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Computational techniques to study breast cancer scaffolds for antiangiogenesis: a review
Robin Singh1, Neha Gupta2, Raj Luxmi1
1Guru Jambheshwar University of Science & Technology, Hisar, Haryana, 125001, India.
Context:
Breast cancer remains one of the leading causes of cancer-related mortality among women worldwide. Tumor angiogenesis plays a crucial role in breast cancer progression, making angiogenesis-associated pathways attractive therapeutic targets. Computational drug discovery approaches, including virtual high-throughput screening (VHTS), molecular docking, molecular dynamics simulations, and binding free energy calculations, have emerged as valuable tools for identifying and optimizing anticancer compounds. This review evaluates the application of these computational techniques in the discovery of antiangiogenic therapeutic candidates for breast cancer.
Methods:
A systematic literature search was conducted across Google Scholar, PubMed, ScienceDirect, and The Cancer Genome Atlas (TCGA). Following screening and eligibility assessment according to predefined inclusion criteria, 38 studies published between 2015 and 2025 were included in the qualitative synthesis. The selected studies investigated computational approaches targeting angiogenesis-related pathways, including VEGFR-2, STAT3, HER2, PI3K/Akt, and SphK1 signaling.
Results And Conclusions:
The reviewed studies demonstrated that VHTS, molecular docking, ADMET prediction, molecular dynamics simulations, MM-GBSA/MM-PBSA analyses, and QSAR modeling effectively facilitate hit identification and lead optimization. Several compounds, including triazolopyrazine derivatives, curcumin, liquiritin, and novel STAT3 inhibitors, exhibited promising antiangiogenic activity and favorable binding characteristics, highlighting the potential of computational approaches in advancing breast cancer drug discovery.
Insights
Computational methods like virtual screening and molecular docking accelerate the discovery of novel antiangiogenic compounds for breast cancer treatment. These techniques identify promising drug candidates targeting key tumor growth pathways.
Area of Science:
- * Computational chemistry and bioinformatics.
- * Drug discovery and development.
- * Cancer research, specifically breast cancer therapeutics.
Background:
- * Breast cancer is a major cause of female mortality globally.
- * Tumor angiogenesis is critical for breast cancer progression, presenting a therapeutic target.
- * Computational drug discovery offers powerful tools for identifying and optimizing anticancer agents.
Purpose of the Study:
- * To review the application of computational techniques in discovering antiangiogenic breast cancer drugs.
- * To evaluate methods like virtual high-throughput screening (VHTS) and molecular docking.
- * To assess the role of these methods in targeting angiogenesis-related pathways.
Main Methods:
- * Systematic literature search of databases (Google Scholar, PubMed, ScienceDirect, TCGA).
- * Qualitative synthesis of 38 studies (2015-2025) on computational approaches.
- * Focus on computational targeting of VEGFR-2, STAT3, HER2, PI3K/Akt, and SphK1 signaling.
Main Results:
- * VHTS, molecular docking, ADMET prediction, molecular dynamics, MM-GBSA/MM-PBSA, and QSAR modeling aid hit identification and lead optimization.
- * Promising antiangiogenic compounds identified include triazolopyrazine derivatives, curcumin, liquiritin, and novel STAT3 inhibitors.
- * These compounds showed favorable binding characteristics and antiangiogenic potential.
Conclusions:
- * Computational approaches are effective in identifying and optimizing antiangiogenic breast cancer drug candidates.
- * Identified compounds demonstrate significant potential for advancing breast cancer therapy.
- * The integration of computational methods is crucial for future drug discovery in oncology.
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