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  • 1
    Online Resource
    Online Resource
    The Difference of Zagreb Indices of Halin Graphs
    MDPI AG ; 2023
    In:  Axioms Vol. 12, No. 5 ( 2023-05-02), p. 450-
    Details
    In: Axioms, MDPI AG, Vol. 12, No. 5 ( 2023-05-02), p. 450-
    Abstract: The difference of Zagreb indices of a graph G is defined as ΔM(G)=∑u∈V(G)(d(u))2−∑uv∈E(G)d(u)d(v), where d(x) denotes the degree of a vertex x in G. A Halin graph G is a graph that results from a plane tree T without vertices of degree two and with at least one vertex of degree at least three such that all leaves are joined through a cycle C in the embedded order. In this paper, we establish both lower and upper bounds on the difference of Zagreb indices for general Halin graphs and some special Halin graphs with fewer inner vertices. Furthermore, extremal graphs attaining related bounds are found.
    Type of Medium: Online Resource
    ISSN: 2075-1680
    URL: Article
    DOI: 10.3390/axioms12050450
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2661511-3
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  • 2
    Online Resource
    Online Resource
    Lead/Drug Discovery from Natural Resources
    MDPI AG ; 2022
    In:  Molecules Vol. 27, No. 23 ( 2022-11-28), p. 8280-
    Details
    In: Molecules, MDPI AG, Vol. 27, No. 23 ( 2022-11-28), p. 8280-
    Abstract: Natural products and their derivatives have been shown to be effective drug candidates against various diseases for many years. Over a long period of time, nature has produced an abundant and prosperous source pool for novel therapeutic agents with distinctive structures. Major natural-product-based drugs approved for clinical use include anti-infectives and anticancer agents. This paper will review some natural-product-related potent anticancer, anti-HIV, antibacterial and antimalarial drugs or lead compounds mainly discovered from 2016 to 2022. Structurally typical marine bioactive products are also included. Molecular modeling, machine learning, bioinformatics and other computer-assisted techniques that are very important in narrowing down bioactive core structural scaffolds and helping to design new structures to fight against key disease-associated molecular targets based on available natural products are considered and briefly reviewed.
    Type of Medium: Online Resource
    ISSN: 1420-3049
    URL: Article
    DOI: 10.3390/molecules27238280
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2008644-1
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  • 3
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    Online Resource
    Literature-Wide Association Studies (LWAS) for a Rare Disease: Drug Repurposing for Inflammatory Breast Cancer
    MDPI AG ; 2020
    In:  Molecules Vol. 25, No. 17 ( 2020-08-28), p. 3933-
    Details
    In: Molecules, MDPI AG, Vol. 25, No. 17 ( 2020-08-28), p. 3933-
    Abstract: Drug repurposing is an effective means for rapid drug discovery. The aim of this study was to develop and validate a computational methodology based on Literature-Wide Association Studies (LWAS) of PubMed to repurpose existing drugs for a rare inflammatory breast cancer (IBC). We have developed a methodology that conducted LWAS based on the text mining technology Word2Vec. 3.80 million “cancer”-related PubMed abstracts were processed as the corpus for Word2Vec to derive vector representation of biological concepts. These vectors for drugs and diseases served as the foundation for creating similarity maps of drugs and diseases, respectively, which were then employed to find potential therapy for IBC. Three hundred and thirty-six (336) known drugs and three hundred and seventy (370) diseases were expressed as vectors in this study. Nine hundred and seventy (970) previously known drug-disease association pairs among these drugs and diseases were used as the reference set. Based on the hypothesis that similar drugs can be used against similar diseases, we have identified 18 diseases similar to IBC, with 24 corresponding known drugs proposed to be the repurposing therapy for IBC. The literature search confirmed most known drugs tested for IBC, with four of them being novel candidates. We conclude that LWAS based on the Word2Vec technology is a novel approach to drug repurposing especially useful for rare diseases.
    Type of Medium: Online Resource
    ISSN: 1420-3049
    URL: Article
    DOI: 10.3390/molecules25173933
    Language: English
    Publisher: MDPI AG
    Publication Date: 2020
    detail.hit.zdb_id: 2008644-1
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  • 4
    Online Resource
    Online Resource
    Oncogenic Role of Tumor Necrosis Factor α-Induced Protein 8 (TNFAIP8)
    MDPI AG ; 2018
    In:  Cells Vol. 8, No. 1 ( 2018-12-24), p. 9-
    Details
    In: Cells, MDPI AG, Vol. 8, No. 1 ( 2018-12-24), p. 9-
    Abstract: Tumor necrosis factor (TNF)-α-induced protein 8 (TNFAIP8) is a founding member of the TIPE family, which also includes TNFAIP8-like 1 (TIPE1), TNFAIP8-like 2 (TIPE2), and TNFAIP8-like 3 (TIPE3) proteins. Expression of TNFAIP8 is strongly associated with the development of various cancers including cancer of the prostate, liver, lung, breast, colon, esophagus, ovary, cervix, pancreas, and others. In human cancers, TNFAIP8 promotes cell proliferation, invasion, metastasis, drug resistance, autophagy, and tumorigenesis by inhibition of cell apoptosis. In order to better understand the molecular aspects, biological functions, and potential roles of TNFAIP8 in carcinogenesis, in this review, we focused on the expression, regulation, structural aspects, modifications/interactions, and oncogenic role of TNFAIP8 proteins in human cancers.
    Type of Medium: Online Resource
    ISSN: 2073-4409
    URL: Article
    DOI: 10.3390/cells8010009
    Language: English
    Publisher: MDPI AG
    Publication Date: 2018
    detail.hit.zdb_id: 2661518-6
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  • 5
    Online Resource
    Online Resource
    Cheminformatics Modeling of Gene Silencing for Both Natural and Chemically Modified siRNAs
    MDPI AG ; 2022
    In:  Molecules Vol. 27, No. 19 ( 2022-09-28), p. 6412-
    Details
    In: Molecules, MDPI AG, Vol. 27, No. 19 ( 2022-09-28), p. 6412-
    Abstract: In designing effective siRNAs for a specific mRNA target, it is critically important to have predictive models for the potency of siRNAs. None of the published methods characterized the chemical structures of individual nucleotides constituting a siRNA molecule; therefore, they cannot predict the potency of gene silencing by chemically modified siRNAs (cm-siRNA). We propose a new approach that can predict the potency of gene silencing by cm-siRNAs, which characterizes each nucleotide (NT) using 12 BCUT cheminformatics descriptors describing its charge distribution, hydrophobic and polar properties. Thus, a 21-NT siRNA molecule is described by 252 descriptors resulting from concatenating all the BCUT values of its composing nucleotides. Partial Least Square is employed to develop statistical models. The Huesken data (2431 natural siRNA molecules) were used to perform model building and evaluation for natural siRNAs. Our results were comparable with or superior to those from Huesken’s algorithm. The Bramsen dataset (48 cm-siRNAs) was used to build and test the models for cm-siRNAs. The predictive r2 of the resulting models reached 0.65 (or Pearson r values of 0.82). Thus, this new method can be used to successfully model gene silencing potency by both natural and chemically modified siRNA molecules.
    Type of Medium: Online Resource
    ISSN: 1420-3049
    URL: Article
    DOI: 10.3390/molecules27196412
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2008644-1
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