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.

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 🔬

Schmidt Science Fellows@schmidtfellows.bsky.social · 11mo ago

We're so excited to welcome our 2025 Fellows to Oxford for their first Science Leadership Program convening. And for 2025 Fellow @alissahummer.com it is a homecoming! Our 2025 Fellows' stories and our Science Leadership Program exemplify our mission. schmidtsciencefellows.org/news/our-202...

Our paper on generalizable antibody-antigen binding affinity prediction has been featured on the cover of the August Issue of @natcomputsci.nature.com! 📔🎉

Nature Computational Science@natcomputsci.nature.com · last yr.

🚨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...

🚨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...

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...

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...

nature.com

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!

Nature Computational Science@natcomputsci.nature.com · last yr.

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...

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.

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 ...

doi.org

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.

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 ...

biorxiv.org

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 🔬🧫🧪

Schmidt Science Fellows@schmidtfellows.bsky.social · last yr.

We are excited to announce our 2025 Schmidt Science Fellows! 32 early career researchers, nominated by the world’s leading research universities, who will take an interdisciplinary approach to advancing discovery schmidtsciencefellows.org/news/2025-fe...

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

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

academic.oup.com