Molecular function is governed by three-dimensional conformational space, yet foundation models and large language models naturally operate over discrete tokens rather than continuous molecular geometry. Here we introduce Chem3DLLM, a foundation-model interface that makes molecular conformational space accessible through representation, conditioning and physics-informed feedback. Chem3DLLM combines reversible compression of molecular tokenization (RCMT), which encodes atom types, coordinates and bond connectivity into compact language-compatible sequences; protein alignment, which conditions ligand conformational generation on binding-pocket context for structure-based drug design; and reinforcement learning with scientific feedback (RLSF), which uses molecular energetics to refine generated conformations. RCMT gives about threefold character compression while preserving deterministic reconstruction with root-mean-square deviation (RMSD) = 0. In protein-pocket-conditioned generation, Chem3DLLM achieves favourable predicted docking profiles, with average and median Vina scores of -7.21 and -7.10. The same interface supports SMILES-to-3D generation on QM9 with 100\% validity and 100\% uniqueness.Ablation indicates that RLSF adds a modest physics-informed refinement to a representation-driven framework. These results suggest a route for connecting foundation models to 3D molecular design through reversible representation, biological conditioning and physics-informed feedback.