# AntBO: Towards Real-World Automated Antibody Design with Combinatorial Bayesian Optimisation

arXiv: https://arxiv.org/abs/2201.12570 · HTML: https://mlsanity.com/paper/2201.12570/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 134 references that are in the map.
- First posted on arXiv 29 Jan 2022.

## Builds on (most cited first, 18 of 18)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [A Tutorial on Bayesian Optimization](https://mlsanity.com/paper/1807.02811/) (2018)
- [Scalable Global Optimization via Local Bayesian Optimization](https://mlsanity.com/paper/1910.01739/) (2019)
- [Bayesian Optimization is Superior to Random Search for Machine Learning Hyperparameter Tuning: Analysis of the Black-Box Optimization Challenge 2020](https://mlsanity.com/paper/2104.10201/) (2021)
- [Iterative Refinement Graph Neural Network for Antibody Sequence-Structure Co-design](https://mlsanity.com/paper/2110.04624/) (2021)
- [Combinatorial Bayesian Optimization using the Graph Cartesian Product](https://mlsanity.com/paper/1902.00448/) (2019)
- [Simulation Intelligence: Towards a New Generation of Scientific Methods](https://mlsanity.com/paper/2112.03235/) (2021)
- [Bayesian Optimization of Combinatorial Structures](https://mlsanity.com/paper/1806.08838/) (2018)
- [BOSS: Bayesian Optimization over String Spaces](https://mlsanity.com/paper/2010.00979/) (2020)
- [High-Dimensional Bayesian Optimisation with Variational Autoencoders and Deep Metric Learning](https://mlsanity.com/paper/2106.03609/) (2021)
- [Think Global and Act Local: Bayesian Optimisation over High-Dimensional Categorical and Mixed Search Spaces](https://mlsanity.com/paper/2102.07188/) (2021)
- [A General Framework for Constrained Bayesian Optimization using Information-based Search](https://mlsanity.com/paper/1511.09422/) (2015)
- [Fold2Seq: A Joint Sequence(1D)-Fold(3D) Embedding-based Generative Model for Protein Design](https://mlsanity.com/paper/2106.13058/) (2021)
- [Pre-training of Graph Neural Network for Modeling Effects of Mutations on Protein-Protein Binding Affinity](https://mlsanity.com/paper/2008.12473/) (2020)
- [Kernels over Sets of Finite Sets using RKHS Embeddings, with Application to Bayesian (Combinatorial) Optimization](https://mlsanity.com/paper/1910.04086/) (2019)
- [Neural message passing for joint paratope-epitope prediction](https://mlsanity.com/paper/2106.00757/) (2021)
- [Are we Forgetting about Compositional Optimisers in Bayesian Optimisation?](https://mlsanity.com/paper/2012.08240/) (2020)
- [Combinatorial Black-Box Optimization with Expert Advice](https://mlsanity.com/paper/2006.03963/) (2020)

## Built on by (most cited first, 16 of 16)

- [Accelerating Bayesian Optimization for Biological Sequence Design with Denoising Autoencoders](https://mlsanity.com/paper/2203.12742/) (2022)
- [Conditional Antibody Design as 3D Equivariant Graph Translation](https://mlsanity.com/paper/2208.06073/) (2022)
- [Full-Atom Peptide Design based on Multi-modal Flow Matching](https://mlsanity.com/paper/2406.00735/) (2024)
- [Reprogramming Pretrained Language Models for Antibody Sequence Infilling](https://mlsanity.com/paper/2210.07144/) (2022)
- [Framework and Benchmarks for Combinatorial and Mixed-variable Bayesian Optimization](https://mlsanity.com/paper/2306.09803/) (2023)
- [Hotspot-Driven Peptide Design via Multi-Fragment Autoregressive Extension](https://mlsanity.com/paper/2411.18463/) (2024)
- [Active learning for affinity prediction of antibodies](https://mlsanity.com/paper/2406.07263/) (2024)
- [Sample-Efficient Optimisation with Probabilistic Transformer Surrogates](https://mlsanity.com/paper/2205.13902/) (2022)
- [Efficient and Principled Scientific Discovery through Bayesian Optimization: A Tutorial](https://mlsanity.com/paper/2604.01328/) (2026)
- [Protein Sequence Design with Batch Bayesian Optimisation](https://mlsanity.com/paper/2303.10429/) (2023)
- [BetterBodies: Reinforcement Learning guided Diffusion for Antibody Sequence Design](https://mlsanity.com/paper/2409.16298/) (2024)
- [Stable Online and Offline Reinforcement Learning for Antibody CDRH3 Design](https://mlsanity.com/paper/2401.05341/) (2023)
- [MOCA-HESP: Meta High-dimensional Bayesian Optimization for Combinatorial and Mixed Spaces via Hyper-ellipsoid Partitioning](https://mlsanity.com/paper/2508.06847/) (2025)
- [Conformational Rank Conditioned Committees for Machine Learning-Assisted Directed Evolution](https://mlsanity.com/paper/2510.24974/) (2025)
- [BOAT: Navigating the Sea of In Silico Predictors for Antibody Design via Multi-Objective Bayesian Optimization](https://mlsanity.com/paper/2604.13980/) (2026)
- [Large Discovery Models: Empirically-grounded Model-Based Open-Ended Search](https://mlsanity.com/paper/2608.15669/) (2026)

## Abstract (arXiv)

Antibodies are canonically Y-shaped multimeric proteins capable of highly specific molecular recognition. The CDRH3 region located at the tip of variable chains of an antibody dominates antigen-binding specificity. Therefore, it is a priority to design optimal antigen-specific CDRH3 regions to develop therapeutic antibodies. However, the combinatorial nature of CDRH3 sequence space makes it impossible to search for an optimal binding sequence exhaustively and efficiently using computational approaches. Here, we present textttAntBO: a combinatorial Bayesian optimisation framework enabling efficient textitin silico design of the CDRH3 region. Ideally, antibodies are expected to have high target specificity and developability. We introduce a CDRH3 trust region that restricts the search to sequences with favourable developability scores to achieve this goal. For benchmarking, textttAntBO uses the textttAbsolut! software suite as a black-box oracle to score the target specificity and affinity of designed antibodies textitin silico in an unconstrained fashion~citeprobert2021one. The experiments performed for 159 discretised antigens used in textttAbsolut! demonstrate the benefit of textttAntBO in designing CDRH3 regions with diverse biophysical properties. In under 200 calls to black-box oracle, textttAntBO can suggest antibody sequences that outperform the best binding sequence drawn from 6.9 million experimentally obtained CDRH3s and a commonly used genetic algorithm baseline. Additionally, textttAntBO finds very-high affinity CDRH3 sequences in only 38 protein designs whilst requiring no domain knowledge. We conclude textttAntBO brings automated antibody design methods closer to what is practically viable for in vitro experimentation.