Computational Science
AI, networks, structures, and data
Our methods are designed for biological systems where information is distributed across molecules, interactions, pathways, and cellular contexts.
AI-driven biology for the future of medicine
The Brylinski Lab develops computational methods at the interface of artificial intelligence, graph learning, structural bioinformatics, and network medicine to understand complex biological systems and guide therapeutic discovery.
The Brylinski Lab
Computational biology for discovery, prediction, and molecular insight
We develop and apply computational approaches to understand complex biological systems, connect molecular mechanisms to disease, and guide biomedical discovery. Our work brings together artificial intelligence, molecular modeling, biological networks, and large-scale data analysis.
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Research
Biomedical questions, computational methods, and integrated research programs.
People
Current lab members, trainees, collaborators, and former group members.
Publications
Research articles, reviews, methods, datasets, and featured scientific outputs.
Teaching
Courses in physical biochemistry, molecular biophysics, modeling, and drug discovery.
News
Media stories, lab features, journal covers, and public-facing research highlights.
Biomedical Discovery
From molecular mechanisms to therapeutic hypotheses
We connect computational predictions to biological interpretation, helping prioritize targets, drugs, mechanisms, and experiments.