03538nas a2200289   4500008004100000022001400041245013400055210006900189260000800258300001400266490000700280520258100287653001002868653002102878653001602899653001402915653001602929653002202945653002202967100001802989700002803007700002003035700001503055700001803070700003303088856012703121       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  uhttp://library.ncl.res.in/content/design-novel-drug-molecules-using-informatics-rich-secondary-metabolites-analysis-indian02422nas a2200217   4500008004100000022001400041245013900055210006900194260000900263490000800272520162400280653002501904653002001929653002301949653001801972653002101990653002202011100002002033700002002053856013102073       2024                        eng d  a0167-732200aTranscriptome analysis and Structure-Based drug discovery identifies potential biofungicides for controlling Fusarium wilt in chickpea0 aTranscriptome analysis and StructureBased drug discovery identif  cAPR 0 v3993 a<p>
	Fusarium wilt caused by the fungal pathogen Fusarium oxysporum f.sp. ciceri (Foc) is a devastating chickpea disease. Foc is a soil-borne pathogen that invades plants through roots and can kill them in three to four weeks. Although Fusarium wilt can be controlled by soil solarization or fumigation with chemical fungicides, these are not always effective. Soil fumigation is also hazardous to the beneficial soil microflora, deteriorates soil health, and causes pollution. Hence, there is an urgent need to identify potent and environment-friendly biofungicides to control fungal pathogens. We employed transcriptome analysis and structure-based drug discovery approaches to identify potential biofungicides from four widely used medicinal plants: Lantana camara, Piper betel, Ricinus communis, and Azadirachta indica. Fusarium wilt-resistant and susceptible chickpea varieties were pathogeninoculated and grown under controlled conditions in a greenhouse. Transcriptome analysis was performed to identify pathogenicity-related differentially expressed genes (DEGs). Over 600 phytochemicals from the four medicinal plants and four chemical fungicides were considered for molecular docking against the predicted protein structures of the four most expressed pathogen DEGs. The phytochemicals with the best docking scores and the lowest predicted toxicity risk were considered for molecular dynamics (MD) simulation at 100 ns timescale, and 15 potential biofungicides were identified. This study paves the way for developing biofungicides with enhanced efficacy and safety to manage Fusarium wilt in chickpea.</p>
10aAntifungal molecules10aCicer arietinum10afusarium oxysporum10aMD Simulation10amedicinal plants10aMolecular docking1 aSahoo, Rosaleen1 aKadoo, Narendra  uhttp://library.ncl.res.in/content/transcriptome-analysis-and-structure-based-drug-discovery-identifies-potential-biofungicides