# Guided Sequence-Structure Generative Modeling for Iterative Antibody Optimization

arXiv: https://arxiv.org/abs/2509.16357 · HTML: https://mlsanity.com/paper/2509.16357/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 36 references that are in the map.
- First posted on arXiv 19 Sep 2025.

## Builds on (most cited first, 16 of 16)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [Generative Flows on Discrete State-Spaces: Enabling Multimodal Flows with Applications to Protein Co-Design](https://mlsanity.com/paper/2402.04997/) (2024)
- [Protein Design with Guided Discrete Diffusion](https://mlsanity.com/paper/2305.20009/) (2023)
- [Simple Guidance Mechanisms for Discrete Diffusion Models](https://mlsanity.com/paper/2412.10193/) (2024)
- [SE(3)-Stochastic Flow Matching for Protein Backbone Generation](https://mlsanity.com/paper/2310.02391/) (2023)
- [Unlocking Guidance for Discrete State-Space Diffusion and Flow Models](https://mlsanity.com/paper/2406.01572/) (2024)
- [Accelerating Bayesian Optimization for Biological Sequence Design with Denoising Autoencoders](https://mlsanity.com/paper/2203.12742/) (2022)
- [Local Latent Space Bayesian Optimization over Structured Inputs](https://mlsanity.com/paper/2201.11872/) (2022)
- [Fine-Tuning Discrete Diffusion Models via Reward Optimization with Applications to DNA and Protein Design](https://mlsanity.com/paper/2410.13643/) (2024)
- [Protein Discovery with Discrete Walk-Jump Sampling](https://mlsanity.com/paper/2306.12360/) (2023)
- [Sequence-Augmented SE(3)-Flow Matching For Conditional Protein Backbone Generation](https://mlsanity.com/paper/2405.20313/) (2024)
- [AbDiffuser: Full-Atom Generation of in vitro Functioning Antibodies](https://mlsanity.com/paper/2308.05027/) (2023)
- [Steering Masked Discrete Diffusion Models via Discrete Denoising Posterior Prediction](https://mlsanity.com/paper/2410.08134/) (2024)
- [Bayesian Optimization of Antibodies Informed by a Generative Model of Evolving Sequences](https://mlsanity.com/paper/2412.07763/) (2024)
- [Generative Humanization for Therapeutic Antibodies](https://mlsanity.com/paper/2412.04737/) (2024)

## Built on by (most cited first, 3 of 3)

- [How to make the most of your masked language model for protein engineering](https://mlsanity.com/paper/2603.10302/) (2026)
- [A Unification of Discrete, Gaussian, and Simplicial Diffusion](https://mlsanity.com/paper/2512.15923/) (2025)
- [Is Sequence Information All You Need for Bayesian Optimization of Antibodies?](https://mlsanity.com/paper/2509.24933/) (2025)

## Abstract (arXiv)

Therapeutic antibody candidates often require extensive engineering to improve key functional and developability properties before clinical development. This can be achieved through iterative design, where starting molecules are optimized over several rounds of in vitro experiments. While protein structure can provide a strong inductive bias, it is rarely used in iterative design due to the lack of structural data for continually evolving lead molecules over the course of optimization. In this work, we propose a strategy for iterative antibody optimization that leverages both sequence and structure as well as accumulating lab measurements of binding and developability. Building on prior work, we first train a sequence-structure diffusion generative model that operates on antibody-antigen complexes. We then outline an approach to use this model, together with carefully predicted antibody-antigen complexes, to optimize lead candidates throughout the iterative design process. Further, we describe a guided sampling approach that biases generation toward desirable properties by integrating models trained on experimental data from iterative design. We evaluate our approach in multiple in silico and in vitro experiments, demonstrating that it produces high-affinity binders at multiple stages of an active antibody optimization campaign.