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Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis

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Flowr.root - A flow matching based foundation model for joint multi-purpose structure-aware 3D ligand generation and

Julian Cremer¹, Tuan Le¹, Mohammad M Ghahremanpour²

¹Machine Learning & Computational Sciences, Pfizer Worldwide R&D, Berlin, Germany.

|November 19, 2025

Summary

Flowr.root is a new AI model for 3D ligand generation and binding affinity prediction. It creates realistic molecules and accurately estimates their binding strength, accelerating drug discovery.

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Area of Science:

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

Background:

Drug discovery relies on generating novel molecules with desired properties.
Predicting binding affinity is crucial for identifying effective drug candidates.
Existing methods often lack accuracy or efficiency in molecular generation and affinity prediction.

Purpose of the Study:

To introduce Flowr.root, an SE(3)-equivariant flow-matching model for pocket-aware 3D ligand generation.
To enable joint binding affinity prediction and confidence estimation.
To support multiple molecular design modes and multi-endpoint affinity predictions.

Main Methods:

Utilized SE(3)-equivariant flow-matching for 3D ligand generation.
Trained on large-scale ligand libraries and mixed-fidelity protein-ligand complexes.
Refined models on curated co-crystal datasets and employed parameter-efficient finetuning.
Integrated an affinity prediction module for enhanced accuracy.

Main Results:

Achieved state-of-the-art performance in unconditional and pocket-conditional 3D ligand generation.
Demonstrated superior accuracy in binding affinity prediction on benchmark datasets.
Showcased computational efficiency and geometric realism in generated structures.
Validated through case studies with significant correlation between predicted and experimental binding energies.

Conclusions:

Flowr.root provides a unified framework for structure-based drug design.
The model accelerates drug discovery from hit identification to lead optimization.
Continuous finetuning enables adaptation to project-specific structure-activity landscapes.
Joint generation and affinity prediction capabilities steer molecular design effectively.