GeoRecon: Graph-Level Representation Learning for 3D Molecules via Reconstruction-Based Pretraining

• URL: http://arxiv.org/abs/2506.13174v1
• Date: Mon, 16 Jun 2025 07:35:49 GMT
• Title: GeoRecon: Graph-Level Representation Learning for 3D Molecules via Reconstruction-Based Pretraining
• Authors: Shaoheng Yan, Zian Li, Muhan Zhang,
• Abstract summary: We present GeoRecon, a graph-level pretraining framework for molecular representation learning.<n>During pretraining, the model is trained to generate a graph representation capable of accurately guiding reconstruction of the molecular geometry.<n>GeoRecon outperforms node-centric baselines on multiple molecular benchmarks.
• Score: 19.398985037052224
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The pretraining-and-finetuning paradigm has driven significant advances across domains, such as natural language processing and computer vision, with representative pretraining paradigms such as masked language modeling and next-token prediction. However, in molecular representation learning, the task design remains largely limited to node-level denoising, which is effective at modeling local atomic environments, yet maybe insufficient for capturing the global molecular structure required by graph-level property prediction tasks, such as energy estimation and molecular regression. In this work, we present GeoRecon, a novel graph-level pretraining framework that shifts the focus from individual atoms to the molecule as an integrated whole. GeoRecon introduces a graph-level reconstruction task: during pretraining, the model is trained to generate an informative graph representation capable of accurately guiding reconstruction of the molecular geometry. This encourages the model to learn coherent, global structural features rather than isolated atomic details. Without relying on additional supervision or external data, GeoRecon outperforms node-centric baselines on multiple molecular benchmarks (e.g., QM9, MD17), demonstrating the benefit of incorporating graph-level reconstruction for learning more holistic and geometry-aware molecular embeddings.

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