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Updated: Jan 4, 2026

Kinase Inhibitor Screening In Self-assembled Human Protein Microarrays
Published on: October 23, 2019
Density Functional Theory and Molecular Simulation Studies for Prioritizing Anaplastic Lymphoma Kinase Inhibitors
Nivya James1, V Shanthi1, K Ramanathan2
1Department of Biotechnology, School of Bio Sciences and Technology, Vellore Institute of Technology, Vellore, Tamil Nadu, 632014, India.
Abstract:
Targeting anaplastic lymphoma kinase (ALK) is one of the important treatment strategies for the treatment of non-small cell lung cancer (NSCLC). In the present perspective, multidimensional approaches were used for the identification of ALK inhibitors. Initially, an e-pharmacophore model was generated using the PHASE algorithm and was used as a 3D query to screen 468,200 molecules of ASINEX database. Prior to the screening process, the model was evaluated for its significance and the ability to differentiate actives from inactives, using enrichment analysis. Subsequently, the hierarchical docking protocol and binding free energy calculations were instigated using GLIDE algorithm and Prime module, respectively. Further, the pharmacokinetic/pharmacodynamics (PK/PD) properties and toxicities of the hit compounds were envisaged respectively using QikProp program, Osiris explorer, and Protox-II algorithm. These approaches retrieved two hits namely BAS 00137817 and BAS 00680055 with acceptable absorption, distribution, metabolism, excretion and toxicity (ADMET) properties and higher affinity towards ALK protein. Additionally, density functional theory calculations and molecular dynamics simulations were performed to validate the inhibitory activity of the lead compounds. It is noteworthy to mention that all the hits constitute of particular scaffolds which play a major role in the downregulation of some ALK-positive lung cancer pathways. We speculate that the outcomes of this research are of substantial prominence in the rational designing of novel and efficacious ALK inhibitors.
Insights
Researchers identified novel anaplastic lymphoma kinase (ALK) inhibitors for non-small cell lung cancer (NSCLC) treatment. Computational methods screened databases, yielding two promising compounds with favorable ADMET properties and high ALK affinity.
Area of Science:
- Computational chemistry
- Drug discovery
- Oncology
Background:
- Targeting anaplastic lymphoma kinase (ALK) is a key strategy in non-small cell lung cancer (NSCLC) therapy.
- Developing novel ALK inhibitors requires advanced computational screening and validation methods.
Purpose of the Study:
- To identify novel ALK inhibitors using a multidimensional computational approach.
- To evaluate the potential efficacy and safety of identified compounds for NSCLC treatment.
Main Methods:
- Generation of an e-pharmacophore model and screening of the ASINEX database.
- Hierarchical docking, binding free energy calculations, and ADMET property prediction.
- Density functional theory and molecular dynamics simulations for validation.
Main Results:
- Two lead compounds, BAS 00137817 and BAS 00680055, were identified with high affinity for ALK.
- These compounds demonstrated acceptable absorption, distribution, metabolism, excretion, and toxicity (ADMET) profiles.
- Identified scaffolds are crucial for downregulating ALK-positive lung cancer pathways.
Conclusions:
- The study successfully identified novel ALK inhibitors with potential for NSCLC treatment.
- The findings support the rational design of new, effective ALK inhibitors.
- Computational approaches are valuable for accelerating drug discovery in oncology.
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