AI-powered Molecular Modeling

Instructor	Debswapna Bhattacharya (dbhattacharya@vt.edu)
Class meets	Mon, Wed 2:30 pm - 3:45 pm at GOODW 241
Office Hours	Debswapna Bhattacharya: Monday and Wednesday 4:00 pm - 5:00 pm at Torgersen 2160N
Class Mailing List	class-cs-6824-20577-202201-g@vt.edu
Piazza	https://piazza.com/vt/spring2022/cs6824/home
Canvas	https://canvas.vt.edu/courses/145337

Description

Welcome to CS 6824: AI-powered Molecular Modeling! This course will survey the emerging field of computational modeling of molecular structures driven by advances in artificial intelligence (AI), with an emphasis on predictive modeling. We will investigate relevant issues from interdisciplinary perspectives of macromolecular modeling and machine learning.

Prerequisites
This course is appropriate for graduate students in computer science, computational biology, bioinformatics, and statistics. Familiarity with fundamental concepts in machine learning, statistics, probability and algorithms is expected.

Grading

Class participation: 20%

Involvement in class
Peer review

Paper presentation: 30%
Project: 50%

Proposal
Report/paper
Presentation

Based on the grading breakdown above, each student's final letter grade for the course will be determined based on the ceiling of the final percentage of points earned. The grade ranges are as follows:

A: 93%-100%
A-: 90%-92%
B+: 87%-89%
B: 83%-86%
B-: 80%-82%
C+: 77%-79%
C: 73%-76%
C-: 70%-72%
D+: 67%-69%
D: 63%-66%
D-: 60%-62%
F: Below 60%

Schedule

Note: This schedule is tentative and subject to change.

Week / Day	Date	Presenter	Topic	Reading
1 / W	Jan 19	Debswapna Bhattacharya	Introduction and Administrativia [Slides]	Yu Li et al. Deep learning in bioinformatics: Introduction, application, and perspective in the big data era Methods, 166, pp. 4-21 (2019) [link]
2 / M	Jan 24	Debswapna Bhattacharya	Molecules [Slides]	D Weininger. SMILES, a Chemical Language and Information System. 1. Introduction to Methodology and Encoding Rules J. Chem. Inf. Comput. Sci., 28(1), 1988 [link] L Hunter. Molecular Biology for Computer Scientists [link]
2 / W	Jan 26	Debswapna Bhattacharya	Deep Learning for Molecular Modeling [Slides]	C Angermueller et al. Deep learning for computational biology Molecular Systems Biology, 12:878 (2016) [link] S Wang et al. Accurate De Novo Prediction of Protein Contact Map by Ultra-Deep Learning Model PLoS Comput Biol 13(1): e1005324 (2017) [link] M AlQuraishi and P Sorger. Differentiable biology: using deep learning for biophysics-based and data-driven modeling of molecular mechanisms Nature Methods, 18, pp. 1169-1180 (2021) [link]
3 / M	Jan 31	Debswapna Bhattacharya	Geometric Deep Learning for Molecular Representation [Slides]	K Atz et al. Geometric Deep Learning on Molecular Representations arXiv (2021) [link] Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges [link]
3 / W	Feb 2	Debswapna Bhattacharya	Deep Generative Learning for Molecular Synthesis [Slides]	Y Xu et al. Deep learning for molecular generation Future Medicinal Chemistry, 11(6), 2019 [link]
4 / M	Feb 7	-	The AlphaFold Breakthrough [Video]	J Jumper et al. Highly accurate protein structure prediction with AlphaFold, Nature (596), pp. 583–589, 2021 [link]
4 / W	Feb 9	Bernard Moussad	Paper 1 [Slides]
5 / M	Feb 14	Ngoc Khoi Dang Samira Mali	Paper 2 [Slides] Paper 3 [Slides]
5 / W	Feb 16	Rahmatullah Roche Mohimenul Karim	Paper 4 [Slides] Paper 5 [Slides]
6 / M	Feb 21	Joung Min Choi Md Hossain Shuvo	Paper 6 [Slides] Paper 7 [Slides]
6 / W	Feb 23	Jun Chen Sareh Ahmadi	Paper 8 [Slides] Paper 9 [Slides]
7 / M	Feb 28	Gunnar Nelson Monjura Afrin Rumi	Paper 10 [Slides] Paper 11 [Slides]
7 / W	Mar 2	Debswapna Bhattacharya	Project Proposal Logistics [Slides]	Group Info
Spring Break
8 / M	Mar 14	Project Groups	Project Proposal Presentation Group 1 [Slides] Group 2 [Slides] Group 3 [Slides] Group 4 [Slides] Group 5 [Slides]
8 / W	Mar 16		No class
9 / M	Mar 21	Project Groups	Project Proposal Presentation Group 6 [Slides] Group 7 [Slides] Group 8 [Slides] Group 9 [Slides] Group 10 [Slides]
9 / W	Mar 23	Mohimenul Karim	Paper 12 [Slides]
10 / M	Mar 28	Md Hossain Shuvo Ngoc Khoi Dang	Paper 13 [Slides] Paper 14 [Slides]
10 / W	Mar 30	Rahmatullah Roche Monjura Afrin Rumi	Paper 15 [Slides] Paper 16 [Slides]
11 / M	Apr 4	Gunnar Nelson Joung Min Choi	Paper 17 [Slides] Paper 18 [Slides]
11 / W	Apr 6		No class
12 / M	Apr 11	Jun Chen Samira Mali	Paper 21 [Slides] Paper 22 [Slides]
12 / W	Apr 13	Sareh Ahmadi Bernard Moussad	Paper 19 [Slides] Paper 20 [Slides]
13 / M	Apr 18	Project Groups	Project Presentation Group 9 [Slides] Group 10 [Slides]
13 / W	Apr 20	Project Groups	Project Presentation Group 7 [Slides] Group 8 [Slides]
14 / M	Apr 25		No class
14 / W	Apr 27	Project Groups	Project Presentation Group 3 [Slides] Group 4 [Slides]
15 / M	May 2	Project Groups	Project Presentation Group 1 [Slides] Group 2 [Slides]
15 / W	May 4	Project Groups	Project Presentation Group 6 [Slides]	Project Final Report Due

Assignments

Paper Presentations
Every student will be presenting 2 papers from this list by delivering two in-class presentations (one before the spring break and one after the spring break from the available slots in the course website). Each paper presentation should be prepared for a duration of 30 minutes plus 5-7 additional minutes for Q&A.

Students should use Piazza public posts to inform others in the class about their paper selection (title of the paper) and date choice. As such, potential conflicts are resolved on a first-come-first-served basis using the timestamp of students' Piazza post. Before making your selection, therefore, checkout Piazza to see what papers/slots have already been taken.

Subsequently, students should upload a copy of the selected papers (title, abstract, PDF, etc.) to EasyChair as the corresponding author (also the only author). DO NOT use the names or contact information of the original authors during the submission. However, keep the title and abstract same as the original paper.

The paper presenters should upload the presentation slides to Canvas no later than 24 hours before their paper presentations. The remaining students should write a short peer-review on the upcoming papers to be presented and submit their report to EasyChair no later than 24 hours before the paper presentations.

Project Proposal
The project proposal should identify the problem, outline your preliminary approach, and propose the metrics for evaluation. It should also discuss a proposed plan containing a breakdown of various tasks and important project milestones and who on your team will be responsible for what tasks for team-based projects. Students should use Piazza public posts to inform others in the class about their team selection. The project milestones should be a prediction for planning purposes, but you are not obligated to adhere to them precisely. Your proposal should list at least three recent, relevant papers you will read and understand as background. The project proposal must be written using the following guidelines:

standard 8.5" x 11" page size
11 point or higher font, except text that is part of an image
Times New Roman font for all text, Cambria Math font for equations, Symbol font for non-alphabetic characters (it is recommended that equations and symbols be inserted as an image)
1" margins on all sides, no text inside 1" margins (no header, footer, name, or page number)
No less than single-spacing (approximately 6 lines per inch)
Do not use line spacing options such as "exactly 11 point", that are less than single spaced

The project proposal is required to be between 2 - 3 pages in PDF file format only to be submitted electronically via Canvas and concurrently uploaded to EasyChair. The page limit includes all references, citations, charts, figures, and images.

Project Report
The final project report should describe the project outcomes in a self-contained manner. Your final project report is required to be between 7 - 8 pages by using the NeurIPS LaTex template, to be submitted electronically via Canvas and concurrently uploaded to EasyChair. Please use this template so we can fairly judge all student projects without worrying about altered font sizes, margins, etc. The submitted PDF may link to supplementary materials including but not limited to code, open access software package via GitHub, project webpage, videos, and other supplementary material.

Deadlines
Note: All deadlines are until 11:59 PM EST unless otherwise specified.

Jan 31: Pick 2 papers to present from this list and electronically submit the papers to EasyChair as the corresponding author
Feb 7: Accept invitation to be a PC member
Submit presentation and peer-review reports no later than 24 hours before each paper presentation
Mar 4: Submit project proposal whitepaper to Canvas and concurrently upload to EasyChair
Mar 13: Submit project proposal presentation to Canvas
Mar 16: Submit proposal peer reviews via EasyChair
Apr 13: Submit project presentation to Canvas
May 4: Submit final project report to Canvas and concurrently upload to EasyChair

Class participation
Students are strongly encouraged to attend all the lectures (exceptions are allowed due to medical reasons or emergencies) and expected to engage in the discussion during the lectures and participate in Q&A. Student are also expected to be actively engaged in class-related discussion on Piazza so that other students may benefit from your questions and our answers. Your class participation grade will depend on your overall engagement in the classroom and in Piazza as well as your intellectual contribution.

Note: Students' first point of contact is Piazza (so that other students may benefit from your questions and our answers). If you have a personal matter, create a private piazza post.

Policies

Academic integrity
The Undergraduate Honor Code pledge that each member of the university community agrees to abide by states: "As a Hokie, I will conduct myself with honor and integrity at all times. I will not lie, cheat, or steal, nor will I accept the actions of those who do."
Students enrolled in this course are responsible for abiding by the Honor Code. A student who has doubts about how the Honor Code applies to any assignment is responsible for obtaining specific guidance from the course instructor before submitting the assignment for evaluation. Ignorance of the rules does not exclude any member of the University community from the requirements and expectations of the Honor Code. For additional information about the Honor Code, please visit: https://www.honorsystem.vt.edu/
This course will have a zero-tolerance philosophy regarding plagiarism or other forms of cheating. Your homework assignments must be your own work, and any external source of code, ideas, or language must be cited to give credit to the original source. I will not hesitate to report incidents of academic dishonesty to the Office of the Undergraduate Honor System.

Principles of Community
Because the course will include in-class discussions, we will adhere to Virginia Tech Principles of Community.

Accessibility
If any student needs special accommodations because of any disabilities, please contact the instructor during the first week of classes. Such students are encouraged to work with The Office of Services for Students with Disabilities to help coordinate accessibility arrangements.

COVID-19 Policy
Virginia Tech is committed to protecting the health and safety of all members of its community. By participating in this class, all students agree to abide by the Virginia Tech Wellness principles. Please follow the instructions posted at the University and public health guidelines for the latest COVID-19 Policy.