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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.

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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.

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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.