  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 $17\%-60\%$.
