Antibody design is a time-consuming and expensive process that often requires1extensive experimentation to identify the best candidates. To address this challenge,2we propose an efficient and risk-aware antibody design framework that leverages3protein language models (PLMs) and batch Bayesian optimization (BO). Our4framework utilizes the generative power of protein language models to predict5candidate sequences with higher naturalness and a Bayesian optimization algorithm6to iteratively explore the sequence space and identify the most promising candidates.7To further improve the efficiency of the search process, we introduce a risk-aware8approach that balances exploration and exploitation by incorporating uncertainty9estimates into the acquisition function of the Bayesian optimization algorithm.10We demonstrate the effectiveness of our approach through experiments on several11benchmark datasets, showing that our framework outperforms state-of-the-art12methods in terms of both efficiency and quality of the designed sequences. Our13framework has the potential to accelerate the discovery of new antibodies and14reduce the cost and time required for antib