An integrated machine learning and computational framework with experimental validation for the identification of
Mushtaq Ahmad Wani1, Pooja Kumari1, Faisal Irshad2
1Discovery Informatics Group, NPMC Division, CSIR-Indian Institute of Integrative Medicine, Jammu, 180001, India.
Abstract:
Chemokine receptor 4 (CXCR4) is a clinically significant G protein-coupled receptor implicated in HIV-1 entry, cancer progression, immune regulation, and metastatic dissemination, making it an attractive therapeutic target. This study employed an integrated computational and experimental framework to identify novel small-molecule CXCR4 inhibitors. A curated dataset of 608 compounds from peer-reviewed literature and patents was used to train machine-learning classification models. Decision Tree, Logistic Regression, and AdaBoost models showed balanced performance across key metrics, and external validation on 2146 in-house compounds identified 44 consensus CXCR4 inhibitors. Molecular docking analyses suggested favorable binding modes and key interactions comparable to those predicted for the reference inhibitor IT1t. One hundred-nanosecond molecular dynamics simulations indicated stable CXCR4-ligand complexes, with equilibration occurring within approximately 20 ns and backbone RMSD values maintained between 4 and 8 Å. MM/GBSA free-energy calculations demonstrated favorable energetics, with IS00622 exhibiting the strongest affinity (-70 kcal/mol), followed by IT1t, IS00998, and IS00179. In vitro assays identified IS00127 as a promising lead, showing strong antiproliferative activity against MDA-MB-231 cells and minimal toxicity toward HEK293 cells. ELISA assays confirmed dose-dependent CXCR4 downregulation with negligible effects on CXCR7, indicating high functional selectivity. Overall, this integrative strategy accelerates the discovery of potent, selective CXCR4 inhibitors for translational research.
Insights
Researchers discovered novel small-molecule inhibitors targeting chemokine receptor 4 (CXCR4), a key player in HIV and cancer. This integrated computational and experimental approach identified potent and selective compounds for future therapeutic development.
Area of Science:
- Medicinal Chemistry
- Computational Biology
- Pharmacology
Background:
- Chemokine receptor 4 (CXCR4) is a G protein-coupled receptor crucial in HIV-1 entry, cancer metastasis, and immune responses.
- Its significance makes CXCR4 an important target for developing novel therapeutics.
Purpose of the Study:
- To identify novel small-molecule inhibitors of CXCR4 using an integrated computational and experimental strategy.
- To validate the efficacy and selectivity of identified inhibitors for potential therapeutic applications.
Main Methods:
- Machine learning models (Decision Tree, Logistic Regression, AdaBoost) were trained on a dataset of 608 compounds.
- Molecular docking, molecular dynamics simulations, and MM/GBSA calculations were performed to assess binding modes and affinities.
- In vitro assays, including antiproliferative and ELISA assays, were used to evaluate lead compounds' activity and selectivity.
Main Results:
- Machine learning models identified 44 consensus CXCR4 inhibitors from an in-house dataset.
- Molecular simulations confirmed stable CXCR4-ligand complexes with favorable binding energetics.
- IS00622 showed the highest binding affinity, while IS00127 demonstrated potent antiproliferative activity and functional selectivity for CXCR4 over CXCR7 in vitro.
Conclusions:
- The integrated computational and experimental approach successfully identified potent and selective CXCR4 inhibitors.
- IS00127 emerged as a promising lead compound for further translational research in CXCR4-related diseases.
- This strategy accelerates the discovery pipeline for CXCR4-targeted therapeutics.
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