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Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA
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ML-PLIC: a web platform for characterizing protein-ligand interactions and developing machine learning-based scoring
Xujun Zhang1, Chao Shen1,2, Tianyue Wang1
1Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China.
Briefings in Bioinformatics
|September 22, 2023
Summary
We developed ML-PLIC, a web platform for characterizing protein-ligand interactions (PLI) and generating machine learning-based scoring functions (MLSFs) for drug discovery via virtual screening.
Area of Science:
- Computational chemistry and cheminformatics
- Drug discovery and development
- Machine learning in bioinformatics
Background:
- Protein-ligand interactions (PLI) are crucial for structure-based drug design.
- Machine learning-based scoring functions (MLSFs) offer a promising approach to analyze PLI.
- Existing methods require robust platforms for automated PLI characterization and MLSF generation.
Purpose of the Study:
- To introduce ML-PLIC, a novel web platform for automated protein-ligand interaction (PLI) characterization.
- To enable the generation of machine learning-based scoring functions (MLSFs) for virtual screening (VS).
- To facilitate structure-based drug design by identifying potential protein binders.
Main Methods:
- ML-PLIC integrates five modules: Docking, Descriptors, Modeling, Screening, and Pipeline.
- The platform automates the generation of physical and biochemical representations of PLI.
- MLSFs are trained using these descriptors for subsequent VS.
- Validation was performed on benchmark datasets and a case study involving Serine/threonine-protein kinase WEE1.
Main Results:
- MLSFs generated by ML-PLIC demonstrated superior accuracy compared to traditional docking tools.
- The platform achieved competitive performance against deep learning-based scoring functions.
- A successful case study highlighted the utility of ML-PLIC in developing MLSFs for WEE1 kinase.
Conclusions:
- ML-PLIC provides a powerful, integrated platform for PLI characterization and MLSF generation.
- The platform enhances the design of structure-based virtual screening pipelines.
- ML-PLIC is freely available, promoting advancements in drug discovery and design.
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