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Related Concept Videos

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Updated: Jan 15, 2026

Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis
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D2Screen: Embedding Pretrained Representation Learning Model and Molecular Docking for Virtual Screening.

Tingli Qian1, Jiao Zhou1,2, Xiang Liu3,4

  • 1Ciechanover Institute of Precision and Regenerative Medicine, School of Medicine, The Chinese University of Hong Kong, Shenzhen 518172, China.

ACS Medicinal Chemistry Letters
|October 15, 2025
PubMed
Summary
This summary is machine-generated.

A new D2Screen pipeline combines deep learning and molecular docking for drug discovery. This approach successfully identified novel SARS-CoV-2 inhibitors effective against drug-resistant mutations.

Keywords:
SARS-CoV-2antidrug resistancedeep learningmolecular dockingthe main proteasevirtual screening

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Area of Science:

  • Computational chemistry and drug discovery
  • Artificial intelligence in pharmacology
  • Virology and infectious diseases

Background:

  • Virtual screening and molecular docking are key computational methods in drug discovery.
  • Deep learning offers a powerful approach to identify bioactive compounds by leveraging existing pharmacological data.
  • Drug resistance in viral infections, such as COVID-19, necessitates the development of novel therapeutic strategies.

Purpose of the Study:

  • To develop and validate a novel computational pipeline, D2Screen (Deep learning and Docking-based Screening), integrating deep learning and molecular docking.
  • To enhance the accuracy and efficiency of compound screening compared to traditional methods.
  • To discover novel inhibitors targeting SARS-CoV-2 main protease (Mpro), including those effective against drug-resistant variants.

Main Methods:

  • Development of an end-to-end pipeline, D2Screen, combining deep learning algorithms with molecular docking simulations.
  • Evaluation of D2Screen's performance using BedROC and EF1% metrics, comparing it against standalone deep learning and molecular docking approaches.
  • Application of D2Screen in a case study to identify inhibitors against SARS-CoV-2 Mpro, focusing on efficacy against drug-resistant mutations.

Main Results:

  • D2Screen demonstrated superior accuracy over individual deep learning or molecular docking methods, evidenced by improved BedROC and EF1% metrics.
  • The pipeline successfully identified a series of noncovalent inhibitors against SARS-CoV-2 Mpro, with the most potent compound exhibiting an IC50 of 5.9 μM.
  • The lead inhibitor displayed significantly reduced susceptibility to drug-resistant Mpro mutations (T21I-E166V), showing a 7.6-fold reduction in efficacy compared to over 1000-fold reduction for Nirmatrelvir.

Conclusions:

  • The D2Screen pipeline represents a significant advancement in computational drug discovery, effectively integrating deep learning and molecular docking.
  • This hybrid approach enhances prediction accuracy and facilitates the identification of potent drug candidates.
  • D2Screen successfully discovered a promising noncovalent inhibitor against SARS-CoV-2 Mpro with notable activity against drug-resistant strains, offering potential for new COVID-19 treatments.