Duc Nguyen | Homepage

Principal Investigator

Duc D. Nguyen

Associate Professor

Email: ducnguyen[at]utk.edu

Office: Ayres Hall 203

Research Group

Trung Nguyen PhD Student (2024 - Present)

Masud Rana Postdoc (2021 - 2024) Now: Assistant Professor at KSU

Farjana Tasnim Mukta PhD Student (2021 - 2024) Now: Lecturer at KSU

Brendan LeStrange Undergrad (2024 - Present)

Benjamin Philpot Undergrad (2022 - 2023)

Avery Meyer Undergrad (2022 - 2023)

Jonathon Fleck Undergrad (2016 - 2019)

Cici Mikat PA, Undergrad (2018-2019)

Jason Kenny PA, Undergrad (2018-2019)

Kyle Cole PA, Undergrad (2018-2019)

Tian Xao Undergrad (2016)

Welcome to Nguyen Lab for AI & Mathematics

At Nguyen Lab, we develop cutting-edge mathematical tools and deep learning models to solve complex problems in drug discovery, material science, and biosciences. Our work bridges differental geometry, topology, graph theory, and artificial intelligence to uncover novel insights in molecular design and multi-scale biological systems.

Research Assistant (RA) Positions Available

We have several RA positions available for Math Ph.D. students. While all interested students are encouraged to contact, preference will be given to those who:

Passed the preliminary exams
Have a strong interest in Topology, Differential Geometry, Graph Theory, and Artificial Intelligence

Why Join Our Lab?

Work on cutting-edge research at the intersection of AI, mathematics, and biology.
Collaborate with experts across disciplines on real-world problems.
Opportunities for publications and conference travel funding.
Gain expertise in high-demand fields to enhance career prospects in academia and industry.
Potential for a Research Assistant (RA) position based on performance.

Research Interests

Topological Data Analysis (TDA) Biomolecular data representations, Drug repurpose, SARS-CoV-2 inhibitors

Differential Geometry Solvation modeling, Differential geometric learning of molecules, Interactive manifolds for drug design

Graph Theory Flexibility of protein, Algebraic graph learning for drug discovery, Manifold learning

Machine Learning Multiscale manifold regularization, Convolution neural network (CNN), Graph convolution neural network (GNN), Multitask learning, Transfer learning, Autoencoder, Generative network complex (GNC), Generative adversarial network (GAN), Reinforcement learning.

Data analysis Low-dimensional mathematical representations, Knowledge based driven data analysis.

Mathematical models for bioscience Differential geometry representations, Multiscale weighted colored algebraic graphs, Topological learning, Flexibility-rigidity index (FRI) for biomolecules, Geometry based solvation modeling.

Drug Discovery D3R Grand Challenges (pose prediction, drug screening, affinity prediction), Small Molecular Property Predictions, Protein-Protein Binding Free Energy and Protein Thermodynamic Stability Changes Upon Mutation, Drug Repurposing, Drug Generation.

Quantitative systems pharmacology Develop mechanistic system biology model, Investigate potential therapeutic drug targets.

Numerical methods for PDEs High order interface methods, Geophysical flow, Electrostatic binding analysis.

Funding Support

NSF-2053284

NSF-2151802

NSF-2245903

Bristol-Myers Squibb

Pfizer

D3R Grand Challenges 4

Our team was the top performer

D3R Grand Challenges 3

Predictions of our team were ranked 1st in 10 of 26 predictive tasks

D3R Grand Challenges 2

Our prediction was ranked 1st for Set 1 (Stage2)

Online Server: FRI

Online server for the flexibility analysis of biomolecules based on flexibility and rigidity index.

FRI

Online Server: RI-Score

Online server for geometric graph theory or rigidity index (RI) based scoring function for protein ligand binding affinity prediction.

RI-Score

Online Server: DG-GL

Online server for differential geometry based geometric data analysis of molecular datasets.

DG-GL

Online Server: AGL-Score

Online server for algebraic graph theory based protein-ligand binding scoring, ranking, docking and screening.

AGL-Score