top of page
Image by National Cancer Institute

DRIVING TARGETED PROTEIN DEGRADATION BY LEVERAGING DIFFERENTIABLE DESIGN AND MOLECULAR DYNAMICS TO PREDICT, OPTIMIZE, AND DELIVER BETTER THERAPEUTICS

Our mission is to bring about a revolution in the entire process of inventing protein degradation therapeutics, replacing the sequential, artisanal approach that currently dominates the industry, with an efficient, integrated, predict-first, physics-based learning system that is suited to the complexity of protein degradation discovery. We are driven to codify and systematize drug discovery, to move away from this sequential approach, and scale the creation of precision-engineered protein degraders.

OUR AUTO/DX PLATFORM

We utilize our proprietary AI-driven Auto/dx platform to discover protein degraders for genetically defined disease drivers

​

Protein degraders represent a novel class of small molecules designed to act on a diverse array of diseases. We have developed a proprietary framework that combines our understanding of medicinal chemistry, molecular simulation, and statistical inference to design degraders that stabilize a ternary complex and elicit a favorable therapeutic response. 

 

Currently, TPD discovery is limited by throughput and complexity. To address this, our Auto/dx platform precision engineers end-to-end design strategies leveraging molecular dynamics to drive efficient and effective TPD discovery including:

​

Biological Discovery: Proprietary screening approach processed and curated to identify TPD targets and match with a library of E3 ligases based on tissue-specific expression, disease signatures, and intracellular localization.

​

Multiobjective Design: Active learning focused on multiparameter optimization of protein degraders using generative modeling and molecular dynamics.

​

Translational Studies: AI-driven quantitative systems pharmacology models for safety, efficacy, biomarker stratification, and patient selection to drive success in clinical development.

ternary.png
bottom of page