03785nas a2200781 4500008004100000022001400041245010300055210006900158260000800227300001200235490000800247520166900255653002201924653002101946653002801967653001401995653002602009653002502035653001402060653002302074653001402097653002302111653001702134653001202151653001702163653002502180653001302205100001702218700001702235700001502252700001302267700001802280700001702298700002102315700001402336700001802350700001302368700001602381700001702397700001902414700001402433700001402447700001402461700001402475700001602489700001802505700001802523700001502541700001902556700001402575700001902589700002002608700001602628700001502644700001802659700001702677700001802694700002002712700001802732700001402750700002002764700001902784700002002803700001502823700001802838700001702856856013002873 2017 eng d a0974-362600aAssessing therapeutic potential of molecules: molecular property diagnostic suite for tuberculosis0 aAssessing therapeutic potential of molecules molecular property  cMAY a515-5310 v1293 a

Abstract: Molecular Property Diagnostic Suite (MPDS TB) is a web tool (http://mpds.osdd.net) designed to assist the in silico drug discovery attempts towards Mycobacterium tuberculosis (Mtb). MPDS TB tool has nine modules which are classified into data library (1–3), data processing (4–5) and data analysis (6–9). Module 1 is a repository of literature and related information available on the Mtb. Module 2 deals with the protein target analysis of the chosen disease area. Module 3 is the compound library consisting of 110.31 million unique molecules generated from public domain databases and custom designed search tools. Module 4 contains tools for chemical file format conversions and 2D to 3D coordinate conversions. Module 5 helps in calculating the molecular descriptors. Module 6 specifically handles QSAR model development tools using descriptors generated in the Module 5. Module 7 integrates the AutoDock Vina algorithm for docking, while module 8 provides screening filters. Module 9 provides the necessary visualization tools for both small and large molecules. The workflow-based open source web portal, MPDS TB 1.0.1 can be a potential enabler for scientists engaged in drug discovery in general and in anti-TB research in particular. Graphical Abstract: SYNOPSIS: A web-based MPDS TB Galaxy tool is developed for assessing therapeutic potential of molecules. MPDS TB is categorized into Data Library, Data Processing and Data Analysis. It can be a potential enabler for scientists engaged in drug discovery in general and in anti-TB research in particular. [Figure not available: see fulltext.] © 2017, Indian Academy of Sciences.

10achemical analysis10aChemoinformatics10acomputational chemistry10aDiagnosis10aDrug discovery portal10aInformation analysis10aLibraries10aMolecular graphics10aMolecules10aNeglected diseases10aOpen science10aPortals10atuberculosis10aWeb-based technology10aWebsites1 aGaur, A., S.1 aBhardwaj, A.1 aSharma, A.1 aJohn, L.1 aVivek, M., R.1 aTripathi, N.1 aBharatam, P., V.1 aKumar, R.1 aJanardhan, S.1 aMori, A.1 aBanerji, A.1 aLynn, A., M.1 aHemrom, A., J.1 aPassi, A.1 aSingh, A.1 aKumar, A.1 aMuvva, C.1 aMadhuri, C.1 aChoudhury, C.1 aKumar, D., A.1 aPandit, D.1 aBharti, D., R.1 aKumar, D.1 aSingam, E., A.1 aRaghava, G., P.1 aSailaja, H.1 aJangra, H.1 aRaithatha, K.1 aTanneeru, K.1 aChaudhary, K.1 aKarthikeyan, M.1 aPrasanthi, M.1 aKumar, N.1 aYedukondalu, N.1 aRajput, N., K.1 aSaranya, P., S.1 aNarang, P.1 aDutta, Prantu1 aKrishnan, R. uhttps://library.ncl.res.in/content/assessing-therapeutic-potential-molecules-molecular-property-diagnostic-suite-tuberculosis01472nas a2200217 4500008004100000245009700041210006900138260000800207300002700215490000600242520073600248100002100984700001801005700001801023700001501041700002001056700001901076700001501095700002201110856012201132 2018 eng d00aIdentification of potent chromone embedded [1,2,3]-triazoles as novel anti-tubercular agents0 aIdentification of potent chromone embedded 123triazoles as novel cAPR aArticle Number: 1717500 v53 aA series of 20 novel chromone embedded [1,2,3]-triazoles derivatives were synthesized via an easy and convenient synthetic procedure starting from 2-hydroxy acetophenone. The in vitro anti-mycobacterial evaluation studies carried out in this work reveal that seven compounds exhibit significant inhibition against Mycobacterium tuberculosis H37Rv strain with MIC in the range of 1.56-12.5 mu g ml(-1). Noticeably, compound 6s was the most potent compound in vitro with a MIC value of 1.56 mu g ml(-1). Molecular docking and chemoinformatics studies revealed that compound 6s displayed drug-like properties against the enoyl-acyl carrier protein reductase of M. tuberculosis further establishing its potential as a potent inhibitor.1 aNalla, Viswanadh1 aShaikh, Aslam1 aBapat, Sanket1 aVyas, Renu1 aKarthikeyan, M.1 aYogeeswari, P.1 aSriram, D.1 aMuthukrishnan, M. uhttps://library.ncl.res.in/content/identification-potent-chromone-embedded-123-triazoles-novel-anti-tubercular-agents01175nas a2200217 4500008004100000022001300041245015400054210007100208260000800279300001400287490000700301520036800308100001800676700002500694700002200719700001900741700002200760700002000782700002200802856013300824 2018 eng d a1359734500aTransition metal free regio-selective C–H hydroxylation of chromanones towards the synthesis of hydroxyl-chromanones using PhI(OAc)2 as the oxidant0 aTransition metal free regioselective C–H hydroxylation of chroma cFEB a2252-22550 v543 a

The chromanone scaffold is considered as a privileged structure in drug discovery. Herein, we report a highly efficient PhI(OAc)2 mediated regioselective, direct C–H hydroxylation of chromanones. This method offers easy access to substituted 6-hydroxy chromanones in moderate to good isolated yields, thus paving the way for their pharmaceutical studies.

1 aViswanadh, N.1 aGhotekar, Ganesh, S.1 aThoke, Mahesh, B.1 aVelayudham, R.1 aShaikh, Aslam, C.1 aKarthikeyan, M.1 aMuthukrishnan, M. uhttps://library.ncl.res.in/content/transition-metal-free-regio-selective-c%E2%80%93h-hydroxylation-chromanones-towards-synthesis02862nas a2200229 4500008004100000245015400041210006900195260000800264300001200272490000700284520205300291100001402344700001802358700001602376700002002392700001902412700001502431700002002446700002202466700001502488856012902503 2019 eng d00aSynthesis, biological evaluation and molecular modeling studies of novel chromone/Aza-Chromone fused alpha-aminophosphonates as src kinase inhibitors0 aSynthesis biological evaluation and molecular modeling studies o cFEB a111-1170 v783 a

A series of novel chromone/aza-chromone fused alpha-aminophosphonate derivatives were synthesized in good yields using silica chloride as the catalyst. All the synthesized compounds were tested for their c-Src kinase inhibitory activity. Aza-chromone compound showed Src kinase inhibition with an IC50 value of 15.8 mu M. The compounds were subjected to molecular docking and dynamics simulations to study the atomic level interactions with an unphosphorylated proto-oncogenic tyrosine protein kinase Src (PDB code 1Y57) as well as phosphorylated tyrosine protein kinase Src (PDB code 2H8H). Docking and molecular dynamic results revealed phosphorylated Src tyrosine kinase protein better results than unphosphorylated tyrosine Src kinase protein. Chemoinformatics study revealed the compounds had lead like properties. Machine learning (SVR) models were built to study the structure activity correlations. A CC of 0.835 was obtained when the SVR model was applied to the 17 synthesized compounds. It is envisaged that the work will provide guidelines for future drug design efforts for Src kinase inhibitors.

1 aBapat, S.1 aViswanadh, N.1 aMujahid, M.1 aShirazi, A., N.1 aTiwari, R., K.1 aParang, K.1 aKarthikeyan, M.1 aMuthukrishnan, M.1 aVyas, Renu uhttps://library.ncl.res.in/content/synthesis-biological-evaluation-and-molecular-modeling-studies-novel-chromoneaza-chromone04467nas a2200181 4500008004100000245007600041210006900117260000800186490000700194520385200201100002004053700002504073700002304098700001504121700002104136700002004157856010804177 2024 eng d00aExplainable AI-assisted web application in cancer drug value prediction0 aExplainable AIassisted web application in cancer drug value pred cJUN0 v123 a
In recent years, there has been an increase in the interest in adopting Explainable Artificial Intelligence (XAI) for healthcare. The proposed system includes
While the model is evaluated on Lung Cancer data, the architecture offered in the proposed solution is cancer agnostic. It may be scaled out to other cancer cell data if the properties are similar. The work presents a viable route for customizing treatments and improving patient outcomes in oncology by combining XAI with a large dataset. This research attempts to create a framework where a user can upload a test case and receive forecasts with explanations, all in a portable PDF report.
1 aKothari, Sonali1 aSharma, Shivanandana1 aShejwal, Sanskruti1 aKazi, Aqsa1 aD'Silva, Michela1 aKarthikeyan, M. uhttps://library.ncl.res.in/content/explainable-ai-assisted-web-application-cancer-drug-value-prediction04606nas a2200193 4500008004100000245007900041210006900120260000800189300001100197490000700208520396300215100002004178700002504198700002304223700001504246700002104261700002004282856011004302 2024 eng d00aAn explainable AI-assisted web application in cancer drug value prediction0 aexplainable AIassisted web application in cancer drug value pred cJUN a1026960 v123 a
In recent years, there has been an increase in the interest in adopting Explainable Artificial Intelligence (XAI) for healthcare. The proposed system includes
While the model is evaluated on Lung Cancer data, the architecture offered in the proposed solution is cancer agnostic. It may be scaled out to other cancer cell data if the properties are similar. The work presents a viable route for customizing treatments and improving patient outcomes in oncology by combining XAI with a large dataset. This research attempts to create a framework where a user can upload a test case and receive forecasts with explanations, all in a portable PDF report.
1 aKothari, Sonali1 aSharma, Shivanandana1 aShejwal, Sanskruti1 aKazi, Aqsa1 aD'Silva, Michela1 aKarthikeyan, M. uhttps://library.ncl.res.in/content/explainable-ai-assisted-web-application-cancer-drug-value-prediction-0