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Updated: May 9, 2025

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SPLIF-Enhanced Attention-Driven 3D CNNs for Precise and Reliable Protein-Ligand Interaction Modeling for METTL3.

Muhammad Junaid1,2, Muhammad Zeeshan3, Abbas Khan4

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Summary

DeepMETTL3, a novel scoring function, enhances structure-based virtual screening (SBVS) by integrating deep learning with protein-ligand interaction fingerprints. This AI-driven approach improves drug discovery accuracy and efficiency.

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Area of Science:

  • Computational chemistry
  • Artificial intelligence in drug discovery
  • Structural biology

Background:

  • Conventional scoring functions in structure-based virtual screening (SBVS) struggle to accurately model complex protein-ligand interactions.
  • There is a need for more accurate and robust scoring functions to improve the efficiency of drug discovery pipelines.

Purpose of the Study:

  • To develop and validate DeepMETTL3, a novel deep learning-based scoring function for enhanced SBVS.
  • To assess the performance of DeepMETTL3 against traditional scoring functions using METTL3 as a therapeutic target.

Main Methods:

  • Integration of 3D convolutional neural networks (CNNs), multihead attention mechanisms, and high-dimensional Structural Protein-Ligand Interaction Fingerprints (SPLIF).
  • Scaffold-based data-splitting strategy and validation on multiple test sets, including those with low chemical similarity to training data.
  • Investigated the impact of active-to-decoy ratio and attention mechanism placement on model performance.

Main Results:

  • DeepMETTL3 significantly outperforms traditional scoring functions in accuracy, robustness, and scalability for SBVS.
  • An active-to-decoy ratio of 1:50 in the training set and placing the attention mechanism after CNN1 improved model generalization.
  • Demonstrated superior performance in classifying active and inactive compounds for the METTL3 target.

Conclusions:

  • DeepMETTL3 represents a significant advancement in target-specific machine learning for SBVS, offering improved predictive power.
  • The developed framework is adaptable to other biological targets, highlighting the potential of deep learning in AI-based drug design.
  • DeepMETTL3 balances computational efficiency with predictive accuracy, advancing molecular docking and virtual screening capabilities.