Make undruggable targets a thing ofthe past


The current pharmaceutical research and development model is broken and unsustainable.



With the current pharmaceutical R&D model, it takes about 12 to 15 years to bring a new drug to market.



The estimated cost of research and development sits at over $2.5 billion per drug approval, increasing at a rate of 8.5% each year.​

success rate


More than 90% of drug candidates that enter Phase 1 clinical trials fail to reach regulatory approval, often due to the limited predictive value of preclinical models of disease.​​



Drug approvals remain stagnant in the current model. Fewer than 70 new drugs enter the market each year. With such a low yield, scalability is nearly impossible.​​​


We must take more shots on goal.

To decrease failure rates and increase R&D productivity, we must take more shots at a variety of disease-causing targets and therapeutic modalities.

Using machine learning, our multi-module drug discovery platforms generate more novel drug candidates, shorten the timeline, cut operational costs and increase the probability of success of pharmaceutical R&D.

One important distinction: we believe AI and computational technologies are impactful in drug discovery only when biology is our North Star. Our platforms combine domain expertise in biology and pharmacology with AI and machine learning to provide an end-to-end solution.

  • SUEDE™ captures compound desirability with validated pharmacophores. With SUEDE™, we screen billions of molecules a million times faster than traditional methods, enabling rapid hit discovery for diverse targets.

  • BAGEL™ designs novel scaffolds from molecular templates using neural networks. To prioritize novelty and bioavailability, BAGEL™ learns pharmacologic features from datasets vetted by experienced medicinal chemists.

  • We own a state-of-the-art drug property prediction platform built on graph neural networks (GNN). GNNs excel at QSAR tasks, consistently outperforming industry-standard techniques in virtual screening and lead optimization.

  • We provide end-to-end computational drug design and high throughput lab automation capabilities. Our wet lab platform generates billions of proprietary biological activity data points, which further enhance our AI models.

  • EMBARR™ designs protein therapeutics with new functions by utilizing in-house proprietary data combined with state-of-the-art deep neural networks. Utilizing a structural understanding, EMBARR™ enables us to quickly explore novel and high potential drug candidates.

  • AETHON™ enables comprehensive protein structure desirability assessments utilizing custom score functions learned from in-house data generation and extensive domain knowledge. By harnessing deep neural networks, AETHON™ improves upon the speed and accuracy of existing methods.

  • PEGASUS™ is our platform for multi-parameter protein therapeutics optimization, capable of improving drug ADMET properties while maintaining function. Operating on multiple modalities and utilizing billions of proprietary data points, PEGASUS™ iteratively builds upon the industry standard with every design cycle.