Large language models (LLMs) are revolutionizing drug discovery by providing data-driven insights into biomolecular design. However, their application to protein-based drug editing systems is hindered by the complexity of protein sequences, high computational demands, and data privacy concerns associated with remote APIs. To address these issues, we present LID-Drug, a Localized Interactive Domain-aggregated framework for protein drug editing. We first propose the Aggregator-Driven Domain Reasoning (ADDR) module, which converts raw amino acid sequences into domain-aggregated input to enhance the LLMs’ understanding of complex protein structures. Secondly, we design an interactive mechanism driven by two key modules: one is Domain-Aware Prompt Construction (DAPC), and the other is Retrieval and Domain Feedback (ReDF). This interactive feedback loop incrementally refines each generation step by incorporating domain-specific retrieval and structured expert feedback. To preserve data privacy, LID-Drug fine-tunes LLMs locally using domain-specific datasets. To address the time demands of fine-tuning large models, we introduce a dynamic low-rank projection optimizer to accelerate fine-tuning convergence. Empirical results demonstrate that LID-Drug achieves a state-of-the-art hit ratio on public protein datasets, outperforming baseline methods by 22.5% to 42.62% while also reducing fine-tuning steps by 25%.