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Molecular Models

Molecular Models

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: Sep 13, 2025

Curation of Computational Chemical Libraries Demonstrated with Alpha-Amino Acids

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Benchmarking 3D Structure-Based Molecule Generators.

Natasha Sanjrani^1,2, Damien E Coupry¹, Peter Pogány¹

¹Department of Cheminformatics, Research Technologies, GSK, Gunnels Wood Road, Stevenage SG1 2NY, U.K.

Journal of Chemical Information and Modeling

|July 25, 2025

Summary

This summary is machine-generated.

Deep learning generators struggle with structural validity, while combinatorial methods are slow. A new benchmark highlights areas for improvement in structure-based drug design generators.

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Area of Science:

* Computational chemistry and cheminformatics.
* Artificial intelligence in drug discovery.
* Molecular modeling and simulation.

Background:

* Evaluating generative models for drug design is crucial.
* Existing methods for generating 3D molecules have limitations.
* Protein-ligand interactions and conformations are key drug design targets.

Purpose of the Study:

* To benchmark 3D combinatorial and deep learning generators.
* To assess their ability to recreate protein-ligand interactions and conformations.
* To identify strengths and weaknesses of different generative approaches.

Main Methods:

* Developed a novel benchmark using the BindingMOAD dataset.
* Evaluated sequential graph neural networks (Pocket2Mol, PocketFlow), diffusion models (DiffSBDD, MolSnapper), and genetic algorithms (AutoGrow4, LigBuilderV3).
* Assessed structural validity, 3D ligand conformations, and interaction recreation.

Main Results:

* Deep learning models failed to generate structurally valid molecules and conformations.
* Combinatorial methods were slow and produced molecules failing 2D filters.
* Identified specific limitations of both deep learning and combinatorial approaches.

Conclusions:

* Deep learning generators need improved focus on structural validity and interaction accuracy.
* Combinatorial generators require optimization for speed and filter compliance.
* The benchmark provides a framework for advancing structure-based drug design generators.