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It is crucial for understanding mechanisms of drug action to quantify the three-dimensional (3D) drug distribution within a single cell at nanoscale resolution. Yet it remains a great challenge due to limited lateral resolution, detection sensitivities, and reconstruction problems. The preferable method is using X-ray nano-computed tomography (Nano-CT) to observe and analyze drug distribution within cells, but it is time-consuming, requiring specialized expertise, and often subjective, particularly with ultrasmall metal nanoparticles (NPs). Furthermore, the accuracy of batch data analysis through conventional processing methods remains uncertain. In this study, we used radioenhancer ultrasmall HfO2nanoparticles as a model to develop a modular and automated deep learning aided Nano-CT method for the localization quantitative analysis of ultrasmall metal NPs uptake in cancer cells. We have established an ultrasmall objects segmentation method for 3D Nano-CT images in single cells, which can highly sensitively analyze minute NPs and even ultrasmall NPs in single cells. We also constructed a localization quantitative analysis method, which may accurately segment the intracellularly bioavailable particles from those of the extracellular space and intracellular components and NPs. The high bioavailability of HfO2NPs in tumor cells from deeper penetration in tumor tissue and higher tumor intracellular uptake provide mechanistic insight into HfO2NPs as advanced radioenhancers in the combination of quantitative subcellular image analysis with the therapeutic effects of NPs on 3D tumor spheroids and breast cancer. Our findings unveil the substantial uptake rate and subcellular quantification of HfO2NPs by the human breast cancer cell line (MCF-7). This revelation explicates the notable efficacy and safety profile of HfO2NPs in tumor treatment. These findings demonstrate that this 3D imaging technique promoted by the deep learning algorithm has the potential to provide localization quantitative information about the 3D distributions of specific molecules at the nanoscale level. This study provides an approach for exploring the subcellular quantitative analysis of NPs in single cells, offering a valuable quantitative imaging tool for minute amounts or ultrasmall NPs.

In view of huge search space in drug design, machine learning has become a powerful method to predict the affinity between small molecular drug and targeting protein with the development of artificial intelligence technology. However, various machine learning algorithms including massive different parameters make the prediction framework choice to be quite difficult. In this work, we took a recent drug design competition (from XtalPi company on the DataCastle platform) as the typical case to find the optimized parameters for different machines learning algorithms and the most effective algorithm. After the parameter optimizations, we compared the typical machine learning methods as decision tree (XGBoost, LightGBM) and artificial neural network (MLP, CNN) with root-mean-square error (RMSE) and coefficient of determination (R2) evaluation. As a result, decision tree is more effective than the neural network as LightGBM>XGBoost>CNN>MLP in the affinity prediction of the specific drug design problem with ~160000 samples. For a much larger screening task in a more complicated drug design study, the sophisticated neural network model may go beyond the decision tree algorithm after generalization enhancing and overfitting reducing. The advanced machine learning methods could extract more information of protein-ligand bindings than traditional ones and improve the screen efficiency of drug design up to 200–1000 times.

Agent and Ontology are distinct technologies that arose independent of each other, having their own standards and specifications. The semantics web is one of the popular research areas these days, and is based on the current Web, which adds more semantics to it for the purpose of building the Ontology of Web content. In this regard, application program on Web can make the purpose of cross-platform calculation come true by taking advantage of Ontology. However, agent is a theory able to enhance abstraction of software itself, and as it is know, negotiation protocol is the basic principle in the electronic commerce which has a direct impact on the efficiency of the negotiation. This study examines the communication architecture with negotiation protocol on the Semantic Web. Precisely speaking, agents make computing with Ontology, and the authors define an agent’s communication ontology for this communication framework and semantic web use Ontology to describe the negotiation protocol. In this context, the buyer or seller will be able to improve semantic cognitive in process of negotiation. Also, it can provide an intelligent platform for the information exchange on the same understanding about the content of communication in the electronic negotiation service.