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Alissa Hummer

@alissahummer.com
118 followers 171 following 23 posts

Schmidt Science Fellow | Postdoc @ Stanford | Prev. DPhil @ Oxford || AI for Molecule & Cell Modeling

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Alissa Hummer @alissahummer.com · 15/06/2026
I am honoured to have been awarded the Corcoran Memorial Prize from @oxfordstatistics.bsky.social for my DPhil research on ML for antibody design. The Corcoran Memorial Prize & Lectures are named in memory of Stephen Corcoran, who was a graduate student in the Department of Statistics.
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Alissa Hummer @alissahummer.com · 04/10/2025
Excited to be pivoting from molecules to cells for my Schmidt Science Fellowship, advised by Emma Lundberg and Wah Chiu! I’m looking forward to building ML models that better reflect how molecules & cells look in real life 🔬
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Alissa Hummer @alissahummer.com · 26/08/2025
Our paper on generalizable antibody-antigen binding affinity prediction has been featured on the cover of the August Issue of @natcomputsci.nature.com! 📔🎉
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Reposted by Alissa Hummer
Nature Computational Science @natcomputsci.nature.com · 25/08/2025
🚨Our August issue is now live and includes research on antibody-antigen binding, molecular screening for zeolite synthesis, psychological experiments with LLMs, and much more! www.nature.com/natcomputsci...
Yellow and orange antibody binding to purple and blue membrane proteins
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Reposted by Alissa Hummer
Nature Computational Science @natcomputsci.nature.com · 08/07/2025
Out now! @alissahummer.com @opig.stats.ox.ac.uk and colleagues present Graphinity, a method to predict change in antibody-antigen binding affinity (∆∆G). Also featuring synthetic datasets of ~1 million FoldX-generated and >20,000 Rosetta Flex ddG-generated ∆∆G values! www.nature.com/articles/s43...
nature.com
Investigating the volume and diversity of data needed for generalizable antibody–antigen ΔΔG prediction - Nature Computational Science
Predicting the effects of mutations on antibody–antigen binding is a key challenge in therapeutic antibody development. Orders of magnitude more data will be needed to unlock accurate, robust predicti...
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Alissa Hummer @alissahummer.com · 09/07/2025
Our work exploring the ability of and requirements for ML to predict the effects of mutations on antibody-antigen binding affinity (ΔΔG) is out now in @natcomputsci.nature.com!
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Reposted by Alissa Hummer
Rachael Kretsch @rachael-kretsch.bsky.social · 20/05/2025
What is the status of nucleic acid structure prediction? Our analysis of CASP16 (doi.org/10.1101/2025...) reveals human expertise is still necessary for the most accurate prediction, but accuracy still heavily relies on templates; having seen a similar structure already.
doi.org
Assessment of nucleic acid structure prediction in CASP16
Consistently accurate 3D nucleic acid structure prediction would facilitate studies of the diverse RNA and DNA molecules underlying life. In CASP16, blind predictions for 42 targets canvassing a full ...
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Alissa Hummer @alissahummer.com · 14/05/2025
It was great to be involved in the evaluation of nucleic acid structure prediction in CASP16! 🧬 RNA modeling remains challenging for deep learning, esp. in the absence of templates and for long-range tertiary/quaternary interactions. Encouraging signs from deep evolutionary data though.
biorxiv.org
Assessment of nucleic acid structure prediction in CASP16
Consistently accurate 3D nucleic acid structure prediction would facilitate studies of the diverse RNA and DNA molecules underlying life. In CASP16, blind predictions for 42 targets canvassing a full ...
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Alissa Hummer @alissahummer.com · 03/04/2025
I'm so excited to join this interdisciplinary community as a 2025 Schmidt Science Fellow! After years behind a keyboard, I will be pivoting toward the wet lab. To unlock the true potential of ML for biology/biomedicine, we need high-quality data and robust evaluation 🔬🧫🧪
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Reposted by Alissa Hummer
Oxford Protein Informatics Group (OPIG) @opig.stats.ox.ac.uk · 27/03/2025
AntiFold, our antibody inverse folding model, has been published at Bioinformatics Advances. Work led by @magnushoie.bsky.social & @alissahummer.com. Paper: academic.oup.com/bioinformati... Webserver: opig.stats.ox.ac.uk/webapps/anti... Codebase available on Github: github.com/oxpig/AntiFold
academic.oup.com
AntiFold: Improved structure-based antibody design using inverse folding
AbstractSummary. The design and optimization of antibodies requires an intricate balance across multiple properties. Protein inverse folding models, capabl
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