Can artificial intelligence help identify best treatments for cancers? LSU researchers say yes.
This article highlights LSU research using artificial intelligence and computational models to help identify more effective cancer treatment strategies.
News & Features
Media coverage and journal covers
Selected stories about the Brylinski Lab, our research, collaborations, and papers featured through journal cover artwork.
Lab in the News
Media coverage
Articles and public-facing stories featuring the lab, our research, collaborators, and broader biomedical impact.
Baton Rouge team among top 10 competing in $5 million IBM Watson AI XPRIZE contest
This article features a Baton Rouge team recognized among the top competitors in the IBM Watson AI XPRIZE contest, highlighting the use of artificial intelligence for high-impact scientific and biomedical applications.
Featured Research
Journal covers
Papers selected or highlighted through journal cover artwork and related visual features.
GeauxDock: A novel approach for mixed-resolution ligand docking using a descriptor-based force field.
GeauxDock introduced a new way to guide molecular docking by combining evolutionary clues with physics-based scoring, helping predict realistic protein–ligand binding poses for drug discovery.
eMatchSite: Sequence Order-Independent Structure Alignments of Ligand Binding Pockets in Protein Models.
eMatchSite made it possible to compare ligand-binding pockets even in imperfect protein models, expanding binding-site analysis from individual crystal structures toward proteome-scale studies of polypharmacology and drug repositioning.
Why not consider a spherical protein? Implications of backbone hydrogen bonding for protein structure and function.
By comparing real proteins with artificial compact structures, this work shows that backbone hydrogen bonding and secondary-structure packing help create the geometric cavities and interfaces that make molecular recognition possible.
Comprehensive structural and functional characterization of the human kinome by protein structure modeling and ligand virtual screening.
This study extends structure-based virtual screening across the human kinome by using predicted kinase models, showing how computational modeling can help prioritize candidate inhibitors even when experimental structures are unavailable.
What is the relationship between the global structures of apo and holo proteins?
This research highlights how ligand binding can reshape proteins, especially through large domain motions in multi-domain systems.
Computational Systems Biology Group focuses on large-scale modeling.
Pages 7-8 highlight the newly formed Computational Systems Biology Group at LSU/CCT and presented large-scale protein, network, and systems-level modeling as an emerging research direction requiring high-performance computing.
Speeding up discovery of new pharmaceuticals at CCT
Pages 20-21 emphasize the lab’s work on highly optimized molecular docking codes, showing how high-performance computing can accelerate virtual screening and make large-scale drug-discovery searches more practical.