Unlocking CDK2: Quantum Chemistry Meets Machine Learning!
Cyclin-dependent kinase 2 (CDK2) plays a central role in controlling the cell cycle, and its dysregulation has been directly linked to cancer progression. Targeting CDK2 with novel inhibitors presents a promising therapeutic strategy in oncology. This study presents a cutting-edge computational pipeline that combines machine learning, molecular docking, pharmacological filtering, and quantum chemistry techniques to accelerate the discovery of potent CDK2 inhibitors. A total of 477,975 molecules were screened, resulting in the identification of three lead compounds with strong inhibitory potential and stable interactions within the CDK2 binding site.
Machine Learning-Based Screening of CDK2 Inhibitors
To effectively narrow down potential CDK2 inhibitors, a Random Forest (RF) classification model was trained and optimized. Statistical assessments revealed that the RF model outperformed other classifiers, showing superior predictive power. This model was applied to the vast COCONUT natural product database, reducing nearly half a million compounds to just 327 promising candidates. This demonstrates how machine learning can revolutionize early-stage drug discovery by streamlining the virtual screening process while maintaining precision and scalability.
Eliminating False Positives: PAINS Filtration
After machine learning-based prediction, the PAINS (Pan-Assay Interference Compounds) filter was applied to eliminate structurally problematic compounds known to generate false positives. From the initial 327 molecules, 309 passed the PAINS screening, ensuring the chemical validity and biological relevance of the candidates. This crucial step prevented the advancement of deceptive hits and reinforced the robustness of the screening workflow, maintaining the integrity of the lead identification phase.
Molecular Docking and Pharmacokinetic Evaluation
The 309 filtered molecules were subjected to molecular docking against the CDK2 active site. Docking results were ranked based on binding scores, and the top 40 molecules were selected for pharmacokinetics (PK) and pharmacodynamics (PD) profiling, including ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) parameters. This step ensured that the selected molecules not only bind effectively to CDK2 but also possess drug-like properties, minimizing late-stage failures in drug development.
DFT and Molecular Dynamics Simulation Studies
The final three compounds that passed PK/PD filters underwent Density Functional Theory (DFT) calculations and Molecular Dynamics (MD) simulations to analyze their structural stability and interaction profiles. Notably, these molecules consistently engaged key residues Lys33 and Asp145, essential for CDK2 inhibition. Molecule 2, in particular, featured a fused heterocyclic system, which contributed to a more stable and extended interaction, suggesting enhanced inhibitory efficacy. MD simulations confirmed the compounds' stability within the CDK2 binding pocket over time.
Development of the pCDK2i_v1.0 Screening Tool
To support ongoing CDK2-targeted drug discovery efforts, the research team developed pCDK2i_v1.0, an open-access web tool that allows users to predict the activity of compounds against CDK2. Built upon the validated RF model and extensive computational analyses, this tool provides an efficient, user-friendly platform for the scientific community to virtually screen and prioritize potential inhibitors. The integration of this tool highlights the growing impact of AI-driven resources in precision oncology and rational drug design.
Technology Scientists Awards
#MachineLearning
#DrugDiscovery
#OncologyResearch
#MolecularDocking
#PAINSFilter
#RandomForest
#DFTCalculations
#MDSimulations
#QuantumChemistry
#CancerTherapy
#Pharmacokinetics
#Pharmacodynamics
#NaturalProducts
#COCONUTDatabase
#Cheminformatics
#PrecisionMedicine
#OpenAccessTools
#Bioinformatics
#TargetedTherapies
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