and a referral to the dean's office.

Students in this course will be expected to comply with the University of Pittsburgh’s Policy on Academic Integrity. Any student suspected of violating this obligation for any reason during the semester will be required to participate in the procedural process, initiated at the instructor level, as outlined in the University Guidelines on Academic Integrity. This may include, but is not limited to, the confiscation of the examination of any individual suspected of violating University Policy. Furthermore, no student may bring any unauthorized materials to an exam, including dictionaries and programmable calculators.

To learn more about Academic Integrity, visit the Academic Integrity Guide for an overview of the topic. For hands-on practice, complete the Academic Integrity Modules.

Disability Services

If you have a disability for which you are or may be requesting an accommodation, you are encouraged to contact both your instructor and Disability Resources and Services (DRS), 140 William Pitt Union, (412) 648-7890, drsrecep@pitt.edu, (412) 228-5347 for P3 ASL users, as early as possible in the term. DRS will verify your disability and determine reasonable accommodations for this course.

Assignment 1: PyMOL Visualization

Recitation 1: Installing things and setting up the environment.