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Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion

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Accelerating Inference in Molecular Diffusion Models with Latent Representations of Protein Structure.

Ian Dunn¹, David Ryan Koes¹

¹Dept. of Computational & Systems Biology, University of Pittsburgh, Pittsburgh, PA 15260.

|May 20, 2024

Summary

This study introduces a new graph neural network (GNN) architecture for diffusion models, improving molecular structure generation for drug design. The novel approach enhances efficiency without sacrificing the quality of generated protein-ligand structures.

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Area of Science:

Structural biology
Structure-based drug design
Computational chemistry

Background:

Diffusion generative models are powerful for molecular modeling but face challenges with computational scaling.
Existing models often use coarse-grained protein representations, losing crucial interaction details and reducing generated structure quality.
Graph neural networks (GNNs) struggle with large molecular graphs, impacting diffusion model feasibility.

Conclusions:

The developed GNN architecture offers an efficient and effective solution for diffusion-based molecular modeling.
This method enhances *de novo* ligand design by balancing computational feasibility with structural accuracy.
Future work can explore broader applications in structural biology and drug discovery.