Exploiting the synergy between computational and experimental biophysics for efficient cancer drug development
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.
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BibTeX
@article{alvarezmena2026exploiting,
title = {{Exploiting the synergy between computational and experimental biophysics for efficient cancer drug development}},
author = {Ana Álvarez-Mena and Mélanie Berbon and Carmelo Di Primo and Rebeca Garcia-Fandino and Ángel Piñeiro and Carlos Fernandez-Lozano and Hugo A.L. Filipe and Birgit Habenstein},
journal = {European Journal of Medicinal Chemistry Reports},
year = {2026},
month = nov,
doi = {10.1016/j.ejmcr.2025.100320},
url = {https://hal.science/hal-05848978v1},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Exploiting the synergy between computational and experimental biophysics for efficient cancer drug development
AU - Ana Álvarez-Mena
AU - Mélanie Berbon
AU - Carmelo Di Primo
AU - Rebeca Garcia-Fandino
AU - Ángel Piñeiro
AU - Carlos Fernandez-Lozano
AU - Hugo A.L. Filipe
AU - Birgit Habenstein
PY - 2026
DA - 2026/11/02
JO - European Journal of Medicinal Chemistry Reports
DO - 10.1016/j.ejmcr.2025.100320
UR - https://hal.science/hal-05848978v1
AB - 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.
SN - 2772-4174
ER -