Molecular modeling and docking analysis of aspirin with pde7b in the context of neuro-inflammation

A Mild Dose of Aspirin Promotes Hippocampal Neurogenesis and Working Memory in Experimental Ageing Mice

Aspirin has been reported to prevent memory decline in the elderly population. Adult neurogenesis in the hippocampus has been recognized as an underlying basis of learning and memory. This study investigated the effect of aspirin on spatial memory in correlation with the regulation of hippocampal neurogenesis and microglia in the brains of ageing experimental mice. Results from the novel object recognition (NOR) test, Morris water maze (MWM), and cued radial arm maze (cued RAM) revealed that aspirin treatment enhances working memory in experimental mice. Further, the co-immunohistochemical assessments on the brain sections indicated an increased number of doublecortin (DCX)-positive immature neurons and bromodeoxyuridine (BrdU)/neuronal nuclei (NeuN) double-positive newly generated neurons in the hippocampi of mice in the aspirin-treated group compared to the control group. Moreover, a reduced number of ionized calcium-binding adaptor molecule (Iba)-1-positive microglial cells was evident in the hippocampus of aspirin-treated animals. Recently, enhanced activity of acetylcholinesterase (AChE) in circulation has been identified as an indicative biomarker of dementia. The biochemical assessment in the blood of aspirin-treated mice showed decreased activity of AChE in comparison with that of the control group. Results from this study revealed that aspirin facilitates hippocampal neurogenesis which might be linked to enhanced working memory.

Deep Neural Network-Assisted Drug Recommendation Systems for Identifying Potential Drug-Target Interactions

In silico methods to identify novel drug-target interactions (DTIs) have gained significant importance over conventional techniques owing to their labor-intensive and low-throughput nature. Here, we present a machine learning-based multiclass classification workflow that segregates interactions between active, inactive, and intermediate drug-target pairs. Drug molecules, protein sequences, and molecular descriptors were transformed into machine-interpretable embeddings to extract critical features from standard datasets. Tools such as CHEMBL web resource, iFeature, and an in-house developed deep neural network-assisted drug recommendation (dNNDR)-featx were employed for data retrieval and processing. The models were trained with large-scale DTI datasets, which reported an improvement in performance over baseline methods. External validation results showed that models based on att-biLSTM and gCNN could help predict novel DTIs. When tested with a completely different dataset, the proposed models significantly outperformed competing methods. The validity of novel interactions predicted by dNNDR was backed by experimental and computational evidence in the literature. The proposed methodology could elucidate critical features that govern the relationship between a drug and its target.