02659nas a2200277 4500008004100000022001400041245011000055210006900165260011100234300001200345490000700357520164400364653000902008653001202017653002202029653003602051653002602087653002202113653000902135100002202144700001902166700002902185700002202214700002502236856012002261 2016 eng d a1386-207300aVirtual screening techniques to probe the antimalarial activity of some traditionally used phytochemicals0 aVirtual screening techniques to probe the antimalarial activity  aEXECUTIVE STE Y-2, PO BOX 7917, SAIF ZONE, 1200 BR SHARJAH, U ARAB EMIRATESbBENTHAM SCIENCE PUBL LTDcJUL a572-5910 v193 a

Malaria parasites show resistance to most of the antimalarial drugs and hence developing antimalarials which can act on multitargets rather than a single target will be a promising strategy of drug design. Here we report a new approach by which virtual screening of 292 unique phytochemicals present in 72 traditionally important herbs is used for finding out inhibitors of plasmepsin-2 and falcipain-2 for antimalarial activity against P. falciparum. Initial screenings of the selected molecules by Random Forest algorithm model of Weka using the bioassay datasets AID 504850 and AID 2302 screened 120 out of the total 292 phytochemicals to be active against the targets. Toxtree scan cautioned 21 compounds to be either carcinogenic or mutagenic and were thus removed for further analysis. Out of the remaining 99 compounds, only 46 compounds offered drug-likeness as per the `rule of five' criteria. Out of ten antimalarial drug targets, only two target proteins such as 3BPF and 3PNR of falcipain-2 and 1PFZ and 2BJU of plasmepsin-2 are selected as targets. The potential binding of the selected 46 compounds to the active sites of these four targets was analyzed using MOE software. The docked conformations and the interactions with the binding pocket residues of the target proteins were understood by `Ligplot' analysis. It has been found that 8 compounds are dual inhibitors of falcipain-2 and plasmepsin-2, with the best binding energies. Compound 117 (6aR, 12aS)-12a-Hydroxy-9-methoxy-2,3-dimethylenedioxy-8-prenylrotenone (Usaratenoid C) present in the plant Millettia usaramensis showed maximum molecular docking score.

10aADME10amalaria10aMolecular docking10amolecular operating environment10aPlasmodium falciparum10avirtual screening10aweka1 aShibi, Indira, G.1 aAswathy, Lilly1 aJisha, Radhakrishnan, S.1 aMasand, Vijay, H.1 aGajbhiye, Jayant, M. uhttps://library.ncl.res.in/content/virtual-screening-techniques-probe-antimalarial-activity-some-traditionally-used02677nas a2200265 4500008004100000022001400041245016300055210006900218260000800287300001400295490000700309520173600316653000902052653002502061653001202086653001902098653001302117653002202130100002402152700002802176700002202204700002102226700003402247856013002281 2021 eng d a0739-110200aPharmacore maping based on docking, ADME/toxicity, virtual screening on 3,5-dimethyl-1,3,4-hexanetriol and dodecanoic acid derivates for anticancer inhibitors0 aPharmacore maping based on docking ADMEtoxicity virtual screenin cJAN a4490-45000 v393 a

Plants produced natural generating products play a significant role in drug discovery of new bioactive compounds and these are used for advancement of innovative curative drugs for specific target health diseases. In this study Docking and ADME/T virtual screening method are apply for in drug discovery and can be divided into ligand- and target structure-based. The aim of this study was to analyze theDecalepis hamiltoniiisolated compounds by using the evaluation of molecular docking and virtual screening of anticancer drugs. MOE docking ADME/Toxicity and virtual screening approaches. A docking energy -12.97 kcal/mol; -9.93- kcal/mol on cancer responsible protein was targeted. Further, the compounds were filtered through the rule of five, ADME/Toxicity risk and synthetic accessibility. The active compound were then docked to recognize the possible target binding pocket to obtain a set of a ligand poses and to prioritize the predicted active compounds. The scrutinize compounds, as well as their metabolites were evaluated for different pharmacokinetics parameter such as ADME/Toxicity. Therefore, the result shows that a large number of compounds were found to be ADME/toxicity positive to be a positive drug molecule against cancer, selected compounds under study satisfies parameters for ADME and Toxicity properties. The present study demonstrate to identifying the novel structures which are having similar structural feature with like activity with respect to the compounds 3,5-Dimethyl-1,3,4-Hexanetriol and Dodecanoic acid that are shown best binding energy with the receptors 4igk and 4b3z respectively. This study may provide significant clues for discovery novel drug inhibitors for cancer properties.

10aADME10aDecalepis hamiltonii10adocking10adrug discovery10aToxicity10avirtual screening1 aPrakash, Palanisamy1 aVijayasarathi, Durairaj1 aSelvam, Kuppusamy1 aKarthi, Sengodan1 aManivasagaperumal, Rengarajan uhttps://library.ncl.res.in/content/pharmacore-maping-based-docking-admetoxicity-virtual-screening-35-dimethyl-134-hexanetriol02249nas a2200253 4500008004100000022001400041245010800055210006900163260000800232300001200240490000700252520141900259653000901678653002401687653001901711653002101730653002201751653001501773653002201788653002001810100001801830700001801848856012901866 2023 eng d a0952-813X00aAIDrugApp: artificial intelligence-based Web-App for virtual screening of inhibitors against SARS-COV-20 aAIDrugApp artificial intelligencebased WebApp for virtual screen cAPR a395-4430 v353 a

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.

10aADME10adeep neural network10adrug designing10amachine learning10aMolecular docking10aSARS-CoV-210avirtual screening10aWeb application1 aKarade, Divya1 aKarade, Vikas uhttps://library.ncl.res.in/content/aidrugapp-artificial-intelligence-based-web-app-virtual-screening-inhibitors-against-sars