A granularity-level information fusion strategy on hypergraph transformer for predicting synergistic effects of anticancer drugs.

Combination therapy has exhibited substantial potential compared to monotherapy. However, due to the explosive growth in the number of cancer drugs, the screening of synergistic drug combinations has become both expensive and time-consuming. Synergistic drug combinations refer to the concurrent use of two or more drugs to enhance treatment efficacy. Currently, numerous computational methods have been developed to predict the synergistic effects of anticancer drugs. However, there has been insufficient exploration of how to mine drug and cell line data at different granularity levels for predicting synergistic anticancer drug combinations. Therefore, this study proposes a granularity-level information fusion strategy based on the hypergraph transformer, named HypertranSynergy, to predict synergistic effects of anticancer drugs. HypertranSynergy introduces synergistic connections between cancer cell lines and drug combinations using hypergraph. Then, the Coarse-grained Information Extraction (CIE) module merges the hypergraph with a transformer for node embeddings. In the CIE module, Contranorm is a normalization layer that mitigates over-smoothing, while Gaussian noise addresses local information gaps. Additionally, the Fine-grained Information Extraction (FIE) module assesses fine-grained information's impact on predictions by employing similarity-aware matrices from drug/cell line features. Both CIE and FIE modules are integrated into HypertranSynergy. In addition, HypertranSynergy achieved the AUC of 0.93${\pm }$0.01 and the AUPR of 0.69${\pm }$0.02 in 5-fold cross-validation of classification task, and the RMSE of 13.77${\pm }$0.07 and the PCC of 0.81${\pm }$0.02 in 5-fold cross-validation of regression task. These results are better than most of the state-of-the-art models.

Preparation of chlorogenic acid surface-imprinted magnetic nanoparticles and their usage in separation of traditional Chinese medicine.

The chlorogenic acid (CGA) surface-imprinted magnetic polymer nanoparticles have been prepared via water-in-oil-in-water multiple emulsions suspension polymerization. This kind of molecularly imprinted polymer nanoparticles (MIPs) had the core-shell structure with the size of about 50 nm. Magnetic susceptibility was given by the successful encapsulation of Fe(3)O(4) nanoparticles with a high encapsulation efficiency of 19.3 wt%. MIPs showed an excellent recognition and selection properties for the imprinted molecule CGA. The recognition capacity of MIPs was near three times than that of non-imprinted polymer nanoparticles (NIPs). Compared with the competitive molecule caffeic acid (CFA), the selectivity of MIPs for CGA was 6.06 times as high as that of NIPs. MIPs could be reused and regenerated, and their rebinding amount in the fifth use was up to 78.85% of that in the first use. The MIPs prepared were successfully applied to the separation of CGA from the extract of Traditional Chinese Medicine Honeysuckle.