02047nas a2200205 4500008004100000022001400041245013100055210006900186260010600255300001600361490000700377520119900384653001901583653002401602653002101626653002901647100002401676700001601700856012501716 2010 eng d a0360-319900aModel study of effect of M = Li+, Na+, Be2+, Mg2+, and Al3+ ion decoration on hydrogen adsorption of metal-organic framework-50 aModel study of effect of M Li Na Be2 Mg2 and Al3 ion decoration  aTHE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLANDbPERGAMON-ELSEVIER SCIENCE LTDcDEC a12846-128570 v353 a

The effect of light metal ion decoration of the organic linker in metal organic framework MOF 5 on its hydrogen adsorption with respect to its hydrogen binding energy (Delta B E) and gravimetric storage capacity is examined theoretically by employing models of the form MC6H6 nH(2) where M = Li+ Na+ Be2+ Mg2+ and Al3+ A systematic investigation of the suitability of DFT functionals for studying such systems is also carried out Our results show that the interaction energy (Delta E) of the metal ion M with the benzene ring Delta B E and charge transfer (Q(trans)) from the metal to benzene ring exhibit the same increasing order Na+ < Li+ < Mg2+ < Be2+ < Al3+ Organic hnker decoration with the above metal ions strengthened H-2 MOF 5 interactions relative to its pure state However amongst these ions only Mg2+ ion resulted in Delta B E magnitudes that were optimal for allowing room temperature hydrogen storage applications of MOF 5 A much higher gravimetric storage capacity (6 15 wt % H-2) is also predicted for Mg2+ decorated MOF 5 as compared to both pure MOF 5 and Li+ decorated MOF (C) 2010 Professor T Nejat Veziroglu Published by Elsevier Ltd All rights reserved

10aBinding energy10aHydrogen adsorption10aHydrogen storage10aMetal organic frameworks1 aMaark, Tuhina, Adit1 aPal, Sourav uhttps://library.ncl.res.in/content/model-study-effect-m-li-na-be2-mg2-and-al3-ion-decoration-hydrogen-adsorption-metal-002226nas a2200253 4500008004100000022001400041245010200055210006900157260011100226300001200337490000700349520123600356653001901592653001201611653003101623653003801654653001801692653003901710100001501749700003301764700002001797700002701817856012801844 2015 eng d a1386-207300aPharmacophore and docking based virtual screening of validated mycobacterium tuberculosis targets0 aPharmacophore and docking based virtual screening of validated m aEXECUTIVE STE Y-2, PO BOX 7917, SAIF ZONE, 1200 BR SHARJAH, U ARAB EMIRATESbBENTHAM SCIENCE PUBL LTDcAUG a624-6370 v183 a

Target based virtual screening has surpassed ligand based virtual screening methods in the recent past mainly as it provides more clues regarding intermolecular interactions and takes into consideration the flexible receptor as well. The current methodology describes a computational strategy of predicting Mycobacterium tuberculosis (M. tuberculosis) binders for five well studied targets representing M. tuberculosis proteome encompassing most of the known mechanisms of action. The diversity of the targets was affirmed by their active site analysis and structural studies. The current approach employed pharmacophore searching, docking and clustering techniques in tandem and was validated by enrichment studies using the available Schrodinger data set consisting of 1000 decoys. The application of this methodology was demonstrated by predicting potential molecular targets for fifty newly synthesized compounds. Cross docking studies on the targets were carried out with 4512 known inhibitors utilizing a high performance computing platform to reveal underlying affinity and promiscuity patterns. Optimum binding energy range for all targets as determined by high throughput docking was found to be -3 to -13 kcal/mol.

10aBinding energy10adocking10aMycobacterium tuberculosis10aopen source drug discovery (OSDD)10apharmacophore10astructure based drug design (SBDD)1 aVyas, Renu1 aKarthikeyan, Muthukumarasamy1 aNainaru, Ganesh1 aMuthukrishnan, Murugan uhttps://library.ncl.res.in/content/pharmacophore-and-docking-based-virtual-screening-validated-mycobacterium-tuberculosis-003075nas a2200265 4500008004100000022001400041245008500055210006900140260009200209300001200301490000700313520211500320653001902435653002602454653003002480653002202510653002802532653003002560653001602590100001702606700002602623700001702649700002702666856011602693 2016 eng d a0892-702200aDFT based assay for tailor-made terpyridine ligand-metal complexation properties0 aDFT based assay for tailormade terpyridine ligandmetal complexat a4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXON, ENGLANDbTAYLOR & FRANCIS LTDcMAY a618-6270 v423 a

Electron-rich terpyridine ligand and its metal complexes have a potential to grow as responsive surfaces by adapting their physicochemical properties as a function of environment. The responsiveness is brought about by judicious molecular level designing that is currently hindered due to lack of information and control on terpyridine (TPy)-metal (M) interactions at single molecule level. So far there is no organised understanding on the binding of different metals with TPy ligand and ways to modulate it. Being a large conjugated [GRAPHICS] system, TPy has a large scope to be functionalised with electron exchanging groups to alter its electronic structure and consequently its binding with metal atoms. In first report of such a kind, using density functional theory (DFT), we demonstrate that convenient modulation of TPy-M binding is possible through functionalisation of TPy for [GRAPHICS] , Ru, Fe, Mo and Au. Electron donating groups viz., CH [GRAPHICS] , OCH [GRAPHICS] , C [GRAPHICS] H [GRAPHICS] , NH [GRAPHICS] and electron withdrawing groups viz., CF [GRAPHICS] , COOH, CN and NO [GRAPHICS] are considered for functionalisation of TPy ligand. Significantly, the present work focuses on the functionalisation at 4 and 4 [GRAPHICS] positions of TPy molecule. The role of such a functionalisation in influencing the ligands structure-property correlation is missing in the literature to the best of our knowledge. The present investigation quantifies that by pertinent functionalisation of TPy, TPy-M binding energies can be modified up to [GRAPHICS] 60kcal/mol. Our results reveal that functionalisation leads to a considerable charge redistribution within the TPy-M complex with carbon atoms in pyridine rings functioning as major electron sink/source with a corresponding red/blue shift of [GRAPHICS] stretching frequency. This modifies the red-ox, optical and other chemical properties of TPy-M complexes. In brief, the present report illustrates a way to design ligands such as TPy for diverse applications through tailor-made functionalisation using electronic structure methodology.

10aBinding energy10acharge redistribution10aDensity functional theory10afunctionalisation10aMetal organic complexes10ametal-ligand interactions10aterpyridine1 aJoshi, Krati1 aKrishnamurty, Sailaja1 aSingh, Iksha1 aSelvaraj, Kaliaperumal uhttps://library.ncl.res.in/content/dft-based-assay-tailor-made-terpyridine-ligand-metal-complexation-properties02246nas a2200289 4500008004100000022001400041245013800055210006900193260000800262300001000270490000700280520126700287653001901554653001101573653001201584653002401596653002101620653003301641653002401674100001501698700001801713700001601731700003301747700002301780700002601803856012701829 2018 eng d a1545-596300aApplication of genetic programming (GP) formalism for building disease predictive models from protein-protein interactions (PPI) data0 aApplication of genetic programming GP formalism for building dis cFEB a27-370 v153 a

Protein-protein interactions (PPIs) play a vital role in the biological processes involved in the cell functions and disease pathways. The experimental methods known to predict PPIs require tremendous efforts and the results are often hindered by the presence of a large number of false positives. Herein, we demonstrate the use of a new Genetic Programming (GP) based Symbolic Regression (SR) approach for predicting PPIs related to a disease. In this case study, a dataset consisting of 135 PPI complexes related to cancer was used to construct a generic PPI predicting model with good PPI prediction accuracy and generalization ability. A high correlation coefficient (CC) magnitude of 0.893, and low root mean square error (RMSE), and mean absolute percentage error (MAPE) values of 478.221 and 0.239, respectively, were achieved for both the training and test set outputs. To validate the discriminatory nature of the model, it was applied on a dataset of diabetes complexes where it yielded significantly low CC values. Thus, the GP model developed here serves a dual purpose: (a) a predictor of the binding energy of cancer related PPI complexes, and (b) a classifier for discriminating PPI complexes related to cancer from those of other diseases.

10aBinding energy10acancer10aDisease10agenetic programming10amachine learning10aprotein-protein interactions10asymbolic regression1 aVyas, Renu1 aBapat, Sanket1 aGoel, Purva1 aKarthikeyan, Muthukumarasamy1 aTambe, Sanjeev, S.1 aKulkarni, Bhaskar, D. uhttps://library.ncl.res.in/content/application-genetic-programming-gp-formalism-building-disease-predictive-models-protein