Cellular Stress-Modulating Drugs Can Potentially Be Identified by in Silico Screening with Connectivity Map (CMap)

Gao, Yurong; Kim, Sung Woo; Lee, Yun‐Il; Lee, Jaemin

doi:10.3390/ijms20225601

Cited by 32 publications

(23 citation statements)

References 178 publications

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“…The other approach is using transcriptome data for DTI predictions, which measures the biological effect of drug action in in vitro experimental conditions. After the first release of the CMAP [ 69 ], a large-scale drug-induced transcriptome dataset, there have been many studies that succeeded in the identification of the drug repositioning candidates for a variety of diseases or the elucidation of the drug mode of action [ 140 , 141 , 142 , 143 ]. A number of studies have also employed the gene-expression profiles as the chemogenomic features for predicting DTIs.…”

Section: Deep Learning Methods For Drug–target Interaction Predictionmentioning

confidence: 99%

Comprehensive Survey of Recent Drug Discovery Using Deep Learning

Kim

Park

Min

et al. 2021

IJMS

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Drug discovery based on artificial intelligence has been in the spotlight recently as it significantly reduces the time and cost required for developing novel drugs. With the advancement of deep learning (DL) technology and the growth of drug-related data, numerous deep-learning-based methodologies are emerging at all steps of drug development processes. In particular, pharmaceutical chemists have faced significant issues with regard to selecting and designing potential drugs for a target of interest to enter preclinical testing. The two major challenges are prediction of interactions between drugs and druggable targets and generation of novel molecular structures suitable for a target of interest. Therefore, we reviewed recent deep-learning applications in drug–target interaction (DTI) prediction and de novo drug design. In addition, we introduce a comprehensive summary of a variety of drug and protein representations, DL models, and commonly used benchmark datasets or tools for model training and testing. Finally, we present the remaining challenges for the promising future of DL-based DTI prediction and de novo drug design.

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Section: Deep Learning Methods For Drug–target Interaction Predictionmentioning

confidence: 99%

Comprehensive Survey of Recent Drug Discovery Using Deep Learning

Kim

Park

Min

et al. 2021

IJMS

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“…Compounds/Inhibitors That Can Target the mTOR Pathway. We used Connectivity Map (CMap) [33], a systematic approach that is driven by data, to discover links between genes, chemicals, and biological situations to search for compounds and inhibitors that might target mTOR-related pathways (Figure 3(a)). According to the results and the actual situation, most of these candidate compounds have been reported to be used against cancer.…”

Section: Connectivity Map (Cmap) Analysis Identifying Potentialmentioning

confidence: 99%

Genomic and Transcriptome Analysis to Identify the Role of the mTOR Pathway in Kidney Renal Clear Cell Carcinoma and Its Potential Therapeutic Significance

Che

et al. 2021

Oxidative Medicine and Cellular Longevity

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The mTOR pathway, a major signaling pathway, regulates cell growth and protein synthesis by activating itself in response to upstream signals. Overactivation of the mTOR pathway may affect the occurrence and development of cancer, but no specific treatment has been proposed for targeting the mTOR pathway. In this study, we explored the expression of mTOR pathway genes in a variety of cancers and the potential compounds that target the mTOR pathway and focused on an abnormal type of cancer, kidney renal clear cell carcinoma (KIRC). Based on the mRNA expression of the mTOR pathway gene, we divided KIRC patient samples into three clusters. We explored possible therapeutic targets of the mTOR pathway in KIRC. We predicted the IC50 of some classical targeted drugs to analyze their correlation with the mTOR pathway. Subsequently, we investigated the correlation of the mTOR pathway with histone modification and immune infiltration, as well as the response to anti-PD-1 and anti-CTLA-4 therapy. Finally, we used a LASSO regression analysis to construct a model to predict the survival of patients with KIRC. This study shows that mTOR scores can be used as tools to study various treatments targeting the mTOR pathway and that we can predict the recovery of KIRC patients through the expression of mTOR pathway genes. These research results can provide a reference for future research on KIRC patient treatment strategies.

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“…In the central node of the network, HSPA5, DDIT3, DNAJC3, ATF3, XBP1, PPP1R15A, DNAJB9, TRIB3, PDIA3, ASNS, HERPUD1, DNAJB1, PDIA4, TXNRD1, DUSP1, ERN1 are genes related to endoplasmic reticulum stress, which is essential for regulating the control of protein quality by endoplasmic reticulum and maintaining the balance of redox state (Xu and Park, 2018;Amen et al, 2019;Gao et al, 2019;Kim E. K. et al, 2019). Current research shows that Endoplasmic Reticulum Associated Unfolded Protein Response (UPR) and endoplasmic reticulum stress can affect the migration and invasion characteristics of breast cancer cells.…”

Section: Md-mb 231 Cellsmentioning

confidence: 99%

Exploring the Regulation Mechanism of Xihuang Pill, Olibanum and β-Boswellic Acid on the Biomolecular Network of Triple-Negative Breast Cancer Based on Transcriptomics and Chemical Informatics Methodology

Yang

Zeng

et al. 2020

Front. Pharmacol.

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Background: Xihuang Pill (XHP) is mainly used to treat "Ru Yan (breast cancer)". Evidence-based medical evidence and showed that XHP improves the efficacy of chemotherapy and reduced chemotherapy-induced toxicity in breast cancer patients. However, the mechanism of XHP against breast cancer is not clear. Methods: The effect of XHP extract on cell half-inhibitory concentration (IC50) and cell viability of MD-MB-231 cells was detected by CCK-8 method. The cell inhibition rate of MDA-MB-453 cells were detected by MTT method. Apoptosis was detected by flow cytometry, cell transfer ability was detected by Transwell method, and cell proliferation ability was detected by colony formation assay. The expression of Notch1, b-catenin and c-myc mRNA in MDA-MB-453 cells were detected by real-time fluorescence quantitative PCR. Then, chemical informatics and transcriptomics methodology was utilized to predict the potential compounds and targets of XHP, and collect triple negative breast cancer (TNBC) genes and the data of Olibanum and b-boswellic acid intervention MD-MB-231 cells (from GSE102891). The cytoscape software was utilized to undergo network construction and network analysis. Finally, the data from the network analysis was imported into the DAVID database for enrichment analysis of signaling pathways and biological processes. Results: The IC50 was 15.08 g/L (for MD-MB-231 cells). After interfering with MD-MB-231 cells with 15.08 g/L XHP extract for 72 h, compared with the control group, the cell viability, migration and proliferation was significantly decreased, while early apoptosis and

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Cellular Stress-Modulating Drugs Can Potentially Be Identified by in Silico Screening with Connectivity Map (CMap)

Cited by 32 publications

References 178 publications

Comprehensive Survey of Recent Drug Discovery Using Deep Learning

Comprehensive Survey of Recent Drug Discovery Using Deep Learning

Genomic and Transcriptome Analysis to Identify the Role of the mTOR Pathway in Kidney Renal Clear Cell Carcinoma and Its Potential Therapeutic Significance

Exploring the Regulation Mechanism of Xihuang Pill, Olibanum and β-Boswellic Acid on the Biomolecular Network of Triple-Negative Breast Cancer Based on Transcriptomics and Chemical Informatics Methodology

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