# Generative Humanization for Therapeutic Antibodies

arXiv: https://arxiv.org/abs/2412.04737 · HTML: https://mlsanity.com/paper/2412.04737/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 37 references that are in the map.
- First posted on arXiv 6 Dec 2024.

## Builds on (most cited first, 7 of 7)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Neural Discrete Representation Learning](https://mlsanity.com/paper/1711.00937/) (2017)
- [Plug and Play Language Models: A Simple Approach to Controlled Text Generation](https://mlsanity.com/paper/1912.02164/) (2019)
- [Autoregressive Diffusion Models](https://mlsanity.com/paper/2110.02037/) (2021)
- [BERT has a Mouth, and It Must Speak: BERT as a Markov Random Field Language Model](https://mlsanity.com/paper/1902.04094/) (2019)
- [Protein Design with Guided Discrete Diffusion](https://mlsanity.com/paper/2305.20009/) (2023)
- [Deciphering antibody affinity maturation with language models and weakly supervised learning](https://mlsanity.com/paper/2112.07782/) (2021)

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

- [Guided Sequence-Structure Generative Modeling for Iterative Antibody Optimization](https://mlsanity.com/paper/2509.16357/) (2025)
- [How to make the most of your masked language model for protein engineering](https://mlsanity.com/paper/2603.10302/) (2026)
- [Conditionally Site-Independent Neural Evolution of Antibody Sequences](https://mlsanity.com/paper/2602.18982/) (2026)

## Abstract (arXiv)

Antibody therapies have been employed to address some of today's most challenging diseases, but must meet many criteria during drug development before reaching a patient. Humanization is a sequence optimization strategy that addresses one critical risk called immunogenicity - a patient's immune response to the drug - by making an antibody more "human-like" in the absence of a predictive lab-based test for immunogenicity. However, existing humanization strategies generally yield very few humanized candidates, which may have degraded biophysical properties or decreased drug efficacy. Here, we re-frame humanization as a conditional generative modeling task, where humanizing mutations are sampled from a language model trained on human antibody data. We describe a sampling process that incorporates models of therapeutic attributes, such as antigen binding affinity, to obtain candidate sequences that have both reduced immunogenicity risk and maintained or improved therapeutic properties, allowing this algorithm to be readily embedded into an iterative antibody optimization campaign. We demonstrate in silico and in lab validation that in real therapeutic programs our generative humanization method produces diverse sets of antibodies that are both (1) highly-human and (2) have favorable therapeutic properties, such as improved binding to target antigens.