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Related Experiment Video

Updated: May 15, 2025

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

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Published on: February 23, 2024

Protein-Ligand Structure and Affinity Prediction in CASP16 Using a Geometric Deep Learning Ensemble and Flow

Alex Morehead¹, Jian Liu¹, Pawan Neupane¹

¹Department of Electrical Engineering & Computer Science, NextGen Precision Health, University of Missouri, Columbia, Missouri, USA.

|April 8, 2025

Summary

We developed MULTICOM_ligand, a deep learning tool for predicting protein-ligand structures and binding affinities. It excels at ranking poses and estimating binding, proving effective in drug discovery assessments.

Keywords:

binding affinity deep learning diffusion model drug discovery flow matching pose prediction protein‐ligand structure

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Area of Science:

Computational biology
Structural bioinformatics
Drug discovery

Background:

Accurate prediction of protein-ligand interactions is crucial for drug discovery.
Current deep learning methods face challenges in pose ranking and binding affinity estimation.
Robust benchmarking is needed to validate prediction models.

Purpose of the Study:

To introduce MULTICOM_ligand, an ensemble deep learning system for protein-ligand structure and binding affinity prediction.
To improve unsupervised pose ranking using structural consensus.
To develop a novel deep generative flow matching model for joint prediction.

Main Methods:

Developed a deep learning-based ensemble approach (MULTICOM_ligand).
Implemented structural consensus ranking for pose selection.
Introduced a deep generative flow matching model for integrated prediction.

Main Results:

MULTICOM_ligand achieved top-5 performance in protein-ligand structure prediction at CASP16.
The system also ranked in the top-5 for binding affinity prediction at CASP16.
Demonstrated efficacy in real-world drug discovery scenarios.

Conclusions:

MULTICOM_ligand offers a robust solution for protein-ligand structure and binding affinity prediction.
The system's performance in CASP16 validates its utility for drug discovery.
The freely available source code facilitates further research and application.