AI in healthcare<\/a> could be instrumental in improving immunotherapies, such as pembrolizumab (Keytruda), that are already revolutionising cancer treatment.<\/p>\nMapping the future of personalised cancer treatments<\/h3>\n One of the greatest challenges in personalised cancer therapeutics is accurately predicting where a drug can bind to its target protein. In this case, the researchers focused on PD-L1, a checkpoint protein that cancers exploit to suppress the immune system.<\/p>\n
Some modern drugs unleash the immune system to attack tumours by blocking PD-L1. However, understanding where exactly to target PD-L1 with new treatments has been a longstanding problem.<\/p>\n
The team developed a sophisticated method that combines AlphaFold2-based AI tools with molecular dynamics simulations and dynamic network analysis.<\/p>\n
Their approach allowed them to predict and confirm key binding regions in the PD-L1 protein that are critical for drug interaction.<\/p>\n
The computational approach was validated with cutting-edge experimental techniques, including cross-linking mass spectrometry and next-generation sequencing.<\/p>\n
These experiments confirmed the accuracy of the team\u2019s predictions, demonstrating the power of combining computational models with experimental validation to unravel complex protein-protein interactions and create personalised cancer treatments.<\/p>\n
The impact of protein interactions on future drug discovery<\/h3>\n The implications of this study go far beyond PD-L1. The methods developed can be applied to many other proteins, potentially leading to the discovery of new drug targets for various diseases, including other types of cancer and autoimmune conditions.<\/p>\n
Additionally, this research paves the way for more cost-effective and rapid development of cancer therapeutics, an area where traditional experimental methods can be slow and expensive.<\/p>\n
\u201cThis research stresses the potential of computational tools like NAMD and VMD, combined with cutting-edge hardware such as NVIDIA DGX systems, to advance cancer therapeutics. Our findings mark a significant step toward developing new, targeted treatments for cancer,\u201d explained Dr Diego Gomes, lead author of the work and a researcher at Auburn.<\/p>\n","protected":false},"excerpt":{"rendered":"
Researchers are leveraging AI and molecular simulations to uncover new pathways for personalised cancer treatments.<\/p>\n","protected":false},"author":22,"featured_media":51029,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"footnotes":""},"categories":[10551],"tags":[24493,849],"acf":[],"yoast_head":"\n
AI unlocks new path to personalised cancer treatments<\/title>\n \n \n \n \n \n \n \n \n \n \n \n \n\t \n\t \n\t \n \n \n \n \n \n\t \n\t \n\t \n