Deep Learning for Molecular Free Energy Calculations – 2025
Apply deep learning and neural networks to calculate molecular free energies for drug discovery applications
Research Overview
Free energies specify the extent of a chemical reaction at equilibrium and whether a process is spontaneous. This project applies deep learning to obtain normalizing flows to generate samples of molecular systems and calculate free energies, focusing on the binding between small molecules and proteins. This research may require specialized neural network architecture to deal with high-dimensional systems with complex probability distributions.
Ideal Candidates
- Computer Science/Data Science/AI students interested in physical and life science applications
- Chemistry/Biochemistry students interested in computational approaches
- Background in machine learning, neural networks, or molecular modeling helpful but not required
Opportunity Details
- Faculty Mentor: Professor David Minh
- Start Date: Flexible
- Positions Available: 2 spots
- Eligibility: Current Illinois Tech undergraduate or graduate students
- Compensation: Unpaid research opportunity
- Credit: Not-for-credit (can be arranged separately if needed)
What You’ll Learn
- Deep learning techniques for molecular systems
- Normalizing flows and neural network architectures
- Computational chemistry and drug discovery methods
- High-dimensional probability distribution analysis
Research Mentor: Professor David Minh
Email: dminh@iit.edu
Lab Website: http://mypages.iit.edu/~dminh/
