Alissa Hummer
@alissahummer.com
Schmidt Science Fellow | Postdoc @ Stanford | Prev. DPhil @ Oxford || ML for Biomolecular Modeling & Design
Immensely grateful to the Schmidt Science Fellowship, my mentors, and all the wonderful & inspiring people I have worked with over the past years.
October 4, 2025 at 5:16 PM
Immensely grateful to the Schmidt Science Fellowship, my mentors, and all the wonderful & inspiring people I have worked with over the past years.
Huge thanks to my co-authors @cschneider.bsky.social, Lewis Chinery, Charlotte Deane @opig.stats.ox.ac.uk!
August 26, 2025 at 4:19 PM
Huge thanks to my co-authors @cschneider.bsky.social, Lewis Chinery, Charlotte Deane @opig.stats.ox.ac.uk!
More information about the paper: bsky.app/profile/alis...
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!
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...
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...
August 26, 2025 at 4:14 PM
More information about the paper: bsky.app/profile/alis...
Link to the paper: 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...
www.nature.com
August 26, 2025 at 4:14 PM
Link to the paper: www.nature.com/articles/s43...
Paper link: 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...
www.nature.com
July 9, 2025 at 4:18 PM
Paper link: www.nature.com/articles/s43...
And thank you to my co-authors @cschneider.bsky.social, Lewis Chinery, and Charlotte Deane – @opig.stats.ox.ac.uk!
July 9, 2025 at 4:18 PM
And thank you to my co-authors @cschneider.bsky.social, Lewis Chinery, and Charlotte Deane – @opig.stats.ox.ac.uk!
Thank you very much to the reviewers and editors who made this work stronger!
July 9, 2025 at 4:18 PM
Thank you very much to the reviewers and editors who made this work stronger!
The Graphinity code and synthetic datasets are publicly available at the following links.
GitHub: github.com/oxpig/Graphi...
Zenodo (code): doi.org/10.5281/zeno...
Zenodo (data): doi.org/10.5281/zeno...
OPIG (data): opig.stats.ox.ac.uk/data/downloa...
GitHub: github.com/oxpig/Graphi...
Zenodo (code): doi.org/10.5281/zeno...
Zenodo (data): doi.org/10.5281/zeno...
OPIG (data): opig.stats.ox.ac.uk/data/downloa...
GitHub - oxpig/Graphinity: Graphinity: Equivariant Graph Neural Network Architecture for Predicting Change in Antibody-Antigen Binding Affinity
Graphinity: Equivariant Graph Neural Network Architecture for Predicting Change in Antibody-Antigen Binding Affinity - oxpig/Graphinity
github.com
July 9, 2025 at 4:18 PM
The Graphinity code and synthetic datasets are publicly available at the following links.
GitHub: github.com/oxpig/Graphi...
Zenodo (code): doi.org/10.5281/zeno...
Zenodo (data): doi.org/10.5281/zeno...
OPIG (data): opig.stats.ox.ac.uk/data/downloa...
GitHub: github.com/oxpig/Graphi...
Zenodo (code): doi.org/10.5281/zeno...
Zenodo (data): doi.org/10.5281/zeno...
OPIG (data): opig.stats.ox.ac.uk/data/downloa...
Since the release of our preprint, we have made key updates including:
📊 Generation of a second synthetic dataset using Rosetta Flex ddG (20,829 ΔΔG values)
🕸️ Evaluation of additional ML architectures (incl. FLAML, CNN, Rotamer Density Estimate, Equiformer)
📊 Generation of a second synthetic dataset using Rosetta Flex ddG (20,829 ΔΔG values)
🕸️ Evaluation of additional ML architectures (incl. FLAML, CNN, Rotamer Density Estimate, Equiformer)
July 9, 2025 at 4:18 PM
Since the release of our preprint, we have made key updates including:
📊 Generation of a second synthetic dataset using Rosetta Flex ddG (20,829 ΔΔG values)
🕸️ Evaluation of additional ML architectures (incl. FLAML, CNN, Rotamer Density Estimate, Equiformer)
📊 Generation of a second synthetic dataset using Rosetta Flex ddG (20,829 ΔΔG values)
🕸️ Evaluation of additional ML architectures (incl. FLAML, CNN, Rotamer Density Estimate, Equiformer)
Antibody-antigen binding affinity lies at the heart of therapeutic antibody development.
We show that orders of magnitude more data will be needed to unlock generalizable ΔΔG prediction. Our findings provide a lower bound on data requirements to inform future method development & data collection.
We show that orders of magnitude more data will be needed to unlock generalizable ΔΔG prediction. Our findings provide a lower bound on data requirements to inform future method development & data collection.
July 9, 2025 at 4:18 PM
Antibody-antigen binding affinity lies at the heart of therapeutic antibody development.
We show that orders of magnitude more data will be needed to unlock generalizable ΔΔG prediction. Our findings provide a lower bound on data requirements to inform future method development & data collection.
We show that orders of magnitude more data will be needed to unlock generalizable ΔΔG prediction. Our findings provide a lower bound on data requirements to inform future method development & data collection.
See the full analysis, led by @rachael-kretsch.bsky.social, in our preprint: www.biorxiv.org/content/10.1...
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 ...
www.biorxiv.org
May 14, 2025 at 3:32 AM
See the full analysis, led by @rachael-kretsch.bsky.social, in our preprint: www.biorxiv.org/content/10.1...
Thank you so much, Nick!
April 4, 2025 at 2:09 PM
Thank you so much, Nick!
If you’re interested in building the data & ML to create molecular-resolution Virtual Cell Models, I would love to chat 🧫💻
April 3, 2025 at 3:35 PM
If you’re interested in building the data & ML to create molecular-resolution Virtual Cell Models, I would love to chat 🧫💻
And thank you to the @schmidtsciences.bsky.social program for encouraging and supporting disciplinary pivots to enable greater impact for our science!
April 3, 2025 at 3:35 PM
And thank you to the @schmidtsciences.bsky.social program for encouraging and supporting disciplinary pivots to enable greater impact for our science!
A huge thank you to all my mentors & lab-mates who have shaped my scientific journey and supported my pivots so far!
Special thanks to Charlotte Deane & @opig.stats.ox.ac.uk, @deboramarks.bsky.social, Madan Babu, @pstansfeld.bsky.social, @rdaslab.bsky.social
Special thanks to Charlotte Deane & @opig.stats.ox.ac.uk, @deboramarks.bsky.social, Madan Babu, @pstansfeld.bsky.social, @rdaslab.bsky.social
April 3, 2025 at 3:35 PM
A huge thank you to all my mentors & lab-mates who have shaped my scientific journey and supported my pivots so far!
Special thanks to Charlotte Deane & @opig.stats.ox.ac.uk, @deboramarks.bsky.social, Madan Babu, @pstansfeld.bsky.social, @rdaslab.bsky.social
Special thanks to Charlotte Deane & @opig.stats.ox.ac.uk, @deboramarks.bsky.social, Madan Babu, @pstansfeld.bsky.social, @rdaslab.bsky.social