The Revenir™ Product Engine
We have created a purpose-built computational drug discovery engine called Revenir™, which captures the biophysical changes caused by mutations in proteins. By examining surface features and numerous biophysical descriptors of both the mutated and wild-type proteins, we build an understanding of that defect and how to correct it.
Reveal
By modeling protein dynamics in new ways, we reveal hidden features on proteins
Dynamic Pocket Hunting
Reproducibly capture allosteric and cryptic pockets with the potential to revert the disease state
Achieve Congruence
Apply real-time ligand-based correction to predict small molecule hits with the potential to correct the disease state
Test
Compound predicted by Revenir™ seamlessly progress from the dry lab to the wet lab for Hit-Lead-Generation
Advantages of Revenir™
The Revenir™ Engine significantly improves on the historical drug discovery process.
Avoids need for high throughput screening
Rapid Identification of Functional Pockets
Robust Identification of Novel Chemical Matter
Unprecedented Hit Rates
Reduction in Time and Cost
Pipeline
Congruence’s pipeline represents both first-in-class and best-in-class potential to address significant
unmet medical needs in a number of high value indications.
Publications
- Congruence Therapeutics: finding a fix for misfolded proteinsMichael Eisenstein
- Quantitative Structure-Activity Relationship (QSAR) modeling to predict the transfer of environmental chemicals across the placentaLaura Lévêque, Nadia Tahiri, Michael-Rock Goldsmith, Marc-André Verner
- AMBER free energy tools: a new framework for the design of optimized alchemical transformation pathwaysHsu-Chun Tsai, Tai-Sung Lee, Abir Ganguly, Timothy J. Giese, Maximilian CCJC Ebert, Paul Labute, Kenneth M. Merz, Jr., and Darrin M. York
- From Protein Sequence to Structure: The Next Frontier in Cross Species Extrapolation for Chemical Safety EvaluationsCarlie A. LaLone, Donovan J. Blatz, Marissa A. Jensen, Sara M.F. Vliet, Sally Mayasich, Kali Z. Mattingly, Thomas R. Transue, Wilson Melendez, Audrey Wilkinson, Cody W. Simmons, Carla Ng, Chengxin Zhang, Yang Zhang