Molecular ground-state conformation prediction based on the Mamba state space model.
Yuxin Gou1, Aming Wu2, Richang Hong1
1School of Computer Science and Information Engineering, Hefei University of Technology, Hefei, China.
Communications Chemistry
|July 6, 2026
Summary
This study introduces MPSU-Mamba, a novel framework using Mamba for molecular structure understanding. It accurately predicts molecular ground-state conformation, outperforming existing methods, especially with limited data.
Area of Science:
- Computational Chemistry
- Machine Learning
- Structural Biology
Background:
- Understanding molecular structures is crucial for predicting properties and conformations.
- State space models like Mamba show promise in sequence modeling but are underexplored for molecular structure tasks.
- Existing methods for molecular ground-state conformation prediction have limitations.
Purpose of the Study:
- To develop a novel framework, MPSU-Mamba, leveraging Mamba for enhanced molecular structure understanding.
- To accurately predict molecular ground-state conformation by capturing atom types, positions, and connections.
- To address the underexplored application of Mamba in molecular conformation prediction.
Main Methods:
- Proposed Mamba-driven Multi-Perspective Structural Understanding (MPSU-Mamba) framework.
- Incorporated three distinct scanning strategies for comprehensive molecular perception.
- Introduced a Max-Activation Gating mechanism to identify critical conformation-related atom information.
Main Results:
- MPSU-Mamba significantly outperformed existing methods on QM9 and Molecule3D datasets.
- Demonstrated superior performance even with limited training samples.
- The framework effectively captures essential molecular structural components.
Conclusions:
- MPSU-Mamba provides a powerful and effective approach for molecular ground-state conformation prediction.
- The method shows robustness and high performance, particularly in data-scarce scenarios.
- This work highlights the potential of Mamba-based models in computational chemistry and molecular modeling.
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