Abstract
Molecular Property Diagnostic Suite (\(\text {MPDS}^{\mathrm{TB}}\)) is a web tool (http://mpds.osdd.net) designed to assist the in silico drug discovery attempts towards Mycobacterium tuberculosis (Mtb). \(\text {MPDS}^{\mathrm{TB}}\) 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, \(\text {MPDS}^{\mathrm{TB}}\) 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 \(\text {MPDS}^{\mathrm{TB}}\) Galaxy tool is developed for assessing therapeutic potential of molecules. \(\text {MPDS}^{\mathrm{TB}}\) 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.
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References
Searls D B 2005 Data integration: Challenges for drug discovery Nat. Rev. Drug Discovery 4 45
Nwaka S, Ramirez B, Brun R, Maes L, Douglas F and Ridley R 2009 Advancing drug innovation for neglected diseases-criteria for lead progression PLoS Negl. Trop. Dis. 3 e440
Sachs J D 2001 A new global commitment to disease control in Africa Nat. Med. 7 521
Jagarlapudi S A and Kishan K V 2009 Database systems for knowledge-based discovery Methods Mol. Biol. 575 159
Winter M J, Owen S F, Murray-Smith R, Panter G H, Hetheridge M J and Kinter L B 2010 Using data from drug discovery and development to aid the aquatic environmental risk assessment of human pharmaceuticals: Concepts, considerations, and challenges Integr. Environ. Assess Manage. 6 38
Lushington G H, Dong Y and Theertham B 2013 Chemical informatics and the drug discovery knowledge pyramid Comb. Chem. High Throughput Screening 16 764
Bajorath J 2017 Compound Data Mining for Drug Discovery Methods Mol. Biol. 1526 247
Boran A D and Iyengar R 2010 Systems approaches to polypharmacology and drug discovery Curr. Opin. Drug Discovery Dev. 13 297
Badrinarayan P and Sastry G N 2011 Virtual high throughput screening in new lead identification Comb. Chem. High Throughput Screening 14 840
Reddy A S, Pati S P, Kumar P P, Pradeep H N and Sastry G N 2007 Virtual screening in drug discovery – a computational perspective Curr. Protein Pept. Sci. 8 329
Collins P Y, Patel V, Joestl S S, March D, Insel T R, Daar A S; Scientific Advisory Board and the Executive Committee of the Grand Challenges on Global Mental Health, Anderson W, Dhansay M A, Phillips A, Shurin S, Walport M, Ewart W, Savill S J, Bordin I A, Costello E J, Durkin M, Fairburn C, Glass R I, Hall W, Huang Y, Hyman S E, Jamison K, Kaaya S, Kapur S, Kleinman A, Ogunniyi A, Otero-Ojeda A, Poo M M, Ravindranath V, Sahakian B J, Saxena S, Singer P A and Stein D J 2011 Grand challenges in global mental health Nature 475 27
Varmus H, Klausner R, Zerhouni E, Acharya T, Daar A S and Singer P A 2003 Public health. Grand Challenges in Global Health Science 302 398
Paul S M, Mytelka D S, Dunwiddie C T, Persinger C C, Munos B H, Lindborg S R and Schacht A L 2010 How to improve R&D productivity: The pharmaceutical industry’s grand challenge Nat. Rev. Drug Discov. 9 203
Dubois D J 2010 Grand Challenges in Pharmacoeconomics and Health Outcomes Front. Pharmacol. 1 7
Yildirim O, Gottwald M, Schüler P and Michel MC M 2016 Opportunities and Challenges for Drug Development: Public–Private Partnerships, Adaptive Designs and Big Data Front. Pharmacol. 7 461
Gostin L O and Mok E A 2009 Grand challenges in global health governance Br. Med. Bull. 90 78
Pai M, Daftary A and Satyanarayana S 2016 TB control: Challenges and opportunities for India Trans. R. Soc. Trop. Med. Hyg. 110 158
Wells T N, Willis P, Burrows J N and Hooft V H R 2016 Open data in drug discovery and development: Lessons from malaria Nat. Rev. Drug Discov. 15 661
Van Voorhis W C, Adams J H, Adelfio R, Ahyong V, Akabas M H, Alano P, Alday A, Alemán Resto Y, Alsibaee A, Alzualde A, Andrews K T, Avery S V, Avery V M, Ayong L, Baker M, Baker S, Ben Mamoun C, Bhatia S, Bickle Q, Bounaadja L, Bowling T, Bosch J, Boucher L E, Boyom F F, Brea J, Brennan M, Burton A, Caffrey C R, Camarda G, Carrasquilla M, Carter D, Belen Cassera M, Chih-Chien Cheng K, Chindaudomsate W, Chubb A, Colon B L, Colón-López D D, Corbett Y, Crowther G J, Cowan N, D’Alessandro S, Le Dang N, Delves M, DeRisi J L, Du A Y, Duffy S, Abd El-Salam El-Sayed S, Ferdig M T, Fernández Robledo J A, Fidock D A, Florent I, Fokou P V, Galstian A, Gamo F J, Gokool S, Gold B, Golub T, Goldgof G M, Guha R, Guiguemde W A, Gural N, Guy R K, Hansen M A, Hanson K K, Hemphill A, Hooft van Huijsduijnen R, Horii T, Horrocks P, Hughes T B, Huston C, Igarashi I, Ingram-Sieber K, Itoe M A, Jadhav A, Naranuntarat Jensen A, Jensen L T, Jiang R H, Kaiser A, Keiser J, Ketas T, Kicka S, Kim S, Kirk K, Kumar V P, Kyle D E, Lafuente M J, Landfear S, Lee N, Lee S, Lehane A M, Li F, Little D, Liu L, Llinás M, Loza M I, Lubar A, Lucantoni L, Lucet I, Maes L, Mancama D, Mansour N R, March S, McGowan S, Medina Vera I, Meister S, Mercer L, Mestres J, Mfopa A N, Misra R N, Moon S, Moore J P, Morais Rodrigues da Costa F, Müller J, Muriana A, Nakazawa Hewitt S, Nare B, Nathan C, Narraidoo N, Nawaratna S, Ojo K K, Ortiz D, Panic G, Papadatos G, Parapini S, Patra K, Pham N, Prats S, Plouffe D M, Poulsen S A, Pradhan A, Quevedo C, Quinn R J, Rice C A, Abdo Rizk M, Ruecker A, St Onge R, Salgado Ferreira R, Samra J, Robinett N G, Schlecht U, Schmitt M, Silva Villela F, Silvestrini F, Sinden R, Smith D A, Soldati T, Spitzmüller A, Stamm S M, Sullivan D J, Sullivan W, Suresh S, Suzuki B M, Suzuki Y, Swamidass S J, Taramelli D, Tchokouaha L R, Theron A, Thomas D, Tonissen K F, Townson S, Tripathi A K, Trofimov V, Udenze K O, Ullah I, Vallieres C, Vigil E, Vinetz J M, Voong Vinh P, Vu H, Watanabe N A, Weatherby K, White P M, Wilks A F, Winzeler E A, Wojcik E, Wree M, Wu W, Yokoyama N, Zollo P H, Abla N, Blasco B, Burrows J, Laleu B, Leroy D, Spangenberg T, Wells T and Willis P A 2016 Open Source Drug Discovery with the Malaria Box Compound Collection for Neglected Diseases and Beyond PLoS Pathog. 28 e1005763
Williamson A E, Ylioja P M, Robertson M N, Antonova-Koch Y, Avery V, Baell J B, Batchu H, Batra S, Burrows J N, Bhattacharyya S, Calderon F, Charman S A, Clark J, Crespo B, Dean M, Debbert S L, Delves M, Dennis A S, Deroose F, Duffy S, Fletcher S, Giaever G, Hallyburton I, Gamo F J, Gebbia M, Guy R K, Hungerford Z, Kirk K, Lafuente-Monasterio M J, Lee A, Meister S, Nislow C, Overington J P, Papadatos G, Patiny L, Pham J, Ralph S A, Ruecker A, Ryan E, Southan C, Srivastava K, Swain C, Tarnowski M J, Thomson P, Turner P, Wallace I M, Wells T N, White K, White L, Willis P, Winzeler E A, Wittlin S and Todd M H 2016 Open Source Drug Discovery: Highly Potent Antimalarial Compounds Derived from the Tres Cantos Arylpyrroles ACS Cent. Sci. 2 687
Rottmann M, McNamara C, Yeung B K, Lee MC, Zou B, Russell B, Seitz P, Plouffe D M, Dharia N V, Tan J, Cohen S B, Spencer K R, González-Páez GE, Lakshminarayana S B, Goh A, Suwanarusk R, Jegla T, Schmitt E K, Beck H P, Brun R, Nosten F, Renia L, Dartois V, Keller T H, Fidock D A, Winzeler E A and Diagana T T 2010 Spiroindolones, a potent compound class for the treatment of malaria Science 329 1175
Meister S, Plouffe D M, Kuhen K L, Bonamy G M, Wu T, Barnes S W, Bopp S E, Borboa R, Bright A T, Che J, Cohen S, Dharia N V, Gagaring K, Gettayacamin M, Gordon P, Groessl T, Kato N, Lee M C, McNamara C W, Fidock D A, Nagle A, Nam T G, Richmond W, Roland J, Rottmann M, Zhou B, Froissard P, Glynne R J, Mazier D, Sattabongkot J, Schultz P G, Tuntland T, Walker J R, Zhou Y, Chatterjee A, Diagana T T and Winzeler E A 2011 Imaging of Plasmodium liver stages to drive next-generation antimalarial drug discovery Science 334 1372
Gamo F J, Sanz L M, Vidal J, de Cozar C, Alvarez E, Lavandera J L, Vanderwall D E, Green D V, Kumar V, Hasan S, Brown J R, Peishoff C E, Cardon L R and Garcia-Bustos J F 2010 Thousands of chemical starting points for antimalarial lead identification Nature 465 305
Guiguemde W A, Shelat A A, Bouck D, Duffy S, Crowther G J, Davis P H, Smithson D C, Connelly M, Clark J, Zhu F, Jiménez-Díaz M B, Martinez M S, Wilson E B, Tripathi A K, Gut J, Sharlow E R, Bathurst I, El Mazouni F, Fowble J W, Forquer I, McGinley P L, Castro S, Angulo-Barturen I, Ferrer S, Rosenthal P J, Derisi J L, Sullivan D J, Lazo J S, Roos D S, Riscoe M K, Phillips M A, Rathod P K, Van Voorhis W C, Avery V M and Guy R K 2010 Chemical genetics of Plasmodium falciparum Nature 465 311
Wells T N 2010 Microbiology. Is the tide turning for new malaria medicines? Science 329 1153
Rees S 2015 The promise of open innovation in drug discovery: An industry perspective Future Med. Chem. 7 1835
Allarakhia M 2014 The successes and challenges of open-source biopharmaceutical innovation Expert Opin. Drug Discovery 9 459
Global Tuberculosis report http://apps.who.int/iris/bitstream/10665/250441/1/9789241565394-eng.pdf?ua=1 (accessed on 31\(^{{\rm st}}\) January 2017)
Guidelines for treatment of tuberculosis, fourth edition http://apps.who.int/iris/bitstream/10665/44165/1/9789241547833_eng.pdf?ua=1&ua=1 (accessed on 31\(^{{\rm st}}\) December 2016)
Esmail H, Barry C E, Young D B and Wilkinson R J 2014 The ongoing challenge of latent tuberculosisPhilos. Trans. R. Soc. London, Ser. B 369 20130437
Davis C E, Carpenter J L, McAllister C K, Matthews J, Bush B A and Ognibene A J 1985 Tuberculosis. Cause of death in antibiotic era Chest 88 726
Frieden T R, Sterling T R, Munsiff S S, Watt C J and Dye C 2003 Tuberculosis Lancet 362 887
Dye C, Scheele S, Dolin P, Pathania V and Raviglione M C 1999 Consensus statement. Global burden of tuberculosis: estimated incidence, prevalence, and mortality by country. WHO Global Surveillance and Monitoring Project JAMA 282 677
Norton B L and Holland D P 2012 Current management options for latent tuberculosis: a review Infect. Drug Resist. 5 163
Johnson R, Streicher E M, Louw G E, Warren R M, van Helden P D and Victor T C 2006 Drug resistance in Mycobacterium tuberculosis Curr. Issues Mol. Biol. 8 97
Kremer L S and Besra G S 2002 Current status and future development of antitubercular chemotherapy Expert Opin. Invest. Drugs 11 1033
Chan E D and Iseman M D 2008 Multidrug-resistant and extensively drug-resistant tuberculosis: a review Curr. Opin. Infect. Diseases 21 587
Daley C L and Caminero J A 2013 Management of multidrug resistant tuberculosis Semin. Respir. Crit. Care Med. 34 44
Choudhury C, Priyakumar U D and Sastry G N 2014 Molecular dynamics investigation of the active site dynamics of mycobacterial cyclopropane synthase during various stages of the cyclopropanation process J. Struct. Biol. 187 38
Choudhury C, Priyakumar U D and Sastry G N 2015 Dynamics based pharmacophore models for screening potential inhibitors of mycobacterial cyclopropane synthase J. Chem. Inf. Model. 55 848
Choudhury C, Priyakumar U D and Sastry G N 2016 Dynamic ligand-based pharmacophore modeling and virtual screening to identify mycobacterial cyclopropane synthase inhibitors J. Chem. Sci. 128 719
Janardhan S, Ram Vivek M and Sastry G N 2016 Modeling the permeability of drug-like molecules through the cell wall of Mycobacterium tuberculosis: an analogue based approach Mol. Biosyst. 12 3377
Reddy A S, Amarnath H S, Bapi R S, Sastry G M and Sastry G N 2008 Protein ligand interaction database (PLID) Comput. Biol. Chem. 32 387
Srivastava H K, Choudhury C and Sastry G N 2012 The efficacy of conceptual DFT descriptors and docking scores on the QSAR models of HIV protease inhibitors Med. Chem. 8 811
Dobson C M 2004 Chemical space and biology Nature 432 824
Lipinski C and Hopkins A 2004 Navigating chemical space for biology and medicine Nature 432 855
Barker A, Kettle J G, Nowak T and Pease J E 2013 Expanding medicinal chemistry space Drug Discovery Today 18 298
Reymond J L and Awale M 2012 Exploring Chemical Space for Drug Discovery Using the Chemical Universe Database ACS Chem. Neurosci. 3 649
Oprea T I and Gottfries J 2001 Chemography: The art of navigating in chemical space J. Com. Chem. 3 157
Xu J and Stevenson J 2000 Drug-like index: A new approach to measure drug-like compounds and their diversity J. Chem. Inf. Comput. Sci. 40 1177
Irwin J J and Shoichet B K 2005 ZINC-a free database of commercially available compounds for virtual screening J. Chem. Inf. Model. 45 177
Bolton E E, Wang Y, Thiessen P A and Bryant S H 2008 PubChem: Integrated platform of small molecules and biological activities Annu. Rep. Comput. Chem. 4 217
Wang Y, Xiao J, Suzek T O, Zhang J, Wang J, Zhou Z, Han L, Karapetyan K, Dracheva S and Shoemaker B A 2012 PubChem’s BioAssay database Nucleic Acids Res. 40 D400
Vasilevich N I, Kombarov R V, Genis D V and Kirpichenok M A 2012 Lessons from natural products chemistry can offer novel approaches for synthetic chemistry in drug discovery J. Med. Chem. 55 7003
Milne G W and Miller J 1986 The NCI drug information system. 1. System overview J. Chem. Inf. Comput. Sci. 26 154
Wishart D S, Knox C, Guo A C, Shrivastava S, Hassanali M, Stothard P, Chang Z and Woolsey J 2006 DrugBank: A comprehensive resource for in silico drug discovery and exploration Nucleic Acids Res. 34 D668
Kanehisa M, Goto S, Sato Y, Kawashima M, Furumichi M and Tanabe M 2014 Data, information, knowledge and principle: Back to metabolism in KEGG Nucleic Acids Res. 42 D199
Pence H E and Williams A 2010 ChemSpider: An online chemical information resource J. Chem. Educ. 87 1123
Chen C Y 2011 TCM Database@ Taiwan: the world’s largest traditional Chinese medicine database for drug screening in silico PLoS One 6 e15939
Kiss R, Sandor M and Szalai F A 2012 http://Mcule.com: A public web service for drug discovery J. Cheminf. 4 P17
Olah M, Rad R, Ostopovici L, Bora A, Hadaruga N, Hadaruga D, Moldovan R, Fulias A, Mractc M and Oprea T I 2008 In Small Molecules to Systems Biology and Drug Design -WOMBAT and WOMBAT-PK: Bioactivity Databases for Lead and Drug Discovery Chemical Biology S L Schreiber, T M Kapoor and G Wess (Eds.) (Weinheim: Wiley-VCH Verlag GmbH) Vol. 1–3 p. 760
Anna G, Louisa J B, Bento A P and Jon C 2012 ChEMBL: A large-scale bioactivity database for drug discovery Nucleic Acids Res. 40 D1100
Jiang C, Jin X, Dong Y and Chen M 2016 Kekule.js: An Open Source JavaScript Chemoinformatics Toolkit J. Chem. Inf. Model. 56 1132
Wojcikowski M, Zielenkiewicz P and Siedlecki P 2015 Open Drug Discovery Toolkit (ODDT): a new open-source player in the drug discovery field J. Cheminf. 7 26
Kuhn T, Willighagen E L, Zielesny A and Steinbeck C 2010 CDK-Taverna: An open workflow environment for chemoinformatics BMC Bioinformatics 11 159
Steinbeck C, Han Y, Kuhn S, Horlacher O, Luttmann E and Willighagen E 2003 The Chemistry Development Kit (CDK): An open-source Java library for Chemo- and Bioinformatics J. Chem. Inf. Comput. Sci. 43 493
Wolstencroft K, Haines R, Fellows D, Williams A, Withers D, Owen S, Soiland-Reyes S, Dunlop I, Nenadic A, Fisher P, Bhagat J, Belhajjame K, Bacall F, Hardisty A, Nieva H A, Balcazar V M P, Sufi S and Goble C 2013 The Taverna workflow suite: Designing and executing workflows of Web Services on the desktop, web or in the cloud Nucleic Acids Res. 41 W557
Beisken S, Meinl T, Wiswedel B, de Figueiredo L F, Berthold M and Steinbeck C 2013 KNIME-CDK: Workflow-driven cheminformatics BMC Bioinf. 14 257
Blankenberg D, Von Kuster G, Coraor N, Ananda G, Lazarus R, Mangan M, Nekrutenko A and Taylor J 2010 Galaxy: a web-based genome analysis tool for experimentalists Curr. Protoc. Mol. Biol. Chapter 19 Unit 19.10.1-21
Afgan E, Baker D, Beek M V D, Blankenberg D, Bouvier D, Cech M, Chilton J, Clements D, Coraor N, Eberhard C, Gruning B, Guerler A, Jackson J H, Kuster G V, Rasche E, Soranzo N, Turaga N, Taylor J, Nekrutenko A and Goecks J 2016 The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2016 update Nucleic Acids. Res. 44 W3
Blankenberg D, Kuster G V, Bouvier E, Baker D, Afgan E, Stoler N, Galaxy Team, Taylor J and Nekrutenko A 2014 Dissemination of scientific software with Galaxy ToolShed Genome Biol. 15 403
Publicly Accessible Galaxy Servers https://wiki.galaxyproject.org/PublicGalaxyServers (accessed on 31\(^{\rm st}\) December 2016)
Hildebrandt A K, Stockel D, Fischer N M, de la Garza L, Kruger J, Nickels S, Rottig M, Scharfe C, Schumann M, Thiel P, Lenhof H P, Kohlbacher O and Hildebrandt A 2015 ballaxy: web services for structural bioinformatics Bioinformatics 31 121
O’Boyle N M, Banck M, James C A, Morley C, Vandermeersch T and Hutchison G R 2011 Open Babel: An open chemical toolbox J. Cheminf. 3 33
Landrum G RDKit: Open-Source Cheminformatics http://www.rdkit.org (accessed on 31\(^{\rm st}\) December 2016)
Ertl P and Rohde B 2012 The Molecule Cloud - compact visualization of large collections of molecules J. Cheminf. 4 12
Peironcely J E, Cherto M R, Fichera D, Reijmers T, Coulier L, Faulon J L and Hankemeier T 2012 OMG: Open Molecule Generator J. Cheminf. 4 21
Vainio M J and Johnson M S 2005 McQSAR: a multiconformational quantitative structure-activity relationship engine driven by genetic algorithms J. Chem. Inf. Model. 45 1953
Joachims T 1999 Advances in Kernel Methods- Making Large-Scale SVM Learning Practical B Scholkopf, C Burges and A Smola (Eds.) (Cambridge: MIT-Press) p. 169
Moriarty N W, Grosse-Kunstleve R W and Adams P D 2009 electronic Ligand Builder and Optimization Workbench (eLBOW): a tool for ligand coordinate and restraint generation Acta Crystallogr., D: Biol. Crystallogr. 65 1074
Dewar M J S, Zoebisch E G, Healy E F and Stewart J J P 1985 Development and use of quantum mechanical molecular models. 76. AM1: A new general purpose quantum mechanical molecular model J. Am. Chem. Soc. 107 3902
Trott O and Olson A J 2010 AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization and multithreading J. Comput. Chem. 31 455
Drug Likeness Tool (DruLiTo) http://www.niper.ac.in/pi_dev_tools/DruLiToWeb/DruLiTo_index.html (accessed on 31\(^{\rm st}\) December 2016)
Lipinski C A, Lombardo F, Dominy B W and Feeney P J 2001 Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings Adv. Drug Delivery Rev. 46 3
Oprea T I 2000 Property distribution of drug-related chemical databases J. Comput. -Aided. Mol. Des. 14 251
Ghose A K, Viswanadhan V N and Wendoloski J J 1999 A knowledge-based approach in designing combinatorial or medicinal chemistry libraries for drug discovery. 1. A qualitative and quantitative characterization of known drug databases J. Comb. Chem. 1 55
Bickerton G R, Paolini G V, Besnard J, Muresan S and Hopkins A L 2012 Quantifying the chemical beauty of drugs Nat. Chem. 4 90
Veber D F, Johnson S R, Cheng H Y, Smith B R, Ward K W and Kopple K D 2002 Molecular properties that influence the oral bioavailability of drug candidates J. Med. Chem. 45 2615
Yap C W 2011 PaDEL-descriptor: An open source software to calculate molecular descriptors and fingerprints J. Comput. Chem. 32 1466
Jensen C and Scacchi W 2005 Collaboration, leadership, control, and conflict negotiation and the netbeans.org open source software development community IEEE 196b
Acknowledgements
We are thankful to OSDD, CSIR and Sir Dorabji TATA trust for providing TCOF fellowships to some of the authors in the study. CSIR \(12^{\mathrm{th}}\) five year program GENESIS (BSC 0121), Department of Science and Technology (New Delhi) and Department of Biotechnology (New Delhi) are also thanked for funding. Code development has taken about 5 years of time starting from 2012 and has witnessed 5 Workshops in IICT, IMTECH, OSDD centre, Bangalore, and NCL. Besides there were several exchange of students between various institutes. We thank CSIR OSDD consortium, NIPER, JNU, and BBAU for providing support. GNS thank J C Bose fellowship of DST. This manuscript is dedicated to the memory of Dr. Anirban Banerji and Dr. Pankaj Narang who have provided a lot of energy and enthusiasm during the kick-start stages of the MPDS teamwork.
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Deceased: ANIRBAN BANERJI and PANKAJ NARANG.
Principal investigator: G NARAHARI SASTRY
Co-principal investigators: P ANSHU BHARDWAJ, PRASAD V BHARATAM, ANDREW M LYNN, DEVESH KUMAR, GAJENDRA P S RAGHAVA, M KARTHIKEYAN, SUBRAMANIAN VENKATESAN
Core developers: ANAMIKA SINGH GAUR, ANSHU BHARDWAJ, ARUN SHARMA, LIJO JOHN, M RAM VIVEK, NEHA TRIPATHI, PRASAD V BHARATAM, RAKESH KUMAR, SRIDHARA JANARDHAN, G NARAHARI SASTRY
Co-developers: ABHAYSINH MORI, ANIRBAN BANERJI, ANMOL J HEMROM, ANURAG PASSI, APARNA SINGH, ASHEESH KUMAR, CHARUVAKA MUVVA, CHINMAI MADHURI, CHINMAYEE CHOUDHURY, D ARUN KUMAR, DEEPAK PANDIT, DEEPAK R BHARTI, ER AZHAGIYA SINGAM, HARI SAILAJA, HARISH JANGRA, KAAMINI RAITHATHA, KARUNAKAR TANNEERU, KUMARDEEP CHAUDHARY, M PRASANTHI, NANDAN KUMAR, N YEDUKONDALU, NEERAJ K RAJPUT, P SRI SARANYA, PANKAJ NARANG, PRASUN DUTTA, R VENKATA KRISHNAN, REETU SHARMA, R SRINITHI, RUCHI MISHRA, S HEMASRI, SANDEEP SINGH, SURESH KUMAR, UCA JALEEL, VIJAY KHEDKAR, YOGESH JOSHI.
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Gaur, A.S., Bhardwaj, A., Sharma, A. et al. Assessing therapeutic potential of molecules: molecular property diagnostic suite for tuberculosis \((\mathbf{MPDS}^{\mathbf{TB}})\) . J Chem Sci 129, 515–531 (2017). https://doi.org/10.1007/s12039-017-1268-4
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DOI: https://doi.org/10.1007/s12039-017-1268-4