@article { ISI:000360934400007, title = {Design and development of cheminfocloud: an integrated cloud enabled platform for virtual screening}, journal = {Combinatorial Chemistry \& High Throughput Screening}, volume = {18}, number = {6}, year = {2015}, month = {JAN}, pages = {604-619}, publisher = {BENTHAM SCIENCE PUBL LTD}, address = {EXECUTIVE STE Y-2, PO BOX 7917, SAIF ZONE, 1200 BR SHARJAH, U ARAB EMIRATES}, abstract = {

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.

}, keywords = {Chemoinformatics, cloud computing, machine learning, Molecular docking, OpenVz, sequence alignment, spectra prediction, text mining, virtual screening}, issn = {1386-2073}, doi = {10.2174/1386207318666150703113656}, author = {Karthikeyan, Muthukumarasamy and Pandit, Deepak and Bhavasar, Arvind and Vyas, Renu} } @article { ISI:000360934400006, title = {MegaMiner: a tool for lead identification through text mining using chemoinformatics tools and cloud computing environment}, journal = {Combinatorial Chemistry \& High Throughput Screening}, volume = {18}, number = {6}, year = {2015}, month = {JAN}, pages = {591-603}, publisher = {BENTHAM SCIENCE PUBL LTD}, address = {EXECUTIVE STE Y-2, PO BOX 7917, SAIF ZONE, 1200 BR SHARJAH, U ARAB EMIRATES}, abstract = {

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 {\textquoteleft}malaria{\textquoteright} 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.

}, keywords = {Chemoinformatics, cloud computing, malaria, text mining, virtual screening}, issn = {1386-2073}, doi = {10.2174/1386207318666150703113525}, author = {Karthikeyan, Muthukumarasamy and Pandit, Yogesh and Pandit, Deepak and Vyas, Renu} } @article {47013, title = {Design of novel drug-like molecules using informatics rich secondary metabolites analysis of Indian medicinal and aromatic plants}, journal = {Combinatorial Chemistry \& High Throughput Screening}, volume = {23}, year = {2020}, month = {JAN}, pages = {1113-1131}, type = {Article}, abstract = {

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.

}, keywords = {Drugs, medicinal plants, Metabolites, scaffolds, text mining, virtual libraries, virtual screening}, issn = {1386-2073}, doi = {10.2174/1386207323666200606211342}, author = {Karade, Divya and Vijayasarathi, Durairaj and Kadoo, Narendra and Vyas, Renu and Ingle, P. K. and Karthikeyan, Muthukumarasamy} } @article {48103, title = {Review on computational analysis of big data in breast cancer for predicting potential biomarkers}, journal = {Current Topics in Medicinal Chemistry}, volume = {22}, year = {2022}, month = {SEP}, pages = {1793-1810}, type = {Review}, abstract = {

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.

}, keywords = {Big data, Biomarkers, Breast cancer, Driver genes, Network analysis, text mining}, issn = {1568-0266}, doi = {10.2174/1568026622666220907121942}, author = {Shaikh, Nilofer and Bapat, Sanket and Karthikeyan, Muthukumarasamy and Vyas, Renu} }