Ångström AI
Gen AI molecular simulations that reproduce wetlab results
About Ångström AI
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.
Company details
Funding
Total raised
$500K
Last stage
Seed
Investors
Founders
Laurence Midgley
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.
LinkedInJavier Antoran
PhD from University of Cambridge. Background in probabilistic modeling and machine learning research. Former researcher at Google and Microsoft. Scaled probabilistic AI methods 1000x during his doctoral program.
Jose Miguel Hernandez Lobato
Professor of Machine Learning at the Department of Engineering, University of Cambridge. Nearly 20 years of research experience in ML with over 15,000 citations. Research focus on machine learning for molecules.
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