NanoToxRadar: A Multitarget Nano-QSAR Model for Predicting the Cytotoxicity Values of Multicomponent Nanoparticles
Jaehyeon Park, Shahzad Rashid, Helena Copsey, Lăng Văn Trần, Alex Zabeo, Danail Rumenov Hristozov, Georgios P. Gakis, Costas A. Charitidis, Seokjoo Yoon, Hyun Kil Shin · ACS Nanoscience Au · 2025
High Resolution Image Download MS PowerPoint Slide Nanotechnological advances have led to the development of nanoparticles with complex structures. In this context, nano-QSAR models have been developed to assess toxicity; however, the applicability domain (AD) of such models is significantly restricted to specific types of nanoparticles (i.e., bare metal oxides, coated metals, or carbon-based nanomaterials) and target cell lines. Accordingly, NanoToxRadar, a web-based platform for predicting the toxicity of multicomponent nanoparticles (MC-NPs) toward various cell lines, was developed to extend the AD of the nano-QSAR model. The size-dependent electron-configuration fingerprint was used to represent the molecular structures of MC-NPs, and one-hot encoded cell types were used to predict toxicities toward 110 cell lines. The CatBoost regression model achieved good performance (R 2 Test = 0.877) and was deployed online ( https://www.kitox.re.kr/nanotoxradar ). The Web site takes the nanoparticle composition of the core as well as the shell, dopant, coating material, and diameter as inputs and predicts pIC 50 values for 110 cell lines.