Abstract: Target discovery and identification processes are driven by the increasing amount of biomedical data. The vast numbers of unstructured texts of biomedical publications provide a rich source of knowledge for drug target discovery research and demand the development of specific algorithms or tools to facilitate finding disease genes and proteins. Text mining is a method that can automatically mine helpful information related to drug target discovery from massive biomedical literature. However, there is a substantial lag between biomedical publications and the subsequent abstraction of information extracted by text mining to databases. The knowledge graph is introduced to integrate heterogeneous biomedical data. Here, we describe e-TSN (Target significance and novelty explorer, http://www.lilab-ecust.cn/etsn/), a knowledge visualization web server integrating the largest database of associations between targets and diseases from the full scientific literature by constructing significance and novelty scoring methods based on bibliometric statistics. The platform aims to visualize target-disease knowledge graphs to assist in prioritizing candidate disease-related proteins. Approved drugs and associated bioactivities for each interested target are also provided to facilitate the visualization of drug-target relationships. In summary, e-TSN is a fast and customizable visualization resource for investigating and analyzing the intricate target-disease networks, which could help researchers understand the mechanisms underlying complex disease phenotypes and improve the drug discovery and development efficiency, especially for the unexpected outbreak of infectious disease pandemics like COVID-19.

Abstract: Inflammatory bowel disease (IBD) is characterized by chronic inflammation of the gastrointestinal system. Omega-3 (omega(3)) fatty acids are polyunsaturated fatty acids (PUFAs) that are largely obtained from diet and have been speculated to decrease the inflammatory response that is involved in IBD; however, the causality of this association has not been established. A two-sample Mendelian randomization (MR) was used to assess genetic associations between 249 circulating metabolites measured in the UK Biobank as exposures and IBD as the outcome. The genome-wide association study summary level data for metabolite measurements and IBD were derived from large European ancestry cohorts. We observed omega(3) fatty acids as a significant protective association with IBD, with multiple modes of MR evidence replicated in three IBD summary genetic datasets. The instrumental variables that were involved in the causal association of omega(3) fatty acids with IBD highlighted an intronic SNP, rs174564, in FADS2, a protein engaged in the first step of alpha-linolenic acid desaturation leading to anti-inflammatory EPA and thence DHA production. A low ratio of omega(3) to omega(6) fatty acids was observed to be a causal risk factor, particularly for Crohn's disease. omega(3) fatty acid supplementation may provide anti-inflammatory responses that are required to attenuate inflammation that is involved in IBD.

Abstract: Epigenetic aging clocks are computational models that use DNA methylation sites to predict age. Since cheek swabs are non-invasive and painless, collecting DNA from buccal tissue is highly desirable. Here, we review 11 existing clocks that have been applied to buccal tissue. Two of these were exclusively trained on adults and, while moderately accurate, have not been used to capture health-relevant differences in epigenetic age. Using 130 common CpGs utilized by two or more existing buccal clocks, we generate a proof-of-concept predictor in an adult methylomic dataset. In addition to accurately estimating age (r = 0.95 and mean absolute error = 3.88 years), this clock predicted that Down syndrome subjects were significantly older relative to controls. A literature and database review of CpG-associated genes identified numerous genes (e.g., CLOCK, ELOVL2, and VGF) and molecules (e.g., alpha-linolenic acid, glycine, and spermidine) reported to influence lifespan and/or age-related disease in model organisms.

Abstract: One of the bottlenecks in the application of basic research findings to patients is the enormous cost, time, and effort required for high-throughput screening of potential drugs for given therapeutic targets. Here we have developed LIGHTHOUSE, a graph-based deep learning approach for discovery of the hidden principles underlying the association of small-molecule compounds with target proteins. Without any 3D structural information for proteins or chemicals, LIGHTHOUSE estimates protein-compound scores that incorporate known evolutionary relations and available experimental data. It identified therapeutics for cancer, lifestyle related disease, and bacterial infection. Moreover, LIGHTHOUSE predicted ethoxzolamide as a therapeutic for coronavirus disease 2019 (COVID-19), and this agent was indeed effective against alpha, beta, gamma, and delta variants of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that are rampant worldwide. We envision that LIGHTHOUSE will help accelerate drug discovery and fill the gap between bench side and bedside.

Abstract: Restless legs syndrome (RLS) is a neurological motor disorder with a high prevalence. The treatment efficacy of RLS is unsatisfactory. Radix Paeoniae Alba (RPA) can effectively treat RLS symptoms such as the discomfort of the legs. RPA has great potential for the development of new medications for RLS. Hence, we explored the mechanism of RPA in the treatment of RLS using network pharmacology and molecular docking. The active components and targets of RPA were obtained from the Traditional Chinese Medicine System Pharmacology database and analysis platform and PharmMapper platform. The RLS-related targets were found in GeneCards, OMIM, DrugBank, and DisGeNET databases. The overlapping targets of RPA and RLS were then collected. The "active components-overlapping targets" network was built, and network topology analysis was performed. Furthermore, Cytoscape 3.9.1 software was used to screen the key components of RPA in the treatment of RLS. Protein-protein interaction was performed using the Search Tool for the Retrieval of Interacting Genes. The gene ontology functions and Kyoto Encyclopedia of Genes and Genomes signaling pathways were analyzed using ClusterProfiler, PathView, and other R packages to reveal the main mechanism of RPA in treating RLS. Component and protein structures were downloaded from the Traditional Chinese Medicine System Pharmacology and Protein Data Bank databases, respectively. The AutoDock 4.2.6 software was used for molecular docking. A total of 12 active components and 109 targets of RPA, as well as 2387 RLS-related targets, were collected. Following that, 47 overlapping targets were obtained. Furthermore, 5 key components and 12 core targets were screened. The results of gene ontology functions were as follows: 2368 biological processes, 264 molecular functions, and 164 cellular components. A total of 207 Kyoto Encyclopedia of Genes and Genomes signaling pathways were obtained, including the lipid and atherosclerosis pathway, the endocrine resistance pathway, the prolactin signaling pathway, and the IL-17 signaling pathway. The components and the core targets completed molecular docking stably. RPA has multi-component, multi-target, and multi-pathway characteristics in treating RLS, which could provide a basis for future research and improve clinical efficacy.

Abstract: BACKGROUND: Shengjing capsule (SJC) is a traditional Chinese medicine (TCM) and has gained widespread clinical application for the treatment of male infertility (MI). However, the pharmacological mechanism of SJC against MI remains vague to date. METHOD: The active ingredients of SJC and their targets were identified from the database, and MI-related genes were retrieved from several databases. Protein-protein interaction (PPI) data were obtained to construct the PPI networks. The candidate targets of SJC against MI were identified through topological analysis of the PPI network. Functional enrichment analysis of candidate targets was performed, and the key target genes were identified from the gene-pathway network. RESULTS: We identified 154 active ingredients and 314 human targets of SJC, as well as 564 MI-related genes. Eight pharmacological network diagrams illustrating the interactions among herbs, active ingredients, targets, and pathways, were constructed. The four dominating network maps included a compound-target network of SJC, a compound-anti-MI targets network, a candidate targets PPI network, a pathway-gene network, and a drug-key compounds-hub targets-pathways network. Systematic analysis indicated that the targets of SJC in the treatment of MI mainly involved RPS6, MAPK1, MAPK3, MDM2, and DDX5. Pathway enrichment analysis showed that SJC had the potential to impact multiple biological pathways, such as cancer-related pathways, viral/bacterial infection-related pathways, and signal transduction-related pathways. CONCLUSION: Our results preliminarily revealed the pharmacological basis and molecular mechanism SJC in treating MI, but further experimental research is required to verify these findings.

Abstract: Discovering new medicines is the hallmark of the human endeavor to live a better and longer life. Yet the pace of discovery has slowed down as we need to venture into more wildly unexplored biomedical space to find one that matches today's high standard. Modern AI-enabled by powerful computing, large biomedical databases, and breakthroughs in deep learning offers a new hope to break this loop as AI is rapidly maturing, ready to make a huge impact in the area. In this paper, we review recent advances in AI methodologies that aim to crack this challenge. We organize the vast and rapidly growing literature on AI for drug discovery into three relatively stable sub-areas: (a) representation learning over molecular sequences and geometric graphs; (b) data-driven reasoning where we predict molecular properties and their binding, optimize existing compounds, generate de novo molecules, and plan the synthesis of target molecules; and (c) knowledge-based reasoning where we discuss the construction and reasoning over biomedical knowledge graphs. We will also identify open challenges and chart possible research directions for the years to come.

Abstract: Glioblastoma (GBM) is the most common form of malignant primary brain tumor with a dismal prognosis. Currently, the standard treatments for GBM rarely achieve satisfactory results, which means that current treatments are not individualized and precise enough. In this study, a multiomics-based GBM classification was established and three subclasses (GPA, GPB, and GPC) were identified, which have different molecular features both in bulk samples and at single-cell resolution. A robust GBM poor prognostic signature (GPS) score model was then developed using machine learning method, manifesting an excellent ability to predict the survival of GBM. NVP-BEZ235, GDC-0980, dasatinib and XL765 were ultimately identified to have subclass-specific efficacy targeting patients with a high risk of poor prognosis. Furthermore, the GBM classification and GPS score model could be considered as potential biomarkers for immunotherapy response. In summary, an integrative genomic analysis was conducted to advance individual-based therapies in GBM.

Abstract: Naringenin (NR) is a kind of flavonoid which plays a great role in the treatment of autism spectrum disorder (ASD). However, the underlying mechanism of NR in treating ASD still remains unclear. This study used network pharmacology and molecular docking to examine the potential targets and pharmacological mechanism of NR on ASD. Targets related to NR were screened from Traditional Chinese Medicine System Pharmacology Database and Analysis Platform (TCMSP), Encyclopedia of Traditional Chinese Medicine Database (ETCM), Traditional Chinese Medicine Integrated Database (TCMID), PharmaMapper database, and targets related to ASD were screened from Online Mendelian Inheritance In Man (OMIM), Disgenet, GeneCards, Therapeutic Target Database (TTD), Drugbank, and ETCM. Screened of the intersected gene targets. Then, we used the protein-protein interaction (PPI) networks to construct a PPI network and used Network Analyzer plug-in to perform topological analysis to screen out the core target. We used Metascape platform to perform gene ontology (GO) functional enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, and used Chem draw, Pymol, AutoDock 1.5.6 software for molecular docking verification with core targets. A total of 149 targets of NR and 1594 potential targets of ASD were screened, and 43 intersected targets and 8 key targets were obtained and screened. A total of 176 GO items were obtained by GO enrichment analysis (P < .05), 153 entries on biological process (BP), 12 entries on BP and 11entries on cell composition (CC) were included. A total of 100 signaling pathways were obtained by KEGG pathway enrichment screening (P < .05).The pathways that are closely related to the pathogenesis of ASD are estrogen signaling, thyroid hormone signaling pathway, prolactin signaling pathway, and endocrine resistance pathway. Molecular docking results showed that NR had the best docking activity with the core target CASP3, and had good binding ability with AKT1, ESR1, ACTB and MAPK3. Taken together, our findings support that NR exerts therapeutic effects on ASD with multi-target, and multi-pathway characteristics, which provides a preliminary theoretical basis for clinical trials. The mechanism of anti-oxidative stress response, anti-apoptosis, regulation of cell growth and metabolism, anti-inflammatory, balance hormone levels may be important for the therapeutic effect.

Abstract: BACKGROUND: Liuwei Dihuang Pill is widely used to treat tinnitus in China. However, the underlying mechanism of Liuwei Dihuang Pill in treating tinnitus still remains unclear. OBJECTIVE: To explore the potential pharmacological mechanism of Liuwei Dihuang Pill in the treatment of tinnitus based on network pharmacology and molecular docking. METHODS: The active components of the Liuwei Dihuang Pill were obtained from the traditional Chinese medicine systems pharmacology database and analysis platform (TCMSP) database. Cytoscape software was used to draw the active component-target network diagram of Liuwei Dihuang Pill, and obtain the core components. Then the corresponding targets were also obtained from the TCMSP database. Targets related to tinnitus were obtained from the GeneCards, DisGeNET, TTD and DrugBank databases. The String database was used to construct protein-protein interaction (PPI) network of common targets of drugs and diseases, then the core targets were screened out. The Annotation, Visualization and Integrated Discovery (DAVID) database was used for gene ontology (GO) enrichment and Kyoto encyclopedia of genes and genomes (KEGG) pathway analysis of common targets. Finally, the molecular docking between the core component and the core target was carried out by AutoDock. RESULTS: The core components of Liuwei Dihuang Pill in the treatment of tinnitus including quercetin, stigmasterol, kaempferol, beta-sitosterol, tetrahydroalstonine, which may act on core targets such as STAT3, transcription factor AP-1 (JUN), tumor necrosis factor (TNF), interleukin-6 and MAPK3. HIF-1 signaling pathway, Influenza A, P53 signaling pathway, and Toll-like receptor signaling pathway play a role in anti-inflammatory, improving microcirculation in the blood-labyrinth barrier, increasing cochlear blood flow, and preventing hair cell damage. The molecular docking results showed that the affinity between core components and core targets was good. CONCLUSION: The potential mechanism of Liuwei Dihuang Pill in the treatment of tinnitus was preliminarily discussed in this study, which may provide a theoretical basis and evidence for further experimental research.