Active and machine learning-enhanced discovery of new FGFR3 inhibitor, Rhapontin, through virtual screening of receptor structures and anti-cancer activity assessment - Zeng, Qingxin; Hu, Haichuan; Huang, Zhengwei; Guo, Aotian; Lu, Sheng; Tong, Wenbin; Zhang, Zhongheng; Shen, Tao (Frontiers in Molecular Biosciences, 2024)
Bridging tradition and innovation, this study explores the potential of natural compounds to enhance the efficacy of Ceritinib against Non-Small Cell Lung Cancer (NSCLC) while reducing its toxicities. Combining computational approaches, simulations, and experimental validation, we delve into the world of traditional remedies and modern science.Machine learning identifies PD173074, Isoquercitrin, and Rhapontin as promising FGFR3 inhibitors, marrying ancient knowledge with cutting-edge analysis.Quantitative Structure-Activity Relationship (QSAR) modeling bolsters these findings, interweaving the threads of heritage and modernity.Molecular dynamics simulations shed light on Rhapontin's stability and interactions, sketching a blueprint of its potential. Physicochemical assessments confirm its drug-like attributes and specificity for FGFR3, harmonizing ancient practices with contemporary drug discovery standards.Beyond molecules, we explore the intricate dance of ligandreceptor interactions, melding age-old wisdom with scientific precision.Rhapontin takes the spotlight, targeting NSCLC cells while sparing healthy ones. As Rhapontin teams up with Ceritinib, the duo demonstrates heightened tumor suppression-a testament to the synergy of tradition and innovation.This study exemplifies the fusion of heritage and advancement, harnessing natural compounds to redefine inflammatory cancer treatment. It's a response to NSCLC's challenges and a stride towards alleviating Ceritinib's side effects.