<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Karade, Divya</style></author><author><style face="normal" font="default" size="100%">Vijayasarathi, Durairaj</style></author><author><style face="normal" font="default" size="100%">Kadoo, Narendra</style></author><author><style face="normal" font="default" size="100%">Vyas, Renu</style></author><author><style face="normal" font="default" size="100%">Ingle, P. K.</style></author><author><style face="normal" font="default" size="100%">Karthikeyan, Muthukumarasamy</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Design of novel drug-like molecules using informatics rich secondary metabolites analysis of Indian medicinal and aromatic plants</style></title><secondary-title><style face="normal" font="default" size="100%">Combinatorial Chemistry &amp; High Throughput Screening</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Drugs</style></keyword><keyword><style  face="normal" font="default" size="100%">medicinal plants</style></keyword><keyword><style  face="normal" font="default" size="100%">Metabolites</style></keyword><keyword><style  face="normal" font="default" size="100%">scaffolds</style></keyword><keyword><style  face="normal" font="default" size="100%">text mining</style></keyword><keyword><style  face="normal" font="default" size="100%">virtual libraries</style></keyword><keyword><style  face="normal" font="default" size="100%">virtual screening</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2020</style></year><pub-dates><date><style  face="normal" font="default" size="100%">JAN</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">23</style></volume><pages><style face="normal" font="default" size="100%">1113-1131</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;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.&lt;/p&gt;
</style></abstract><issue><style face="normal" font="default" size="100%">10</style></issue><work-type><style face="normal" font="default" size="100%">Article</style></work-type><custom3><style face="normal" font="default" size="100%">&lt;p&gt;Foreign&lt;/p&gt;
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</style></custom4></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Karade, Divya</style></author><author><style face="normal" font="default" size="100%">Karade, Vikas</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">AIDrugApp: artificial intelligence-based Web-App for virtual screening of inhibitors against SARS-COV-2</style></title><secondary-title><style face="normal" font="default" size="100%">Journal  of Experimental &amp; Thereotical  Artificial  Intelligence </style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">ADME</style></keyword><keyword><style  face="normal" font="default" size="100%">deep neural network</style></keyword><keyword><style  face="normal" font="default" size="100%">drug designing</style></keyword><keyword><style  face="normal" font="default" size="100%">machine learning</style></keyword><keyword><style  face="normal" font="default" size="100%">Molecular docking</style></keyword><keyword><style  face="normal" font="default" size="100%">SARS-CoV-2</style></keyword><keyword><style  face="normal" font="default" size="100%">virtual screening</style></keyword><keyword><style  face="normal" font="default" size="100%">Web application</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2023</style></year><pub-dates><date><style  face="normal" font="default" size="100%">APR</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">35</style></volume><pages><style face="normal" font="default" size="100%">395-443</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;
	Currently, there is no effective cure for SARS-COVID-19 diseases. The identification of novel therapeutic targets and drug-like compounds is required for the development of anti-COVID-19 drugs. Virtual screening is currently the most significant component for identifying drug-like molecules from large datasets for drug design and development. However, there are no effective easily available and user-friendly applications for virtual screening of drug leads against SARS-COV-2. Therefore, we have developed a user-friendly web-app named `AIDrugApp' for the virtual screening of inhibitor molecules against SARS-CoV-2. AIDrugApp is a novel open-access, deep learning AI-based inhibitory activity prediction and data statistics visualisation platform. Users can predict the inhibitory activities (Active/Inactive) and pIC-50 values of new compounds against SARS-CoV-2 replicase polyprotein, 3CLpro and human angiotensin-converting enzymes. It is also useful for virtual screening of chemical features of molecules towards SARS-COVID-19 clinical trial bioactivities. This paper presents the development and architecture of AIDrugApp. We also present two case studies where large sets of molecules were screened using the `Bioactivity Prediction' module of our app. Screened molecules were analysed further for validation by molecular docking and ADME analysis to identify the potential drug candidates.&lt;/p&gt;
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