Relating Substructures and Side Effects of Drugs with Chemical-chemical Interactions

Asymmetric Lipid Vesicles: Techniques, Applications, and Future Perspectives as an Innovative Drug Delivery System

Novel lipid-based nanosystems have been of interest in improving conventional drug release methods. Liposomes are the most studied nanostructures, consisting of lipid bilayers ideal for drug delivery, thanks to their resemblance to the cell plasma membrane. Asymmetric liposomes are vesicles with different lipids in their inner and outer layers; because of this, they can be configured to be compatible with the therapeutic drug while achieving biocompatibility and stability. Throughout this review, topics such as the applications, advantages, and synthesis techniques of asymmetric liposomes will be discussed. Further, an in silico analysis by computational tools will be examined as a helpful tool for designing and understanding asymmetric liposome mechanisms in pharmaceutical applications. The dual-engineered design of asymmetric liposomes makes them an ideal alternative for transdermal drug delivery because of the improved protection of pharmaceuticals without lowering adsorption rates and system biocompatibility.

A novel graph attention model for predicting frequencies of drug-side effects from multi-view data

Identifying the frequencies of the drug-side effects is a very important issue in pharmacological studies and drug risk-benefit. However, designing clinical trials to determine the frequencies is usually time consuming and expensive, and most existing methods can only predict the drug-side effect existence or associations, not their frequencies. Inspired by the recent progress of graph neural networks in the recommended system, we develop a novel prediction model for drug-side effect frequencies, using a graph attention network to integrate three different types of features, including the similarity information, known drug-side effect frequency information and word embeddings. In comparison, the few available studies focusing on frequency prediction use only the known drug-side effect frequency scores. One novel approach used in this work first decomposes the feature types in drug-side effect graph to extract different view representation vectors based on three different type features, and then recombines these latent view vectors automatically to obtain unified embeddings for prediction. The proposed method demonstrates high effectiveness in 10-fold cross-validation. The computational results show that the proposed method achieves the best performance in the benchmark dataset, outperforming the state-of-the-art matrix decomposition model. In addition, some ablation experiments and visual analyses are also supplied to illustrate the usefulness of our method for the prediction of the drug-side effect frequencies. The codes of MGPred are available at https://github.com/zhc940702/MGPred and https://zenodo.org/record/4449613.