Discovery and optimization of monoclonal antibodies for therapeutic applications relies on large sequence libraries but is hindered by developability issues such as low solubility, high aggregation, and high immunogenicity. Generative language models, trained on millions of protein sequences, are a powerful tool for the on-demand generation of realistic, diverse sequences. We present the Immunoglobulin Language Model (IgLM), a deep generative language model for creating synthetic antibody libraries. Compared with prior methods that leverage unidirectional context for sequence generation, IgLM formulates antibody design based on text-infilling in natural language, allowing it to re-design variable-length spans within antibody sequences using bidirectional context. We trained IgLM on 558 million (M) antibody heavy- and light-chain variable sequences, conditioning on each sequence’s chain type and species of origin. We demonstrate that IgLM can generate full-length antibody sequences from a variety of species and its infilling formulation allows it to generate infilled complementarity-determining region (CDR) loop libraries with improved in silico developability profiles. A record of this paper’s transparent peer review process is included in the supplemental information.

用于治疗应用的单克隆抗体的发现和优化依赖于大型序列库，但受到低溶解度、高聚集和高免疫原性等可开发性问题的阻碍。经过数百万个蛋白质序列训练的生成语言模型是按需生成真实、多样化序列的强大工具。我们提出了免疫球蛋白语言模型（IgLM），这是一种用于创建合成抗体库的深度生成语言模型。与之前利用单向上下文生成序列的方法相比，IgLM 基于自然语言的文本填充来制定抗体设计，使其能够使用双向上下文重新设计抗体序列内的可变长度跨度。我们在 5.58 亿 (M) 个抗体重链和轻链可变序列上训练 IgLM，并根据每个序列的链类型和来源物种进行调节。我们证明 IgLM 可以生成来自多种物种的全长抗体序列，其填充配方使其能够生成填充互补决定区 (CDR) 环文库，并具有改进的计算机可开发性。补充信息中包含了本文透明同行评审过程的记录。