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Updated: Jul 19, 2025

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

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

Published on: June 20, 2025

PLANTAIN: Diffusion-inspired Pose Score Minimization for Fast and Accurate Molecular Docking.

Michael Brocidiacono¹, Konstantin I Popov¹, David Ryan Koes²

¹Eshelman School of Pharmacy, University of North Carolina at Chapel Hill.

|August 7, 2023

Summary

PLANTAIN, a novel method, combines molecular docking and diffusion models for faster, accurate prediction of small molecule poses in protein binding sites. This approach significantly enhances virtual screening efficiency.

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Area of Science:

Computational chemistry
Structural biology
Drug discovery

Background:

Molecular docking predicts small molecule 3D poses within protein binding sites.
Traditional methods rely on physics-based scoring functions for pose prediction.
Recent diffusion models iteratively refine ligand poses.

Approach:

PLANTAIN combines traditional docking with diffusion model principles.
A neural network develops a fast pose scoring function.
L-BFGS minimization optimizes ligand poses from random starting points.

Key Points:

PLANTAIN achieves state-of-the-art performance in pose prediction.
The method is ten times faster than the next-best approach.
A novel diffusion-inspired pose scoring function is introduced.

Conclusions:

PLANTAIN offers a significant advancement in molecular docking speed and accuracy.
The method is publicly released to benefit virtual screening applications.
PLANTAIN is expected to enhance the utility and throughput of drug discovery pipelines.