We build GenAI-based molecular simulations to substitute wet lab experiments in the drug development pipeline. Our Biotech/Pharma clients can verify the efficacy and safety of new drug candidates using our computer simulations, which match the accuracy of wet lab experiments but are faster.

Our AI models do not require training data, the limiting factor in ML for bio. They learn directly from the equations of physics. Furthermore, our simulations obey the laws of physics, avoiding the hallucinations seen in other GenAI technologies (e.g., generating an image of a person with six fingers.)

Since joining YC, Angstrom AI has developed the first physically accurate AI-based simulation of multiple molecules interacting. We have published the first molecule water solubility results with accuracy within the error range of wet lab experiments.
Fast Gen AI molecular simulations that reproduce wetlab results
Title: Ångström AI: Gen AI molecular simulations that reproduce wetlab results | Y Combinator

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### Gen AI molecular simulations that reproduce wetlab results

Angstrom AI builds GenAI-based molecular simulations to substitute wet lab experiments in the pre-clinical drug development pipeline. We are a team of 2 PhD's and 2 Professors from the University of Cambridge who decided to start a company together after we realised how to combine breakthoughs in our research in quantum-accurate models of physics and generative AI models. Our Biotech/Pharma clients can verify the efficacy and safety of new drug candidates using our computer simulations, which match the accuracy of wet lab experiments, but are over 100x faster. We achieve this accuracy by constraining our genAI-based simulations to obey the laws of physics, avoiding the hallucinations seen in other GenAI technologies. Since joining YC, Angstrom AI has developed the first physically accurate gen-AI based simulation of multiple molecules interacting. We have published the first molecule water solubility results with accuracy within the error range of wet lab experiments. We have also kicked-off a 150K pilot project with a pharma company to apply our tech to estimating solubility in their drug development pipeline.



Ångström AI

Founded:2024

Team Size:5

Location:San Francisco

### Active Founders

### Javier Antoran, Founder

Co-founder of Ångström AI, accelerating drug discovery+ development by substituting wet lab experiments with GenAI molecular simulations. My background is in probabilistic modeling and machine learning research (PhD University of Cambridge) with experience as a researcher at Google, Microsoft, and in Quant Finance. I am interested in meeting fellow founders or other people from the industry - reach out if you want to grab a virtual coffee!



### Jose Miguel Hernandez Lobato, Founder

Miguel is co-founder of Ångström AI and Professor of Machine Learning at the Department of Engineering, University of Cambridge, UK. He has nearly 20 years of research experience in Machine Learning and his work has been cited over 15,000 times. His research in Machine Learning for Molecules has led him to build strong relationships with partners in BioTech and Pharma. Before joining Cambridge as a faculty, he was a postdoctoral fellow at Harvard University.



### Laurence Midgley, Founder

I'm the co-founder of Ångström AI which substitutes wet lab experiments with physically accurate GenAI molecular simulations for clients in Pharma and BioTech. Before founding Ångström AI, I was a research engineer at InstaDeep (acquired by BioNTech 2023), and pursued a PhD at the University of Cambridge in generative AI models for molecular systems. I love coding and surfing, reach out if you are in the Bay Area and want to catch some waves.



### Company Launches

[### Ångström AI: Gen AI molecular simulations](

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**TLDR:** **We build fast and experimentally accurate generative AI-based simulations of molecular interactions for pharma and biotech companies.** These simulations can tell us whether a drug is going to bind to a protein or how quickly a drug will act once it is consumed by a patient.

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Our Team
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We are a team from the University of Cambridge with 30+ years of combined experience in AI and Molecular Modelling, including:

*   [Javier]( who scaled probabilistic AI methods 1000x during his PhD, turned down a research scientist position in Big Tech to start Angstrom AI.
*   [Laurence]( who developed FAB, a method for training AIs from physical equations without training data, during his PhD and was a researcher at InstaDeep before its acquisition by BioNTech in 2023.
*   [Miguel]( Javier and Laurence's PhD supervisor, author of foundational research on AI for drug discovery and generative modeling.
*   [Gabor]( who developed MACE, a state of the art quantum mechanically accurate force field.

Miguel and Gabor's work has been cited over 40k times.

The Problem
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During drug discovery and development, pharmaceutical companies need to understand how drug molecules interact with other molecules in the human body to determine the drugs’ efficacy and safety.

Conventional methods to assess molecular interactions are unsatisfactory:

*   Wet lab experiments are accurate but slow and expensive.
*   Machine learning prediction methods, like AlphaFold, are fast but inaccurate. They are limited by the quality and quantity of training data, which must be generated by lab experiments.
*   Molecular Dynamics simulates interactions by rolling out the equations of physics, offering a balance between wet lab and machine learning predictions in accuracy and speed. These factors depend on the model of physics used. More accurate models are more expensive to run. This makes the method compute bottlenecked rather than data bottlenecked.

Here is a video of a simulation of 64 water molecules. [

Solution: Fast and Accurate Molecular Simulations
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We run molecular simulations, keeping us in the compute constrained regime, but we combine **1) quantum mechanically accurate models of physics 2) generative AI that allows us to run these models quickly.**

1.  We use the MACE (multi-atomic cluster expansion) physics model, which accurately reproduces quantum-mechanical interactions. It was developed by Gabor, our co-founder. In collaboration with our academic partners, we recently showed MACE simulations are the **first ever to provide accuracy comparable to lab experiments** when estimating the hydration-free energies (a quantity relevant to drug bioavailability). Below is a plot from the [resulting publication]( However, MACE is computationally expensive, each of the results from the below plot required 1 week of compute on 8 A100 GPUs.
    
    
    
2.  We use diffusion models to accelerate MACE simulations, making them computationally affordable. Our models generate states **consistent with physics, but the transitions between states are non-physical and significantly faster.** Here is a video of our AI simulating the same water box as above.
    

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Demos!
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Here are a couple of examples to show off the type of stuff you can do with our generative models.

### Speeding up Supercool Water

Supercool water—liquid water below freezing, here at -40°C—is notoriously difficult to simulate with traditional methods because cold molecules move slower. The plot below shows the correlation between water molecule orientations across simulation steps. **Our AI introduces about 10,000 times more information per step compared to traditional simulations.**



### Hydrating Methane

Modeling interactions between molecules and water allows us to calculate how quickly the molecules will dissolve and their bioavailability as drugs. Here are videos of a traditional simulation of a methane molecule interacting with water and our AI simulation. **Our AI prioritizes the movement of the methane molecule and its surrounding water, which are the parts that matter for solubility and bioavailability calculations.**

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These are the first ever **genAI accelerated, physically accurate molecular dynamics simulations** incorporating the interaction of many molecules. We are scaling up - so stay tuned!

Our Ask
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Reach out to [info@angstrom-ai.com](mailto:info@angstrom-ai.com) if

*   You are in pharma or biotech and interested in learning about the theory of diffusion models and quantum-mechanically accurate models of atomic interactions. We would be happy to give a talk on our research or have a more informal chat over Zoom.
*   You have friends who work in pharma or biotech and are interested in computational methods. We would love to meet them!

#### YC Sign Photo


