We present a multi-objective binder design paradigm based on instruction fine-tuning and direct preference optimization (DPO) of autoregressive protein language models (pLMs). Multiple design objectives are encoded in the language model through direct optimization on expert curated preference sequence datasets comprising preferred and dispreferred distributions. We show the proposed alignment strategy enables ProtGPT2 to effectively design binders conditioned on specified receptors and a drug developability criterion. Generated binder samples demonstrate median isoelectric point (pI) improvements by $17\%-60\%$.

翻译：我们提出了一种基于自回归蛋白质语言模型的指令微调与直接偏好优化（DPO）的多目标配体设计范式。通过直接在专家标注的偏好序列数据集（包含优选和劣选分布）上进行优化，将多个设计目标编码至语言模型中。研究表明，该对齐策略使ProtGPT2能够根据指定受体和药物可开发性标准有效设计配体。生成的配体样本中位等电点（pI）提升幅度达$17\%-60\%$。