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Molecular Models

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Chemistry-Enhanced Diffusion-Based Framework for Small-to-Large Molecular Conformation Generation.

Yifei Zhu^1,2, Jiahui Zhang^1,2, Jiawei Peng²

¹SCNU Environmental Research Institute, Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety, School of Environment, South China Normal University, Guangzhou 510006, P. R. China.

The Journal of Physical Chemistry Letters

|April 29, 2026

Summary

StoL, a new framework, rapidly generates large molecule 3D structures using a fragment-based diffusion model. This knowledge-free approach bypasses the need for large molecule training data, offering scalable and transferable predictions.

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Area of Science:

Computational Chemistry
Machine Learning
Structural Biology

Background:

Predicting 3D molecular structures at the quantum chemistry level is computationally intensive.
Existing machine learning methods struggle with large molecules, requiring significant computational resources.

Purpose of the Study:

Introduce StoL, a novel diffusion-model-based framework for rapid, knowledge-free generation of large molecular structures.
Enable the assembly of large molecules from small-molecule data without prior exposure to similar structures.

Main Methods:

StoL decomposes input molecules (SMILES) into chemically valid fragments.
A diffusion model, trained on small molecules, generates 3D structures for these fragments.
Fragments are then assembled into diverse conformations using a LEGO-style approach.

Main Results:

StoL achieves rapid generation of large molecular structures.
The fragment-based strategy eliminates the need for large-molecule training data.
Generated structures demonstrate high scalability, transferability, and broad configurational coverage, validated by DFT calculations.

Conclusions:

StoL offers a scalable and transferable solution for predicting large molecular 3D structures.
The knowledge-free, fragment-based diffusion model approach accelerates molecular structure generation.
StoL provides chemically rational structures with broad configurational coverage, overcoming limitations of current methods.