Exploiting the Synergy between Computational and Experimental Biophysics for Efficient Cancer Drug Development

Abstract

[Abstract]: Targeted cancer therapies have revolutionized oncology by developing treatments that specifically target cancer cells, reducing side effects. However, traditional drug discovery approaches are often hindered by high costs, long timelines, and low success rates. To address these challenges, the combination of computational and experimental biophysical techniques has become a highly effective approach. Molecular modeling methods, such as docking, molecular dynamics simulations, and virtual screening, enable in silico identification and optimization of drug candidates, while experimental biophysical techniques like NMR, SPR, and BLI validate molecular structures, binding interactions and affinities. This combined approach enhances the precision and efficiency of drug discovery, enabling progress in targeting oncogenic mutations, disrupting protein-protein interactions, and advancing drug repurposing efforts. Despite its potential, several challenges remain, including predictive limitations in computational models, experimental reproducibility, and the complexity of integrating diverse datasets. Future advances, particularly in artificial intelligence-driven methodologies, high-throughput screening, and drug repurposing, hold great potential to accelerate the development of innovative and effective cancer therapies.