Multi-armed poly(L-glutamic acid)-graft-polypropyleneinime as effective and serum resistant gene delivery vectors

CKG-IMC: An inductive matrix completion method enhanced by CKG and GNN for Alzheimer's disease compound-protein interactions prediction

Alzheimer's disease (AD) is one of the most prevalent chronic neurodegenerative disorders globally, with a rapidly growing population of AD patients and currently no effective therapeutic interventions available. Consequently, the development of therapeutic anti-AD drugs and the identification of AD targets represent one of the most urgent tasks. In this study, in addition to considering known drugs and targets, we explore compound-protein interactions (CPIs) between compounds and proteins relevant to AD. We propose a deep learning model called CKG-IMC to predict Alzheimer's disease compound-protein interaction relationships. CKG-IMC comprises three modules: a collaborative knowledge graph (CKG), a principal neighborhood aggregation graph neural network (PNA), and an inductive matrix completion (IMC). The collaborative knowledge graph is used to learn semantic associations between entities, PNA is employed to extract structural features of the relationship network, and IMC is utilized for CPIs prediction. Compared with a total of 16 baseline models based on similarities, knowledge graphs, and graph neural networks, our model achieves state-of-the-art performance in experiments of 10-fold cross-validation and independent test. Furthermore, we use CKG-IMC to predict compounds interacting with two confirmed AD targets, 42-amino-acid β-amyloid (Aβ42) protein and microtubule-associated protein tau (tau protein), as well as proteins interacting with five FDA-approved anti-AD drugs. The results indicate that the majority of predictions are supported by literature, and molecular docking experiments demonstrate a strong affinity between the predicted compounds and targets.

Amine-terminated dendritic polymers as promising nanoplatform for diagnostic and therapeutic agents' modification: A review

It is often challenging to design diagnostic and therapeutic agents that fulfill all functional requirements. So, bulk and surface modifications as a common approach for biomedical applications have been suggested. There have been considerable research interests in using nanomaterials to the prementioned methods. Among all nanomaterials, dendritic materials with three-dimensional structures, host-guest properties, and nano-polymeric dimensions have received considerable attention. Amine-terminated dendritic structures including, polyamidoamine (PAMAM), polypropyleneimine (PPI), and polyethyleneimine (PEI), have been enormously utilized in bio-modification. This review briefly described the structure of these three common dendritic polymers and their use to modify diagnostic and therapeutic agents in six major applications, including drug delivery, gene delivery, biosensor, bioimaging, tissue engineering, and antimicrobial activity. The current review covers amine-terminated dendritic polymers toxicity challenging and improvement strategies as well.

Identify Compounds' Target Against Alzheimer's Disease Based on In-Silico Approach

BACKGROUND

Alzheimer's disease swept every corner of the globe and the number of patients worldwide has been rising. At present, there are as many as 30 million people with Alzheimer's disease in the world, and it is expected to exceed 80 million people by 2050. Consequently, the study of Alzheimer's drugs has become one of the most popular medical topics.

METHODS

In this study, in order to build a predicting model for Alzheimer's drugs and targets, the attribute discriminators CfsSubsetEval, ConsistencySubsetEval and FilteredSubsetEval are combined with search methods such as BestFirst, GeneticSearch and Greedystepwise to filter the molecular descriptors. Then the machine learning algorithms such as BayesNet, SVM, KNN and C4.5 are used to construct the 2D-Structure Activity Relationship(2D-SAR) model. Its modeling results are utilized for Receiver Operating Characteristic curve(ROC) analysis.

RESULTS

The prediction rates of correctness using Randomforest for AChE, BChE, MAO-B, BACE1, Tau protein and Non-inhibitor are 77.0%, 79.1%, 100.0%, 94.2%, 93.2% and 94.9%, respectively, which are overwhelming as compared to those of BayesNet, BP, SVM, KNN, AdaBoost and C4.5.

CONCLUSION

In this paper, we conclude that Random Forest is the best learner model for the prediction of Alzheimer's drugs and targets. Besides, we set up an online server to predict whether a small molecule is the inhibitor of Alzheimer's target at http://47.106.158.30:8080/AD/. Furthermore, it can distinguish the target protein of a small molecule.

Polycationic adamantane-based dendrons form nanorods in complex with plasmid DNA

Different HYDRAmers are synthesized and complexed to a model plasmid DNA. Appropriate chemical modifications can improve efficiently the complexation to get HYDRAplexes, in form of long nanorods, with very good DNA binding and protecting properties.