Chain-of-Molecules: Agentic Reasoning in Chemical Space for Constrained Molecular Optimization

Chain-of-thought reasoning has enabled language models to solve complex problems through explicit intermediate steps. We ask whether this paradigm can be transferred from language into chemical space. We introduce Chain-of-Molecules (CoM), a molecular-generation paradigm in which intermediate reasoning states are molecules and transitions between them are explicit chemical edits. In Agentic CoM, a symbolic environment executes valid edits, rejects infeasible ones, and returns the resulting molecule, allowing the model to explore alternatives, backtrack, and adapt its subsequent actions. We evaluate CoM in two complementary settings. First, a controlled property-targeting benchmark compares four generation paradigms across in-distribution, interpolation, and extrapolation regimes, disentangling the contributions of intermediate molecular states and interleaved environment execution. Second, we ask which generation paradigm yields the most adaptable base model for molecular optimization: starting directly from the respective pretrained base models, we use reinforcement learning to adapt them to three downstream bioactivity objectives without task-specific supervised finetuning. Agentic CoM performs strongest across both settings. Together, these results support explicit molecular trajectories and environment feedback not only as an effective optimization paradigm, but as a basis for reusable pretrained molecular optimizers. Code, data, and model checkpoints are publicly available.