Pangaea: Agentic Search over a Unified Construction Space for Automated Molecular Three-Dimensional Descriptor Design

Chemically informed molecular descriptors remain valuable for machine-learning based molecular property prediction, especially in data-limited regimes. Among these, three-dimensional (3D) descriptors are particularly appealing due to their ability to encode rich spatial chemical information. However, task-specific 3D descriptor design requires substantial expertise and remains difficult to automate. We abstract 3D descriptor construction as a three-stage process consisting of Formation, Extraction, and Compression, starting from chemically annotated molecular point-cloud sources. This abstraction enables new descriptors to be constructed by combining stage-wise operators and their parameters, thereby reformulating task-specific 3D descriptor design as a search for the combination best suited to each task. Building on this formulation, we propose *Pangaea*, which provides a Unified Construction Space that structures candidate descriptors as executable pipelines, together with an Incumbent Revision Agent that uses a large language model to iteratively refine the best-performing pipeline based on evaluation history, design-space semantics and chemical priors. QM9 experiments under data-limited conditions show *Pangaea* ranks first among descriptor-based methods, achieves a 24% relative reduction in test MAE, and outperforms four deep-learning baselines.