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Molecular Models

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Physical models representing molecular architectures of chemical compounds play essential roles in understanding chemistry. The use of molecular models makes it easier to visualize the structures and shapes of atoms and molecules.

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Updated: Jan 11, 2026

Interactive Molecular Model Assembly with 3D Printing

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Published on: August 13, 2020

Generating 3D Binding Molecules Using Shape-Conditioned Diffusion Models with Guidance.

Ziqi Chen¹, Bo Peng¹, Tianhua Zhai²

¹Computer Science and Engineering, The Ohio State University, Columbus, OH 43210.

Nature Machine Intelligence

|November 19, 2025

Summary

This summary is machine-generated.

A new generative artificial intelligence (genAI) method, DiffSMol, accelerates drug development by designing novel 3D molecules. This AI tool shows superior performance in generating molecules with desired shapes and binding affinities.

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Area of Science:

Computational chemistry
Artificial intelligence in drug discovery
Molecular modeling

Background:

Drug development is a lengthy and expensive process.
Generative artificial intelligence (genAI) offers potential solutions to streamline drug discovery.
Novel methods are needed to generate molecules with specific properties for optimal drug candidates.

Purpose of the Study:

To develop a novel genAI method, DiffSMol, for facilitating drug development.
To generate 3D binding molecules based on known ligand shapes and protein pocket information.
To improve the efficiency and success rate of identifying promising drug candidates.

Main Methods:

Developed DiffSMol, a genAI method utilizing a diffusion model and pre-trained shape embeddings.
Employed iterative shape guidance to refine generated 3D molecular structures.
Integrated protein pocket information for guiding molecule generation towards optimal binding affinities.
Evaluated performance on benchmark datasets against state-of-the-art methods.

Main Results:

DiffSMol with shape guidance achieved a 61.4% success rate in generating molecules resembling ligand shapes, significantly outperforming baselines (11.2%).
DiffSMol with pocket guidance improved binding affinities by 13.2% compared to the best baseline.
Combining shape and pocket guidance further enhanced binding affinities by 17.7%.
Generated molecules exhibited favorable physicochemical and pharmacokinetic properties in case studies.

Conclusions:

DiffSMol demonstrates superior performance in generating 3D binding molecules with desired shapes and affinities.
The method shows significant potential for accelerating drug discovery and developing promising drug candidates.
DiffSMol offers a powerful tool for computational chemistry and AI-driven drug development.