Jonah Vilseck
Indiana University-Purdue University-Indianapolis, IN·Chemistry
Finding a new drug takes years, large teams and millions of dollars. Computer simulations can cut these costs by predicting how strongly a drug candidate binds its target protein before anyone makes it.
- Fields
Chemistry
Biophysics
Pharmacology
- Topics
computational drug design
drug discovery
structure-based drug design
- Grants
- 1 known grant, through Jun 2027
What they work on
- Use lambda dynamics free energy simulations to understand how missense mutations in a drug target cause drug resistance, and to identify drug modifications that overcome it.
- Characterize protein-protein interactions and binding specificity by simulating chemical changes on both binding partners at once, searching large combinatorial spaces with LD-based methods.
- Automate the generation of new, target-specific lead compound analogs by integrating lambda dynamics calculations with machine-learning or deep-learning algorithms.
- Apply these LD-based techniques to model protein targets of therapeutic importance in multiple myeloma and Alzheimer's disease to guide small-molecule and protein drug design.
More about the research
This project develops and applies lambda dynamics (LD), a simulation method for computing binding free energies. Free energy measures how favorable binding is. Older methods test one chemical change at a time. LD can sample many changes at once within a single molecular dynamics simulation, which models the movement of every atom over time. Earlier LD work sampled tens to hundreds of chemical variants of a single small molecule or protein, using about a tenth of the computing power of standard methods.
Why it matters
Faster, cheaper computer-guided drug design could shorten early drug discovery for multiple myeloma, Alzheimer's disease, and other cancers and infectious diseases. Atom-level insight into how mutations cause drug resistance could help researchers design drugs that keep working. Pairing simulations with artificial intelligence could automate parts of lead optimization.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Tai-Sung Lee
Rutgers University-New Brunswick, NJ·Chemistry
Build a Python-based executing environment that connects AMBER's molecular dynamics engine, a free energy module and user workflow control, so new simulation methods can be added and prototyped quickly.
Jay Ponder
Washington University in St Louis, MO·Chemistry
Develop the AMOEBA+ polarizable multipole force field for DNA and RNA, with parameters generated automatically by the Poltype2 software, to model nucleic acid structure, dynamics and interactions.
Ao Ma
University of Illinois Chicago, IL·Biophysics
Compute true reaction coordinates for ligand binding using energy flow theory and the generalized work functional method, then simulate natural, unbiased binding in HIV-1 protease and PDZ domains.
The school
Indiana University-Purdue University-Indianapolis: 25 labs on PI Pages, $89M a year on research, 120 research doctorates a year.
More about Indiana University-Purdue University-Indianapolis
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