Computational Innovations in Cancer Research and How Computing is Transforming Drug Discovery and Development: A...

Area of Science:

• Oncology
• Computational Chemistry
• Bioinformatics

Background:

• Cancer remains a leading cause of mortality globally due to its complexity and treatment resistance.
• Traditional experimental methods for cancer research are often slow, expensive, and lack predictive accuracy.

Purpose of the Study:

• To review the impact of Computer-Aided Drug Design (CADD) and related computational approaches in advancing cancer research and therapy.
• To highlight how CADD accelerates drug discovery, target identification, and compound optimization for anticancer agents.

Main Methods:

• Literature review of studies on CADD in cancer research.
• Focus on CADD applications in drug discovery, target identification, and compound optimization.
• Examination of computational innovations like AI, ML, and quantum computing in cancer therapy development.

Main Results:

• CADD and computational methods significantly enhance cancer drug discovery, accelerating target identification and lead optimization.
• AI/ML models, molecular docking, dynamics, QSAR, and virtual screening improve predictions of efficacy, toxicity, and resistance.
• Integration of multi-omics data aids biomarker discovery and patient stratification, with case studies showing reduced development times and improved selectivity for targets like EGFR, PARP, and KRAS G12C.

Conclusions:

• CADD is revolutionizing cancer research by speeding up drug discovery and designing more selective anticancer agents.
• Computational approaches, including AI, ML, and quantum computing, enable rational drug design and predictive modeling.
• Integrating genomics, proteomics, imaging, and computational methods is crucial for developing more effective, personalized cancer therapies.