Our goal is to build an AI-powered retrosynthesis engine that can automatically trace a complex target molecule—or any user-defined product concept—back to a handful of inexpensive, readily purchasable starting materials, proposing step-wise disconnections, plausible reagents, and reaction conditions in natural-language form; the entire pipeline is driven by a large-language-model framework augmented with retrieval and reinforcement learning, enabling rapid, explainable route planning without hand-coded templates.
