Collaborative Approach between Explainable Artificial Intelligence and Simplified Chemical Interactions to Explore
Tomomi Shimazaki1, Masanori Tachikawa2
1Graduate School of Nanobioscience, Yokohama City University, 22-2 Seto, Yokohama, Kanagawa 236-0027, Japan.
This study combines explainable artificial intelligence (AI) with simplified chemical scores to enhance virtual screening for drug discovery. The approach efficiently identifies active drug ligands for cancer target protein cyclin-dependent kinase 2 (CDK2).
Area of Science:
- Computational chemistry
- Drug discovery
- Artificial intelligence in medicine
Background:
- Virtual screening is crucial for identifying potential drug candidates.
- Traditional docking simulations struggle to differentiate active ligands from decoys.
- Cyclin-dependent kinase 2 (CDK2) is a significant target protein in cancer therapy.
Purpose of the Study:
- To develop an improved virtual screening method for drug discovery.
- To enhance the efficiency and accuracy of identifying active ligands.
- To apply explainable AI (AI) and simplified scoring functions to target CDK2.
Main Methods:
- Employing machine learning models integrated with simplified scoring functions.
- Utilizing simplified Coulomb and Lennard-Jones interaction scores.
- Applying explainable AI to analyze ligand-receptor interactions.
Main Results:
- Simplified interaction scores significantly improved machine learning model classification.
- The combined approach demonstrated enhanced ability to identify active ligands.
- Explainable AI successfully highlighted key CDK2 residues involved in ligand recognition.
Conclusions:
- The collaborative approach of explainable AI and simplified scoring functions is effective for virtual screening.
- This method offers a more efficient and accurate way to discover drug candidates.
- The findings provide insights into CDK2-ligand interactions for cancer drug development.
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