Reinforcement Learning for Antibody Sequence Infilling
Date:
Conference Talk at 21st International Conference on Computational Intelligence Methods for Bioinformatics and Biostatistics (CIBB 2026), Sapienza University of Rome, Rome, Italy
Conference talk, joint work with Chak Shing Lee, Conor F. Hayes, and Denis Vashchenko, on a framework that combines a pretrained antibody infilling language model with reinforcement learning to optimize functional properties. The model generates specific antibody regions, such as CDR loops, within full sequences and is fine-tuned either online with REINFORCE and PPO under KL regularization, or offline with Direct Reward Optimization on static experimental datasets. Across design tasks including binding affinity, immunogenicity, and expression, the approach improves alignment with measured biophysical properties and outperforms likelihood-only baselines.