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EC-Conf: A ultra-fast diffusion model for molecular conformation generation with equivariant consistency
Zhiguang Fan1,2, Yuedong Yang1, Mingyuan Xu3
1School of Computer Science and Engineering, Sun Yat-Sen University, Guangzhou, 510006, China.
Journal of Cheminformatics
|September 3, 2024
Summary
An equivariant consistency model (EC-Conf) accelerates 3D molecule conformation generation. This fast diffusion method achieves high quality with significantly fewer computational steps than existing models.
Area of Science:
- Computational Chemistry
- Machine Learning
- Structural Biology
Background:
- Diffusion models have advanced 3D molecule conformation generation but suffer from high computational costs.
- Efficient generation of low-energy molecular conformations is crucial for drug discovery and materials science.
Purpose of the Study:
- To introduce a novel, computationally efficient method for generating low-energy 3D molecular conformations.
- To address the limitations of existing diffusion models in terms of speed and resource requirements.
Main Methods:
- Developed an equivariant consistency model (EC-Conf) utilizing a modified SE(3)-equivariant transformer.
- Implemented a highly efficient consistency diffusion process for generating molecular conformations directly from Cartesian coordinates.
- Evaluated performance on GEOM-QM9 and GEOM-Drugs datasets.
Main Results:
- EC-Conf achieves comparable conformation quality to state-of-the-art diffusion models using only one sampling step.
- Performance improves with a few additional sampling iterations.
- Demonstrated at least two orders of magnitude greater efficiency compared to current diffusion models for learning low-energy conformation distributions.
Conclusions:
- EC-Conf offers a significant improvement in the efficiency of diffusion-based conformation generation while maintaining high structural quality.
- The model serves as a general framework adaptable for complex structure generation tasks, including protein modeling.
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