Atomwise - The Future of Human Health is Drugging the Undruggable

Of the roughly 20 thousand proteins encoded in the human genome, a little over 750 have FDA-approved drugs, and only about 2-3 thousand have a small molecule drug or biologic under development.

About 4 thousand genes have evidence linked to human disease, but may not yet have enough structural data for traditional drug design.

The remaining 80% of human gene targets are uncharted territory, representing the most challenging and most promising future for pharma and human health. AI has proven it can help unlock drug targets in both the most challenging and uncharted zones.

AI finds a novel reducer for Miro1, and a promising new drug candidate for Parkinson's Disease.

Using the AI technology that tackles complex drug discovery optimization, we have searched for small molecules from a vast chemical space in silico that are predicted to bind to Miro1. Treating fibroblasts from PD patients with the selected compound elimi- nates the Miro1 molecular pathology..."

Hsieh et al., 2019, Cell Metabolism 30, 1–10, https://doi.org/10.1016/j.cmet.2019.08.023

READ THE PUBLICATION

We solve the hardest problems in computer aided drug design, delivering hits even when there is little-to-no structural data for a target. AtomNet® screens make-on-demand molecules so that our AI results won't send you down a synthesis rabbit hole. 2-3 weeks after we complete a virtual screen you will receive predicted compounds to test in your lab. We have a history of success, delivering at least one hit validated in laboratory experiments 74% of the time, keeping our partners ahead of their competition.

The Challenge of Drugging the Undruggable

AI gives you the competitive advantage of scale you need to succeed.

Without AI we are just scratching the surface of what could be drugged.

A hypothesis on HTRA-1 leads to a promising new drug candidate, and the formation of a new company to develop therapies for Age-Related Macular Degeneration (AMD).

Pursuing a novel target without disease evidence yields a validated new target for pediatric cancer.

AI finds a novel reducer for Miro1, and a promising new drug candidate for Parkinson's Disease.

Competitive Advantage of Scale & Speed

Proven Success With the Most Challenging Targets

Overall
Success

No Training
Data

Homology
Model

X-ray
Structure

Protein-Protein Interactions

CryoEM
Data

Make the Atomwise AI Advantage Work for You

Broadly Applicable

Data Advantage

Strong Partner

What Impossible Biological Target Do You Want to Drug?

The Challenge of Drugging the Undruggable

AI gives you the competitive advantage of scale you need to succeed.

Without AI we are just scratching the surface of what could be drugged.

A hypothesis on HTRA-1 leads to a promising new drug candidate, and the formation of a new company to develop therapies for Age-Related Macular Degeneration (AMD).

Pursuing a novel target without disease evidence yields a validated new target for pediatric cancer.

AI finds a novel reducer for Miro1, and a promising new drug candidate for Parkinson's Disease.

Competitive Advantage of Scale & Speed

Proven Success With the Most Challenging Targets

Overall Success

No TrainingData

Homology Model

X-ray Structure

Protein-Protein Interactions

CryoEM Data

Make the Atomwise AI Advantage Work for You

Broadly Applicable

Data Advantage

Strong Partner

What Impossible Biological Target Do You Want to Drug?

Overall
Success

No Training
Data

Homology
Model

X-ray
Structure

CryoEM
Data