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Updated: Apr 12, 2026
A Bilingual Computational Workflow for Identifying Potential PLK1 Inhibitors in American Sign Language and English
Published on: April 3, 2026
Combining docking-based comparative intermolecular contacts analysis and k-nearest neighbor correlation for the
Nour Jamal Jaradat1, Mohammad A Khanfar, Maha Habash
1Faculty of Pharmacy, Zarqa University, Zarqa, Jordan.
Abstract:
Check point kinase 1 (Chk1) is an important protein in G2 phase checkpoint arrest required by cancer cells to maintain cell cycle and to prevent cell death. Therefore, Chk1 inhibitors should have potential as anti-cancer therapeutics. Docking-based comparative intermolecular contacts analysis (dbCICA) is a new three-dimensional quantitative structure activity relationship method that depends on the quality and number of contact points between docked ligands and binding pocket amino acid residues. In this presented work we implemented a novel combination of k-nearest neighbor/genetic function algorithm modeling coupled with dbCICA to select critical ligand-Chk1 contacts capable of explaining anti-Chk1 bioactivity among a long list of inhibitors. The finest set of contacts were translated into two valid pharmacophore hypotheses that were used as 3D search queries to screen the National Cancer Institute's structural database for new Chk1 inhibitors. Three potent Chk1 inhibitors were discovered with IC50 values ranging from 2.4 to 69.7 µM.
Insights
Checkpoint kinase 1 (Chk1) inhibitors show promise as anti-cancer drugs. This study identified novel Chk1 inhibitors using a computational approach combining docking analysis and machine learning to screen a large database.
Area of Science:
- Medicinal Chemistry
- Computational Biology
- Cancer Therapeutics
Background:
- Checkpoint kinase 1 (Chk1) is crucial for cancer cell survival by regulating the G2 phase cell cycle arrest.
- Inhibiting Chk1 presents a potential therapeutic strategy for cancer treatment.
Purpose of the Study:
- To identify novel Chk1 inhibitors using a novel computational method.
- To develop and validate pharmacophore models for Chk1 inhibitor discovery.
Main Methods:
- Implemented a novel combination of k-nearest neighbor/genetic function algorithm modeling with docking-based comparative intermolecular contacts analysis (dbCICA).
- Utilized two pharmacophore hypotheses derived from critical ligand-Chk1 contacts as 3D search queries.
- Screened the National Cancer Institute's structural database.
Main Results:
- Identified critical ligand-Chk1 contacts contributing to anti-Chk1 bioactivity.
- Generated two validated pharmacophore hypotheses.
- Discovered three potent Chk1 inhibitors with IC50 values ranging from 2.4 to 69.7 µM.
Conclusions:
- The novel computational approach effectively identified potent Chk1 inhibitors.
- The developed pharmacophore models are valuable for future Chk1 inhibitor discovery.
- Chk1 inhibitors hold significant potential as anti-cancer therapeutics.
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