Mamba-driven multi-perspective structural understanding for molecular ground-state conformation prediction

2025-11-10

AI Summary

This work addresses the insufficient accuracy of molecular ground-state conformation prediction by proposing MPSU-Mamba, the first framework to adapt the state-space model Mamba for molecular structure understanding, establishing a multi-perspective sequential modeling paradigm. Methodologically, it introduces a tri-view scanning strategy—encoding atomic types, 3D geometric positions, and topological connectivity—and incorporates a bright-channel guidance mechanism to enhance selective capture of critical conformational features. Evaluated on QM9 and Molecule3D, MPSU-Mamba significantly outperforms existing state-of-the-art models, maintaining strong generalization even under few-shot settings. The core contribution lies in the novel adaptation of Mamba to molecular graph-structured data, empirically validating its effectiveness and robustness for 3D conformation prediction.

Technology Category

• Machine Learning: Structured Learning
• Intelligent Robots: State Estimation
• Computer Vision: 3D Computer Vision

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding

Abstract

A comprehensive understanding of molecular structures is important for the prediction of molecular ground-state conformation involving property information. Meanwhile, state space model (e.g., Mamba) has recently emerged as a promising mechanism for long sequence modeling and has achieved remarkable results in various language and vision tasks. However, towards molecular ground-state conformation prediction, exploiting Mamba to understand molecular structure is underexplored. To this end, we strive to design a generic and efficient framework with Mamba to capture critical components. In general, molecular structure could be considered to consist of three elements, i.e., atom types, atom positions, and connections between atoms. Thus, considering the three elements, an approach of Mamba-driven multi-perspective structural understanding (MPSU-Mamba) is proposed to localize molecular ground-state conformation. Particularly, for complex and diverse molecules, three different kinds of dedicated scanning strategies are explored to construct a comprehensive perception of corresponding molecular structures. And a bright-channel guided mechanism is defined to discriminate the critical conformation-related atom information. Experimental results on QM9 and Molecule3D datasets indicate that MPSU-Mamba significantly outperforms existing methods. Furthermore, we observe that for the case of few training samples, MPSU-Mamba still achieves superior performance, demonstrating that our method is indeed beneficial for understanding molecular structures.

Problem

• Predicting molecular ground-state conformation using Mamba models
• Understanding molecular structures from multiple perspectives efficiently
• Improving conformation prediction for complex molecules with limited data

Innovation

• Mamba-driven multi-perspective structural understanding framework
• Three dedicated scanning strategies for molecular perception
• Bright-channel mechanism for critical conformation discrimination