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Proteins are dynamic macromolecules that carry out a wide variety of essential processes; however, the activities of most proteins depend on their interactions with other molecules or ions, known as ligands.
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Updated: Mar 16, 2026

Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA

Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA

Published on: February 23, 2024

Molecular embedding-based algorithm selection in protein-ligand docking.

Jiabao Brad Wang¹, Siyuan Cao¹, Hongxuan Wu¹

¹Division of Natural and Applied Sciences, Duke Kunshan University, 8 Duke Av., Suzhou, 215316, Jiangsu, China.

Journal of Cheminformatics

|March 15, 2026

Summary

MolAS, a new algorithm-selection model, improves molecular docking performance by predicting per-algorithm effectiveness. It offers significant gains over the single best solver (SBS) and helps close the gap to the virtual best solver (VBS).

Keywords:

Algorithm selection Cheminformatics Docking benchmarks Molecular embeddings Pose evaluation Protein-ligand docking

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Area of Science:

Computational chemistry
Drug discovery
Machine learning in bioinformatics

Background:

Molecular docking algorithm selection is challenging due to context-dependent performance.
No single algorithm reliably performs across all structural, chemical, and protocol variations.
Existing methods often lack adaptability and generalizability.

Purpose of the Study:

To introduce MolAS, a lightweight algorithm-selection model for molecular docking.
To predict per-algorithm performance using pretrained protein and ligand embeddings.
To improve upon the single best solver (SBS) and approach the virtual best solver (VBS) performance.

Main Methods:

Utilized pretrained protein and ligand embeddings.
Employed attentional pooling and a shallow residual decoder for performance prediction.
Evaluated MolAS across five molecular docking benchmarks with hundreds to thousands of labeled complexes.

Main Results:

MolAS achieved up to a 15 percentage-point absolute improvement over the single best solver (SBS).
The model closed 17-66% of the gap between the virtual best solver (VBS) and SBS.
Performance was most effective with low oracle entropy and separable top-solver regions, but degraded under protocol mismatch.

Conclusions:

MolAS offers a robust, embedding-based approach to docking algorithm selection.
Its effectiveness is linked to oracle landscape characteristics and protocol stability.
The model serves as an in-domain selector and a diagnostic tool for assessing selection well-posedness.