Deep Learning‐Driven Co‐Assembly of Naturally Sourced Compound Nanoparticles for Potentiated Cancer Immunotherapy

Abstract Co‐assembly of excipient‐free nanoparticles has emerged as a promising drug delivery platform due to their high drug‐loading capacity, ease of preparation, and ability to achieve combination therapeutic effects. However, the absence of systematic design strategies has hindered their broader application. In this study, a deep learning platform, Gramord, is developed to rationally design the excipient‐free anti‐tumor nanoparticles of nature‐sourced compounds. A comprehensive database of excipient‐free nanoparticles is first built and used to train Gramord for predicting self‐assembly compatibility. By screening 1800 naturally‐derived small molecules and their derivatives, the compound pairs capable of forming excipient‐free nanoparticles are identified. Leveraging the advantage of oridonin (Ori) for inducing apoptosis of tumor cells and cepharanthine (Cep) for eliciting immunogenic cell death of tumor cells, the Ori‐Cep pair for preparing the self‐assemble nanoparticles (namely OCN) is subsequently selected. Using a mouse model of CT26 colorectal tumor, it is demonstrated that the systemically administrated OCN specifically accumulate at the tumor sites, and regress tumor growth by inducing anti‐tumor immunogenicity and recruiting tumor‐infiltrating cytotoxic T lymphocytes. This study highlights the application of artificial intelligence in designing excipient‐free nanomedicine, offering a scalable and cost‐effective approach to expanded therapeutic options.