Pharmaceutical three-dimensional (3D) printing is an advanced fabrication technology with the potential to enable truly personalised dosage forms. Recent studies have integrated artificial intelligence (AI) to accelerate formulation and process development, drastically transforming current approaches to pharmaceutical 3D printing. To date, most AI-driven efforts remain narrowly focused, while failing to account for the broader formulation challenges inherent to the technology. Recent advances in AI have introduced artificial general intelligence concepts, wherein systems extend beyond conventional predictive modelling toward more generalised, human-like reasoning. In this work, we investigate the application of large language models (LLMs), fine-tuned on a fused deposition modelling (FDM) dataset comprising over 1400 formulations, to recommend suitable excipients based on active pharmaceutical ingredient (API) dose, and predict filament mechanical properties. Four LLM architectures were fine-tuned, with systematic evaluation of both fine-tuning and generative parameter configurations. Our results demonstrate that Llama2 was best suited for recommending excipients for FDM formulations. Additionally, model selection and parameterisation significantly influence performance, with smaller LLMs exhibiting instances of catastrophic forgetting. Furthermore, we demonstrate: (i) even with relatively small dataset of over 1400 formulations, it can lead to model catastrophic forgetting; (ii) standard LLM metrics only evaluate linguistic performance but not formulation processability; and (iii) LLMs trained on biomedically-related data do not always produce the best results. Addressing these challenges is essential to advancing LLMs beyond linguistic proficiency and toward reliable systems for pharmaceutical formulation development.

翻译：药物三维（3D）打印是一项先进的制造技术，具有实现真正个性化给药剂型的潜力。近期研究通过整合人工智能（AI）以加速处方与工艺开发，彻底改变了当前药物3D打印的技术路径。迄今为止，大多数AI驱动的研究仍局限于特定领域，未能全面应对该技术固有的广泛处方挑战。人工智能的最新进展引入了通用人工智能概念，使系统能够超越传统的预测建模，实现更通用、类人的推理能力。本研究探索了基于熔融沉积成型（FDM）数据集（包含1400余种处方）微调的大语言模型（LLMs）的应用，旨在根据活性药物成分（API）剂量推荐适宜辅料，并预测线材力学性能。研究对四种LLM架构进行了微调，并系统评估了微调参数与生成参数的配置。结果表明，Llama2模型最适合为FDM处方推荐辅料。此外，模型选择与参数化显著影响性能，较小规模的LLMs出现了灾难性遗忘现象。本研究进一步揭示：（i）即使使用超过1400处方的相对小型数据集，仍可能导致模型灾难性遗忘；（ii）标准LLM评估指标仅衡量语言性能，未能评价处方工艺可行性；（iii）基于生物医学相关数据训练的LLMs并非总能产生最优结果。克服这些挑战对于推动LLMs超越语言精通能力、发展成为药物处方开发可靠系统至关重要。