Emergent Large Language Models (LLMs) show impressive capabilities in performing a wide range of tasks. These models can be harnessed for biophysical use, as well. The main challenge in this endeavor lies in transforming 3D chemical data into 1D language-like data. We developed a method to transform molecular data into language-like data and tokenize it for LLM use in biophysical context. We then trained a model, 3bmGPT, and validated it with a known protein-ligand complex. Using the pre-trained result, the model can assess the chemical properties of targets, detect shared binding properties and structures, and reveal related drugs. 3bmGPT and the synthetic-language to describe binding interactions uncovered novel protein-protein networks influenced by ligands, indicating functionally related yet previously unreported interactions. We provide open access to a fully functional web-based tool utilizing 3bmGPT.