02120nas a2200217 4500008004100000022001400041245008400055210006900139260011100208300001200319490000700331520128100338653002401619653002501643653003101668653002201699100003301721700001901754700001501773856011401788 2015 eng d a1386-207300aChemScreener: a distributed computing tool for scaffold based virtual screening0 aChemScreener a distributed computing tool for scaffold based vir aEXECUTIVE STE Y-2, PO BOX 7917, SAIF ZONE, 1200 BR SHARJAH, U ARAB EMIRATESbBENTHAM SCIENCE PUBL LTDcJAN a544-5610 v183 a

In this work we present ChemScreener, a Java-based application to perform virtual library generation combined with virtual screening in a platform-independent distributed computing environment. ChemScreener comprises a scaffold identifier, a distinct scaffold extractor, an interactive virtual library generator as well as a virtual screening module for subsequently selecting putative bioactive molecules. The virtual libraries are annotated with chemophore-, pharmacophore- and toxicophore-based information for compound prioritization. The hits selected can then be further processed using QSAR, docking and other in silico approaches which can all be interfaced within the ChemScreener framework. As a sample application, in this work scaffold selectivity, diversity, connectivity and promiscuity towards six important therapeutic classes have been studied. In order to illustrate the computational power of the application, 55 scaffolds extracted from 161 anti-psychotic compounds were enumerated to produce a virtual library comprising 118 million compounds (17 GB) and annotated with chemophore, pharmacophore and toxicophore based features in a single step which would be non-trivial to perform with many standard software tools today on libraries of this size.

10aScaffold extraction10atherapeutic category10avirtual library generation10avirtual screening1 aKarthikeyan, Muthukumarasamy1 aPandit, Deepak1 aVyas, Renu uhttps://library.ncl.res.in/content/chemscreener-distributed-computing-tool-scaffold-based-virtual-screening-002057nas 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

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.

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

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.

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-001949nas a2200229 4500008004100000022001400041245007300055210006900128260011100197300001200308490000700320520110300327653001901430653001701449653000801466653002501474653002201499100003301521700004301554700001501597856010701612 2015 eng d a1386-207300aPrediction of bioactive compounds using computed NMR chemical shifts0 aPrediction of bioactive compounds using computed NMR chemical sh aEXECUTIVE STE Y-2, PO BOX 7917, SAIF ZONE, 1200 BR SHARJAH, U ARAB EMIRATESbBENTHAM SCIENCE PUBL LTDcJAN a562-5760 v183 a

NMR based chemical shifts are an important diagnostic parameter for structure elucidation as they capture rich information related to conformational, electronic and stereochemical arrangement of functional groups in a molecule which is responsible for its activity towards any biological target. The present work discusses the importance of computing NMR chemical shifts from molecular structures. The NMR chemical shift data (experimental or computed) was used to generate fingerprints in binary formats for mapping molecular fragments (as descriptors) and correlating with the bioactivity classes. For this study, chemical shift data derived binary fingerprints were computed for 149 classes and 4800 bioactive molecules. The sensitivity and selectivity of fingerprints in discriminating molecules belonging to different therapeutic categories was assessed using a LibSVM based classifier. An accuracy of 82% for proton and 94% for carbon NMR fingerprints were obtained for anti-psoriatic and anti-psychotic molecules demonstrating the effectiveness of this approach for virtual screening.

10aChemical shift10afingerprints10aNMR10asimilarity searching10avirtual screening1 aKarthikeyan, Muthukumarasamy1 aRajamohanan, Pattuparambil, Ramanpilla1 aVyas, Renu uhttps://library.ncl.res.in/content/prediction-bioactive-compounds-using-computed-nmr-chemical-shifts-002219nas a2200253 4500008004100000022001400041245006500055210006500120260011100185300001200296490000700308520137700315653001401692653001101706653001201717653001401729653001401743653002101757653002201778100003301800700001901833700001501852856009801867 2015 eng d a1386-207300aProtein ligand complex guided approach for virtual screening0 aProtein ligand complex guided approach for virtual screening aEXECUTIVE STE Y-2, PO BOX 7917, SAIF ZONE, 1200 BR SHARJAH, U ARAB EMIRATESbBENTHAM SCIENCE PUBL LTDcJAN a577-5900 v183 a

The target ligand association data is a rich source of information which is not exploited enough for drug design efforts in virtual screening. A java based open-source toolkit for Protein Ligand Network Extraction (J-ProLiNE) focused on protein-ligand complex analysis with several features integrated in a distributed computing network has been developed. Sequence alignment and similarity search components have been automated to yield local, global alignment scores along with similarity and distance scores. 10000 proteins with co-crystallized ligands from pdb and MOAD databases were extracted and analyzed for revealing relationships between targets, ligands and scaffolds. Through this analysis, we could generate a protein ligand network to identify the promiscuous and selective scaffolds for multiple classes of proteins targets. Using J-ProLiNE we created a 507 x 507 matrix of protein targets and native ligands belonging to six enzyme classes and analyzed the results to elucidate the protein-protein, protein-ligand and ligand-ligand interactions. In yet another application of the J-ProLiNE software, we were able to process kinase related information stored in US patents to construct disease-gene-ligand-scaffold networks. It is hoped that the studies presented here will enable target ligand knowledge based virtual screening for inhibitor design.

10aComplexes10aligand10aprotein10ascaffolds10asequences10asimilarity score10avirtual screening1 aKarthikeyan, Muthukumarasamy1 aPandit, Deepak1 aVyas, Renu uhttps://library.ncl.res.in/content/protein-ligand-complex-guided-approach-virtual-screening-002540nas a2200301 4500008004100000022001400041245008400055210006900139260011100208300001200319490000700331520149700338653001701835653001801852653002301870653002301893653002401916653001301940653001401953653001401967653002201981100003302003700001502036700002302051700002402074700002602098856011402124 2015 eng d a1386-207300aRole of chemical reactivity and transition state modeling for virtual screening0 aRole of chemical reactivity and transition state modeling for vi aEXECUTIVE STE Y-2, PO BOX 7917, SAIF ZONE, 1200 BR SHARJAH, U ARAB EMIRATESbBENTHAM SCIENCE PUBL LTDcJAN a638-6570 v183 a

Every drug discovery research program involves synthesis of a novel and potential drug molecule utilizing atom efficient, economical and environment friendly synthetic strategies. The current work focuses on the role of the reactivity based fingerprints of compounds as filters for virtual screening using a tool ChemScore. A reactant-like (RLS) and a product-like (PLS) score can be predicted for a given compound using the binary fingerprints derived from the numerous known organic reactions which capture the molecule-molecule interactions in the form of addition, substitution, rearrangement, elimination and isomerization reactions. The reaction fingerprints were applied to large databases in biology and chemistry, namely ChEMBL, KEGG, HMDB, DSSTox, and the Drug Bank database. A large network of 1113 synthetic reactions was constructed to visualize and ascertain the reactant product mappings in the chemical reaction space. The cumulative reaction fingerprints were computed for 4000 molecules belonging to 29 therapeutic classes of compounds, and these were found capable of discriminating between the cognition disorder related and anti-allergy compounds with reasonable accuracy of 75% and AUC 0.8. In this study, the transition state based fingerprints were also developed and used effectively for virtual screening in drug related databases. The methodology presented here provides an efficient handle for the rapid scoring of molecular libraries for virtual screening.

10afingerprints10aintermediates10ametabolic pathways10aproduct-like score10areactant-like score10areaction10ascreening10asynthesis10avirtual screening1 aKarthikeyan, Muthukumarasamy1 aVyas, Renu1 aTambe, Sanjeev, S.1 aRadhamohan, Deepthi1 aKulkarni, Bhaskar, D. uhttps://library.ncl.res.in/content/role-chemical-reactivity-and-transition-state-modeling-virtual-screening-002126nas a2200217 4500008004100000022001400041245008400055210006900139260011100208300001200319490000700331520132700338653002101665653001201686653001601698653001301714653002201727100003301749700001501782856011101797 2015 eng d a1386-207300aRole of open source tools and resources in virtual screening for drug discovery0 aRole of open source tools and resources in virtual screening for aEXECUTIVE STE Y-2, PO BOX 7917, SAIF ZONE, 1200 BR SHARJAH, U ARAB EMIRATESbBENTHAM SCIENCE PUBL LTDcJUL a528-5430 v183 a

Advancement in chemoinformatics research in parallel with availability of high performance computing platform has made handling of large scale multi-dimensional scientific data for high throughput drug discovery easier. In this study we have explored publicly available molecular databases with the help of open-source based integrated in-house molecular informatics tools for virtual screening. The virtual screening literature for past decade has been extensively investigated and thoroughly analyzed to reveal interesting patterns with respect to the drug, target, scaffold and disease space. The review also focuses on the integrated chemoinformatics tools that are capable of harvesting chemical data from textual literature information and transform them into truly computable chemical structures, identification of unique fragments and scaffolds from a class of compounds, automatic generation of focused virtual libraries, computation of molecular descriptors for structure-activity relationship studies, application of conventional filters used in lead discovery along with in-house developed exhaustive PTC (Pharmacophore, Toxicophores and Chemophores) filters and machine learning tools for the design of potential disease specific inhibitors. A case study on kinase inhibitors is provided as an example.

10aChemoinformatics10akinases10aopen source10ascaffold10avirtual screening1 aKarthikeyan, Muthukumarasamy1 aVyas, Renu uhttps://library.ncl.res.in/content/role-open-source-tools-and-resources-virtual-screening-drug-discovery-002659nas 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-used03539nas 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

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.

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-indian01754nas a2200241 4500008004100000022001400041245010500055210006900160260000900229300001100238490000700249520094300256653001601199653002101215653001801236653002501254653002201279100002101301700002601322700002001348700002401368856012001392 2021 eng d a0960-894X00aMachine learning classifiers aid virtual screening for efficient design of mini-protein therapeutics0 aMachine learning classifiers aid virtual screening for efficient cAPR  a1278520 v383 a

De novo design of mini-proteins (4-12 kDa) has recently been shown to produce new candidates for protein therapeutics. They are temperature stable molecules that bind to the drug target with high affinity for inhibiting its interactions. The development of mini-protein binders requires laboratory screening of tens of thousands of molecules for effective target binding. In this study we trained machine learning classifiers which can distinguish, with 90% accuracy and 80% precision, mini-protein binders from non-binding molecules designed for a particular target; this significantly reduces the number of mini protein candidates for experimental screening. Further, on the basis of our results we propose a multi-stage protocol where a small dataset (few hundred experimentally verified target-specific mini-proteins) can be used to train classifiers for improving the efficiency of mini-protein design for any specific target.

10aDrug design10amachine learning10aMini-proteins10aProtein therapeutics10avirtual screening1 aGaur, Neeraj, K.1 aGoyal, Venuka, Durani1 aKulkarni, Kiran1 aMakde, Ravindra, D. uhttps://library.ncl.res.in/content/machine-learning-classifiers-aid-virtual-screening-efficient-design-mini-protein02695nas a2200265 4500008004100000022001400041245015600055210006900211260000800280300001200288490000700300520175900307653001502066653001502081653003102096653002202127653002702149653002702176653001802203653001702221653002202238100002702260700001902287856012302306 2021 eng d a0739-110200aPharmacophore modeling of pretomanid (PA-824) derivatives for antitubercular potency against replicating and non-replicating Mycobacterium tuberculosis0 aPharmacophore modeling of pretomanid PA824 derivatives for antit cFEB a889-9000 v393 a

Pretomanid (PA-824) is the recently (2019) approved drug for the treatment of extensively drug-resistant (XDR) TB and the multidrug-resistant (MDR) TB by US FDA. The experimental data of antitubercular activity of 543 pretomanid derivatives (total 6 datasets) against replicating (active) and non-replicating (dormant) forms of Mycobacterium tuberculosis (strain H37Rv) are available in the literature. Such vast experimental data of pretomanid derivatives against both of these endpoints, and recent approval of pretomanid molecule as a drug encouraged us to utilize this existing experimental information for the development of the 3D-pharmacophore models. The developed model (Hypo-1, MABA) showed the three physicochemical features namely, the oxygen atom of nitro group (HBA_1), fused pyran ring of imidazopyran heterocycle (HYAl_2) and the 4-fluorophenyl moiety (HYAr_3) are crucial for the antitubercular activity against replicating M. tb. Subsequently, the pharmacophore model (Hypo-1, LORA) developed against the non-replicating form of M. tb also showed the contribution of three physicochemical features namely, the 4-tri-fluoromethyl group (HYAl_2) and both the phenyl groups (HYAr_3, HYAr_4) of biaryl moiety in increasing the antitubercular activity. Both the pharmacophoric classifier models showed the classification accuracies of 82.98 and 74.42% for the training set compounds, and 63.91 and 61.60% for the test set compounds respectively, for labelling the compounds into higher and lower active classes. Both the models were also found to be retaining the higher active compounds in top 1.00% of the total number of compounds (decoys and actives), after performing the decoy set screening. Communicated by Ramaswamy H. Sarma

10aLORA assay10aMABA assay10aMycobacterium tuberculosis10anon-replicating M10apharmacophore modeling10apretomanid derivatives10areplicating M10atuberculosis10avirtual screening1 aAher, Rahul, Balasaheb1 aSarkar, Dhiman uhttps://library.ncl.res.in/content/pharmacophore-modeling-pretomanid-pa-824-derivatives-antitubercular-potency-against02677nas 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-sars02768nas a2200265 4500008004100000022001400041245013800055210006900193260000900262300001400271490000700285520182600292653002702118653002102145653002202166653003002188653004202218653002202260100002002282700002002302700000502322700003302327700001502360856012702375 2023 eng d a0739-110200aComprehensive molecular docking and dynamic simulations for drug repurposing of clinical drugs against multiple cancer kinase targets0 aComprehensive molecular docking and dynamic simulations for drug cNOV  a7735-77430 v413 a

Drug repurposing is a method to identify novel therapeutic agents from the existing drugs and clinical compounds. In the present comprehensive work, molecular docking, virtual screening and dynamics simulations were carried out for ten cancer types viz breast, colon, central nervous system, leukaemia, melanoma, ovarian, prostate, renal and lung (non-small and small cell) against validated eighteen kinase targets. The study aims to understand the action of chemotherapy drugs mechanism through binding interactions against selected targets via comparative docking simulations with the state-art molecular modelling suits such as MOE, Cresset-Flare, AutoDock Vina, GOLD and GLIDE. Chemotherapeutic drugs (n = 112) were shortlisted from standard drug databases with appropriate chemoinformatic filters. Based on docking studies it was revealed that leucovorin, nilotinib, ellence, thalomid and carfilzomib drugs possessed potential against other cancer targets. A library was built to enumerate novel molecules based on the scaffold and functional groups extracted from known drugs and clinical compounds. Twenty novel molecules were prioritised further based on drug-like attributes. These were cross docked against 1MQ4 Aurora-A Protein Kinase for prostate cancer and 4UYA Mitogen-activated protein kinase for renal cancer. All docking programs yielded similar results but interestingly AutoDock Vina yielded the lowest RMSD with the native ligand. To further validate the final docking results at atomistic level, molecular dynamics simulations were performed to ascertain the stability of the protein-ligand complex. The study enables repurposing of drugs and lead identification by employing a host of structure and ligand based virtual screening tools and techniques. Communicated by Ramaswamy H. Sarma

10achemotherapeutic drugs10aDrug repurposing10aMolecular docking10amolecular simulation (MD)10astructure-based drug designing (SBDD)10avirtual screening1 aShaikh, Nilofer1 aLinthoi, R., K.1 a1 aKarthikeyan, Muthukumarasamy1 aVyas, Renu uhttps://library.ncl.res.in/content/comprehensive-molecular-docking-and-dynamic-simulations-drug-repurposing-clinical-drugs