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Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

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OTMol: Robust Molecular Structure Comparison via Optimal Transport.

Xiaoqi Wei, Xuhang Dai, Yaqi Wu

|September 15, 2025

Summary

This summary is machine-generated.

We developed OTMol, a novel method using optimal transport for molecular alignment. OTMol accurately matches atoms, preserving chemical features like chirality and improving structural comparisons in cheminformatics.

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Area of Science:

Computational chemistry
Structural bioinformatics
Cheminformatics

Background:

Root-mean-square deviation (RMSD) is crucial for assessing molecular similarity.
Traditional RMSD methods struggle with atom ordering, chirality, and cluster comparisons.
Existing alignment algorithms often fail to generalize across diverse chemical structures.

Purpose of the Study:

To introduce OTMol, a new method for accurate molecular alignment.
To address limitations of traditional RMSD calculation methods.
To provide a generalizable framework for structural comparison tasks.

Main Methods:

Formulated molecular alignment as a fused supervised Gromov-Wasserstein (fsGW) optimal transport problem.
Leveraged intrinsic geometric and topological molecular information.
Ensured one-to-one atom mapping to preserve molecular integrity.

Main Results:

OTMol achieves low RMSD values across diverse systems (ligands, peptides, lipids, clusters).
The method preserves chirality and bond connectivity.
OTMol demonstrates computational efficiency and avoids erroneous many-to-one alignments.

Conclusions:

Optimal transport theory offers a powerful approach for molecular alignment.
OTMol provides a principled, data-driven, and generalizable solution for structural comparison.
This framework advances applications in cheminformatics and molecular modeling.