02057nas a2200289   4500008004100000022001400041245010400055210006900159260011100228300001200339490000700351520101200358653002101370653002001391653002101411653002201432653001101454653002301465653002301488653001601511653002201527100003301549700001901582700002101601700001501622856013001637       2015                        eng d  a1386-207300aDesign and development of cheminfocloud: an integrated cloud enabled platform for virtual screening0 aDesign and development of cheminfocloud an integrated cloud enab  aEXECUTIVE STE Y-2, PO BOX 7917, SAIF ZONE, 1200 BR SHARJAH, U ARAB EMIRATESbBENTHAM SCIENCE PUBL LTDcJAN  a604-6190 v183 a<p>The power of cloud computing and distributed computing has been harnessed to handle vast and heterogeneous data required to be processed in any virtual screening protocol. A cloud computing platorm ChemInfoCloud was built and integrated with several chemoinformatics and bioinformatics tools. The robust engine performs the core chemoinformatics tasks of lead generation, lead optimisation and property prediction in a fast and efficient manner. It has also been provided with some of the bioinformatics functionalities including sequence alignment, active site pose prediction and protein ligand docking. Text mining, NMR chemical shift (1H, 13C) prediction and reaction fingerprint generation modules for efficient lead discovery are also implemented in this platform. We have developed an integrated problem solving cloud environment for virtual screening studies that also provides workflow management, better usability and interaction with end users using container based virtualization, OpenVz.</p>10aChemoinformatics10acloud computing10amachine learning10aMolecular docking10aOpenVz10asequence alignment10aspectra prediction10atext mining10avirtual screening1 aKarthikeyan, Muthukumarasamy1 aPandit, Deepak1 aBhavasar, Arvind1 aVyas, Renu  uhttps://library.ncl.res.in/content/design-and-development-cheminfocloud-integrated-cloud-enabled-platform-virtual-screening-002426nas a2200241   4500008004100000022001400041245012700055210006900182260011100251300001200362490000700374520149700381653002101878653002001899653001201919653001601931653002201947100003301969700001902002700001902021700001502040856012902055       2015                        eng d  a1386-207300aMegaMiner: a tool for lead identification through text mining using chemoinformatics tools and cloud computing environment0 aMegaMiner a tool for lead identification through text mining usi  aEXECUTIVE STE Y-2, PO BOX 7917, SAIF ZONE, 1200 BR SHARJAH, U ARAB EMIRATESbBENTHAM SCIENCE PUBL LTDcJAN  a591-6030 v183 a<p>Virtual screening is an indispensable tool to cope with the massive amount of data being tossed by the high throughput omics technologies. With the objective of enhancing the automation capability of virtual screening process a robust portal termed MegaMiner has been built using the cloud computing platform wherein the user submits a text query and directly accesses the proposed lead molecules along with their drug-like, lead-like and docking scores. Textual chemical structural data representation is fraught with ambiguity in the absence of a global identifier. We have used a combination of statistical models, chemical dictionary and regular expression for building a disease specific dictionary. To demonstrate the effectiveness of this approach, a case study on malaria has been carried out in the present work. MegaMiner offered superior results compared to other text mining search engines, as established by F score analysis. A single query term `malaria' in the portlet led to retrieval of related PubMed records, protein classes, drug classes and 8000 scaffolds which were internally processed and filtered to suggest new molecules as potential anti-malarials. The results obtained were validated by docking the virtual molecules into relevant protein targets. It is hoped that MegaMiner will serve as an indispensable tool for not only identifying hidden relationships between various biological and chemical entities but also for building better corpus and ontologies.</p>10aChemoinformatics10acloud computing10amalaria10atext mining10avirtual screening1 aKarthikeyan, Muthukumarasamy1 aPandit, Yogesh1 aPandit, Deepak1 aVyas, Renu  uhttps://library.ncl.res.in/content/megaminer-tool-lead-identification-through-text-mining-using-chemoinformatics-tools-and-003539nas a2200289   4500008004100000022001400041245013400055210006900189260000800258300001400266490000700280520258100287653001002868653002102878653001602899653001402915653001602929653002202945653002202967100001802989700002803007700002003035700001503055700001803070700003303088856012803121       2020                        eng d  a1386-207300aDesign of novel drug-like molecules using informatics rich secondary metabolites analysis of Indian medicinal and aromatic plants0 aDesign of novel druglike molecules using informatics rich second  cJAN  a1113-11310 v233 a<p>Background: Several medicinal plants are being used in Indian medicine systems from ancient times. However, in most cases, the specific molecules or the active ingredients responsible for the medicinal or therapeutic properties are not yet known. Objective: This study aimed to report a computational protocol as well as a tool for generating novel potential drug candidates from the bioactive molecules of Indian medicinal and aromatic plants through the chemoinformatics approach. Methods: We built a database of the Indian medicinal and aromatic plants coupled with associated information (plant families, plant parts used for the medicinal purpose, structural information, therapeutic properties, etc.) We also developed a Java-based chemoinformatics open-source tool called DoMINE (Database of Medicinally Important Natural products from plantaE) for the generation of virtual library and screening of novel molecules from known medicinal plant molecules. We employed chemoinformatics approaches to in-silico screened metabolites from 104 Indian medicinal and aromatic plants and designed novel drug-like bioactive molecules. For this purpose, 1665 ring containing molecules were identified by text mining of literature related to the medicinal plant species, which were later used to extract 209 molecular scaffolds. Different scaffolds were further used to build a focused virtual library. Virtual screening was performed with cluster analysis to predict drug-like and lead-like molecules from these plant molecules in the context of drug discovery. The predicted drug-like and lead-like molecules were evaluated using chemoinformatics approaches and statistical parameters, and only the most significant molecules were proposed as the candidate molecules to develop new drugs. Results and Conclusion: The supra network of molecules and scaffolds identifies the relationship between the plant molecules and drugs. Cluster analysis of virtual library molecules showed that novel molecules had more pharmacophoric properties than toxicophoric and chemophoric properties. We also developed the DoMINE toolkit for the advancement of natural product-based drug discovery through chemoinformatics approaches. This study will be useful in developing new drug molecules from the known medicinal plant molecules. Hence, this work will encourage experimental organic chemists to synthesize these molecules based on the predicted values. These synthesized molecules need to be subjected to biological screening to identify potential molecules for drug discovery research.</p>
10aDrugs10amedicinal plants10aMetabolites10ascaffolds10atext mining10avirtual libraries10avirtual screening1 aKarade, Divya1 aVijayasarathi, Durairaj1 aKadoo, Narendra1 aVyas, Renu1 aIngle, P., K.1 aKarthikeyan, Muthukumarasamy  uhttps://library.ncl.res.in/content/design-novel-drug-molecules-using-informatics-rich-secondary-metabolites-analysis-indian02031nas a2200253   4500008004100000022001400041245010200055210006900157260000800226300001400234490000700248520121200255653001301467653001501480653001801495653001701513653002101530653001601551100002001567700001801587700003301605700001501638856012401653       2022                        eng d  a1568-026600aReview on computational analysis of big data in breast cancer for predicting potential biomarkers0 aReview on computational analysis of big data in breast cancer fo  cSEP  a1793-18100 v223 a<p>
	Breast cancer is the most predominantly occurring cancer in the world. Several genes and proteins have been recently studied to predict biomarkers that enable early disease identification and monitor its recurrence. In the era of high-throughput technology, studies show several applications of big data for identifying potential biomarkers. The review aims to provide a comprehensive overview of big data analysis in breast cancer towards the prediction of biomarkers with emphasis on computational methods like text mining, network analysis, next-generation sequencing technology (NGS), machine learning (ML), deep learning (DL), and precision medicine. Integrating data from various computational approaches enables the stratification of cancer patients and the identification of molecular signatures in cancer and their subtypes. The computational methods and statistical analysis help expedite cancer prognosis and develop precision cancer medicine (PCM). As a part of case study in the present work, we constructed a large gene-drug interaction network to predict new biomarkers genes. The gene-drug network helped us to identify eight genes that could serve as novel potential biomarkers.</p>
10aBig data10aBiomarkers10aBreast cancer10aDriver genes10aNetwork analysis10atext mining1 aShaikh, Nilofer1 aBapat, Sanket1 aKarthikeyan, Muthukumarasamy1 aVyas, Renu  uhttps://library.ncl.res.in/content/review-computational-analysis-big-data-breast-cancer-predicting-potential-biomarkers