In silico proof of principle of machine learning-based antibody design at unconstrained scale
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Abstract
Generative machine learning (ML) has been postulated to become a major driver in the computational design of antigen-specific monoclonal antibodies (mAb). However, efforts to confirm this hypothesis have been hindered by the infeasibility of testing arbitrarily large numbers of antibody sequences for their most critical design parameters: paratope, epitope, affinity, and developability. To address this challenge, we leveraged a lattice-based antibody-antigen binding simulation framework, which incorporates a wide range of physiological antibody-binding parameters. The simulation framework enables the computation of synthetic antibody-antigen 3D-structures, and it functions as an oracle for unrestricted prospective evaluation and benchmarking of antibody design parameters of ML-generated antibody sequences. We found that a deep generative model, trained exclusively on antibody sequence (one dimensional: 1D) data can be used to design conformational (three dimensional: 3D) epitope-specific antibodies, matching, or exceeding the training dataset in affinity and developability parameter value variety. Furthermore, we established a lower threshold of sequence diversity necessary for high-accuracy generative antibody ML and demonstrated that this lower threshold also holds on experimental real-world data. Finally, we show that transfer learning enables the generation of high-affinity antibody sequences from low-N training data. Our work establishes a priori feasibility and the theoretical foundation of high-throughput ML-based mAb design.
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Akbar et al. showed promising performance in predicting binding affinity to 3D antigen structures using sequence information alone.
“Akbar et al. [61] have shown promising performance in predicting binding affinity to 3D antigen structures using sequence information alone.”
From Computational and artificial intelligence-based methods for antibody development (2023) · cites this paper as [61]
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- Authors
- Akbar, R., Robert, P. A., Weber, C. R., Widrich, M., Frank, R., Pavlović, M., Scheffer, L., Chernigovskaya, M., Snapkov, I., Slabodkin, A., Mehta, B. B., Miho, E., Lund-Johansen, F., Andersen, J. T., Hochreiter, S., Hobæk Haff, I., Klambauer, G., Sandve, G. K., & Greiff, V.
- Affiliations
- Oslo University Hospital (NO)
- ETH Zurich (CH)
- Johannes Kepler University of Linz (AT)
- University of Oslo (NO)
- FHNW University of Applied Sciences and Arts Northwestern Switzerland (CH)
- Department of Immunology, Oslo University Hospital Rikshospitalet and University of Oslo, Norway
- Department of Mathematics, University of Oslo, Oslo, Norway
- Corresponding author
- Akbar, Rahmad
- Greiff, Victor
- ORCID
- Akbar, Rahmad
- Robert, Philippe A.
- Weber, Cédric R.
- Widrich, Michael
- Frank, Robert
- Pavlović, Milena
- Scheffer, Lonneke
- Chernigovskaya, Maria
- Snapkov, Igor
- Slabodkin, Andrei
- Mehta, Brij Bhushan
- Miho, Enkelejda
- Lund-Johansen, Fridtjof
- Andersen, Jan Terje
- Hochreiter, Sepp
- Klambauer, Günter
- Sandve, Geir Kjetil
- Greiff, Victor
- Venue
- mAbs
- Publisher
- Informa UK Limited
- Published
- 2022-04-04
- Volume
- 14
- Issue
- 1
- Pages
- 2031482-2031482
- ISSN
- 1942-0862 (ISSN-L)
- 1942-0870
- PMC
- PMC8986205
- OpenAlex
- W4225693775
- Cited by
- 94
- References
- 74
- Citations by year
- 2026: 8 · 2025: 17 · 2024: 31 · 2023: 19 · 2022: 14 · 2021: 5
- FWCI
- 11.52
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- mAbs (publishedVersion, cc-by-nc)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- NORA - Norwegian Open Research Archives (submittedVersion, cc-by-nc)
- Repository for Publications and Research Data (ETH Zurich) (submittedVersion, cc-by-nc)
- ETH Zürich Research Collection
- Funders
- the Leona M.and Harry B.Helmsley Charitable Trust (#2019PG-T1D011)
- UiO World-Leading Research Community
- LifeScience Convergence Environment Immunolingo
- EU Horizon 2020 iReceptorplus (825821)
- Research Council of Norway FRIPRO (300740)
- Research Council of Norway IKTPLUSS (311341)
- Norwegian Cancer Society (215817)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Monoclonal and Polyclonal Antibodies Research
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › vaccines and immunoinformatics approaches
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Keywords
- monoclonal antibodies, epitope-specific antibodies, antibody binding sites, lattice simulation, deep generative models, antibody affinity, developability, sequence diversity, transfer learning
- MeSH terms
- Machine Learning, Antibodies, Monoclonal, Antigen-Antibody Reactions, Epitopes, Binding Sites, Antibody
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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