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Improving Docking-Based Virtual Screening Ability by Integrating Multiple Energy Auxiliary Terms from Molecular

Wen-Ling Ye1, Chao Shen2, Guo-Li Xiong1

  • 1Xiangya School of Pharmaceutical Sciences, Central South University, Changsha 410003, P. R. China.

Journal of Chemical Information and Modeling
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Summary

A new method, EAT-Score, improves virtual screening (VS) for drug discovery by using energy auxiliary terms (EAT) with machine learning. This approach significantly enhances the accuracy of identifying potential drug compounds compared to traditional scoring functions.

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Area of Science:

  • Computational chemistry
  • Drug discovery
  • Bioinformatics

Background:

  • Virtual screening (VS) using molecular docking is crucial for identifying novel drug candidates.
  • Current docking scoring functions (SFs) often lack sufficient accuracy, hindering effective drug discovery.
  • Improving the predictive power of SFs is essential for efficient hit compound retrieval.

Purpose of the Study:

  • To develop a novel scoring function, EAT-Score, to enhance the accuracy of virtual screening.
  • To leverage energy auxiliary terms (EAT) from molecular docking and machine learning for improved predictions.
  • To validate the performance of EAT-Score against classical and state-of-the-art VS methods.

Main Methods:

  • Proposed EAT-Score by integrating energy auxiliary terms (EAT) from Molecular Operating Environment (MOE) scoring and Protein-Ligand Interaction Fingerprint (PLIF) features.
  • Utilized eXtreme Gradient Boosting (XGBoost), a machine learning algorithm, to build the EAT-Score model.
  • Strictly validated EAT-Score performance on the DUD-E diverse subset using various performance metrics, including AUC, LogAUC, and BEDROC.

Main Results:

  • EAT-Score demonstrated significantly improved performance in discriminating active compounds from decoys compared to classical SFs, with AUC values improving by approximately 0.3.
  • EAT-Score achieved comparable or superior prediction performance relative to other state-of-the-art VS methods, including machine learning-based SFs.
  • Shapley additive explanations (SHAP) analysis revealed that EAT-Score effectively captures critical binding pattern information.

Conclusions:

  • EAT-Score represents a significant advancement in virtual screening accuracy for drug discovery.
  • The model's ability to interpret binding mechanisms offers valuable insights for guiding drug design.
  • EAT-Score provides a powerful and interpretable tool for identifying novel hit compounds efficiently.