Current drug development is still costly and slow given tremendous technological advancements in drug discovery and medicinal chemistry. Using machine learning (ML) to virtually screen compound libraries promises to fix this for generating drug leads more efficiently and accurately. Herein, we explain the broad basics and integration of both virtual screening (VS) and ML. We then discuss artificial neural networks (ANNs) and their usage for VS. The ANN is emerging as the dominant classifier for ML in general, and has proven its utility for both structure-based and ligand-based VS. Techniques such as dropout, multitask learning and convolution improve the performance of ANNs and enable them to take on chemical meaning when learning about the drug-target-binding activity of compounds.
Alzheimer’s Disease (AD) is a neurodegenerative condition that currently has no known cure. The principles of the expanding field of network medicine (NM) have recently been applied to AD research. The main principle of NM proposes that diseases are much more complicated than one mutation in one gene, and incorporate different genes, connections between genes, and pathways that may include multiple diseases to create full scale disease networks. AD research findings as a result of the application of NM principles have suggested that functional network connectivity, myelination, myeloid cells, and genes and pathways may play an integral role in AD progression, and may be integral to the search for a cure. Different aspects of the AD pathology could be potential targets for drug therapy to slow down or stop the disease from advancing, but more research is needed to reach definitive conclusions. Additionally, the holistic approaches of network pharmacology in traditional Chinese medicine (TCM) research may be viable options for the AD treatment, and may lead to an effective cure for AD in the future.
Alzheimer’s disease (AD) is an age-related progressive form of dementia that features neuronal loss, intracellular tau, and extracellular amyloid-β (Aβ) protein deposition. Neurodegeneration is accompanied by neuroinflammation mainly involving microglia, the resident innate immune cell population of the brain. During AD progression, microglia shift their phenotype, and it has been suggested that they express matricellular proteins such as secreted protein acidic and rich in cysteine (SPARC) and Hevin protein, which facilitate the migration of other immune cells, such as blood-derived dendritic cells. We have detected both SPARC and Hevin in postmortem AD brain tissues and confirmed significant alterations in transcript expression using real-time qPCR. We suggest that an infiltration of myeloid-derived immune cells occurs in the areas of diseased tissue. SPARC is highly expressed in AD brain and collocates to Aβ protein deposits, thus contributing actively to cerebral inflammation and subsequent tissue repair, and Hevin may be downregulated in the diseased state. However, further research is needed to reveal the exact roles of SPARC and Hevin proteins and associated signaling pathways in AD-related neuroinflammation. Nevertheless, normalizing SPARC/Hevin protein expression such as interdicting heightened SPARC protein expression may confer a novel therapeutic opportunity for modulating AD progression.
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