Sun, K.; Wang, Y. E.; Purnomo, J. C.; Cavanagh, J. M.; Alteri, G. B.; Head-Gordon, T.

Fragment-based drug discovery (FBDD) relies heavily on the design of chemically viable linkers to connect fragments binding to different pocket regions into potent lead molecules. While recent generative models have advanced spatial fragment linking, they frequently produce linkers characterized by high torsional strain and non-drug-like motifs. In this work, we present LinkLlama, a fine-tuned Meta Llama 3 model that bridges the gap between text-based generation and 3D spatial awareness. By accepting natural language prompts that specify geometric constraints, such as distances and angles, alongside physicochemical targets like Lipinski's rules and rotatable bond limits, LinkLlama generates highly tailored molecules for the input fragments. Leveraging the inherent chemical grammar captured through supervised fine-tuning on a curated corpus of drug-like molecules from ChEMBL, the model prioritizes chemical validity without requiring complex reinforcement learning loops. Benchmarking on the ZINC and HiQBind datasets demonstrates that LinkLlama maintains competitive geometric fidelity compared to strictly 3D-aware models while achieving a two-fold increase in the proportion of chemically reasonable designs. This rising success rate, jumping from ~35\% to over 80\%, is defined by strict adherence to comprehensive structural filters including PAINS, non-drug-like chemical patterns and complex ring systems. We further illustrate the model's versatility through prospective case studies in novel small-molecule scaffold hopping and PROTAC linker design, validated via molecular docking and molecular dynamics simulations against known crystal poses. Ultimately, LinkLlama demonstrates that large language models can overcome the structural pitfalls of purely 3D-generative methods, offering a highly controllable and chemically robust framework to accelerate linker design and drug discovery in general.