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[2507.09043] GAGA: Gaussianity-Aware Gaussian Approximation for Efficient 3D Molecular Generation
Summary
The paper presents GAGA, a method enhancing the efficiency of 3D molecular generation by leveraging Gaussian approximations, improving both quality and computational cost.
Why It Matters
This research addresses the significant computational challenges in 3D molecular generation, a critical area in drug discovery and materials science. By optimizing generative models, GAGA can accelerate research and development processes in these fields, making it highly relevant for both academia and industry.
Key Takeaways
- GAGA improves the efficiency of Gaussian Probability Path Generative Models (GPPGMs).
- The method identifies optimal steps in the generative process to enhance Gaussianity.
- It achieves better generation quality without sacrificing training fidelity.
- GAGA reduces the computational cost associated with long generative trajectories.
- Experimental results show substantial improvements in benchmarks for 3D molecular generation.
Computer Science > Machine Learning arXiv:2507.09043 (cs) [Submitted on 11 Jul 2025 (v1), last revised 13 Feb 2026 (this version, v2)] Title:GAGA: Gaussianity-Aware Gaussian Approximation for Efficient 3D Molecular Generation Authors:Jingxiang Qu, Wenhan Gao, Ruichen Xu, Yi Liu View a PDF of the paper titled GAGA: Gaussianity-Aware Gaussian Approximation for Efficient 3D Molecular Generation, by Jingxiang Qu and 2 other authors View PDF HTML (experimental) Abstract:Gaussian Probability Path based Generative Models (GPPGMs) generate data by reversing a stochastic process that progressively corrupts samples with Gaussian noise. Despite state-of-the-art results in 3D molecular generation, their deployment is hindered by the high cost of long generative trajectories, often requiring hundreds to thousands of steps during training and sampling. In this work, we propose a principled method, named GAGA, to improve generation efficiency without sacrificing training granularity or inference fidelity of GPPGMs. Our key insight is that different data modalities obtain sufficient Gaussianity at markedly different steps during the forward process. Based on this observation, we analytically identify a characteristic step at which molecular data attains sufficient Gaussianity, after which the trajectory can be replaced by a closed-form Gaussian approximation. Unlike existing accelerators that coarsen or reformulate trajectories, our approach preserves full-resolution learning dynamics whi...
