Screening nanomaterials (NMs) with desired properties from the extensive chemical space presents significant challenges. The potential toxicity of NMs further limits their applications in biological systems. Traditional methods struggle with these complexities, but generative models offer a possible solution to producing new molecules without prior knowledge. However, converting complex 3D nanostructures into computer-readable formats remains a critical prerequisite. To overcome these challenges, we proposed an innovative deep-learning framework for the de novo design of biocompatible NMs. This framework comprises two predictive models and a generative model, utilizing a Quasi-SMILES representation to encode three-dimensional structural information on NMs. Our generative model successfully created 289 new NMs not previously seen in the training set. The predictive models identified a particularly promising NM characterized by high cellular uptake and low toxicity. This NM was successfully synthesized, and its predicted properties were experimentally validated. Our approach advances the application of artificial intelligence in NM design and provides a practical solution for balancing functionality and toxicity in NMs.
Keywords: nano-QSAR; nanobio interactions; nanocombinatorial chemistry; nanodescriptors; nanotoxicity.