Artificial Intelligence | Machine Learning | Natural Language Processing Anagenex | Evolving New Small Molecule Medicines


Enterprise, Drug Discovery, Evolution Platform, Biotech, Technology San Francisco, California, United States

Anagenex

Artificial Intelligence | Machine Learning | Natural Language Processing


Anagenex | Evolving New Small Molecule Medicines

Anagenex

Drug Discovery, Evolution Platform, Biotech, Technology


San Francisco, California, United States

Our ideas about diseases outpace our ability to drug them. 80% of protein targets are currently untouched because they don’t fit conventional ideas about what proteins are amenable to small molecule medicines. For these proteins, it’s hard to find a chemical starting point, and for any protein, it takes years of effort, a few dozen compounds at a time to turn those starting points into medicines.

We combine machine learning with massively parallel biochemical tools such as DNA Encoded Libraries (DELs) and Affinity Selected Mass Spectrometry (ASMS) to analyze more compounds more efficiently than ever before. By working with large datasets throughout our search process and letting our machine learning model guide our experiments, we are able to find molecules for the hardest problems in drug discovery, bringing first-in-class and best-in-class treatments to patients.

Recent advances in functional and genetic screening powered by innovations such as CRISPR have identified a tantalizing number of proteins that could meaningfully alter disease progression. Still, new medicines, particularly the small-molecule medicines that account for the majority of FDA approvals, take a decade or more to develop. At Anagenex, we integrate machine learning, biology, and chemistry to address historically intractable medical problems and bring medicines to patients faster.

First, we test billions of custom-synthesized compounds to see which ones are likely to modulate a protein “target” we believe to be important in some diseases. For any target, we run dozens of experiments at a billion compound scale, generating rich, high-quality datasets. Those datasets train proprietary neural networks to understand what compounds are promising. Finally, we use those machine learning models to design new multimillion compound experiments, synthesizing and then testing those compounds to repeat the cycle. Every iteration improves and bring

 

B2B

1 to 25

N/A

N/A

Scaling Up

2019

 
 

Biotechnology

Develop New Small Molecules

Increase Efficiency
Increase Productivity

 
 

Analytics
Service

Yes

Active

 
 

   Machine Learning
   Natural Language Processing
   Knowledge Representation and Reasoning


Drug Discovery

Drug Discovery


Image

Image

Video

Video

Text

Text

Structured

Structured

 

   Software


S3

S3

GitHub

GitHub

Python

Python

Kubernetes

Kubernetes

AWS

AWS


Machine Learning Algorithm

Machine Learning Algorithm

Deep Learning Algorithm

Deep Learning Algorithm

 
 

6

1

$30M

Company was founded 2019 and it took almost 3 years (Jun 2022) to raise first external round

 
 

Date

Round

$ Raised

Investors

06/08/2022

Series A

$30M

Catalio Capital Management, Lux Capital, Khosla Ventures, Obvious Ventures, Air Street Capital, Menlo Ventures

Date : 06/08/2022

Round: Series A

$ Raised: $30M

Investors: Catalio Capital Management, Lux Capital, Khosla Ventures, Obvious Ventures, Air Street Capital, Menlo Ventures

 

Investors

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Nicolas Tilmans

Nicolas Tilmans
Founder/CEO

Barb Ernisse

Barb Ernisse
Director Of Operations

 
 

Potential Customers

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