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 (R² _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 pIC₅₀ values for 110 cell lines.