www.3d-qsar.com: a web portal that brings 3-D QSAR to all electronic devices—the Py-CoMFA web application as tool to build models from pre-aligned datasets


Comparative molecular field analysis (CoMFA), introduced in 1988, was the first 3-D QSAR method ever published and sold. Since then thousands of application, articles and citation have proved 3-D QSAR as a valuable method to be used in drug design. Several other 3-D QSAR methods have appeared, but still CoMFA remains the most used and cited. Nevertheless from a survey on the Certara® web site it seems that CoMFA is no more available. Herein is presented a python implementation of the CoMFA (Py-CoMFA). Py-CoMFA is usable through the www.3d-qsar.com web applications suites portal by mean of any electronic device that can run a web browser. As benchmark, 30 different publicly available datasets were used to assess the Py-CoMFA usability and robustness. Comparisons with published results proved Py-COMFA to be in very good agreement with those obtained with the original CoMFA. Although the used datasets were pre-aligned, by means of the other web application available through the portal, 3-D QSAR models can be easily build from scratch. In conclusion, although CoMFA is a well known methodology and given the availability of several publicly available Hansch type QSAR web portals, Py-CoMFA represents a valuable tools for any chemoinformatics and informatics non-skilled user that can also be used as support to teach 3-D QSAR. Importantly, Py-CoMFA is the first and unique tool publicly available to build 3-D QSAR models.

This is a preview of subscription content, log in to check access.

Fig. 1.
Fig. 2


  1. 1.

    Reker D, Schneider G (2015) Active-learning strategies in computer-assisted drug discovery. Drug Discov Today 20(4):458–465. https://doi.org/10.1016/j.drudis.2014.12.004

    Article  PubMed  Google Scholar 

  2. 2.

    Cohen J (2003) Applied multiple regression/correlation analysis for the behavioral sciences, vol 1. Taylor & Francis, Routledge

    Google Scholar 

  3. 3.

    Pearson K (1901) LIII. On lines and planes of closest fit to systems of points in space. Philos Mag Ser 2(11):559–572. https://doi.org/10.1080/14786440109462720

    Article  Google Scholar 

  4. 4.

    H MJR (1958) OR 9 (1):63–65. doi:10.2307/3007679

    Article  Google Scholar 

  5. 5.

    Hotelling H (1957) The relations of the newer multivariate statistical methods to factor analysis. Br J Stat Psychol 10(2):69–79. https://doi.org/10.1111/j.2044-8317.1957.tb00179.x

    Article  Google Scholar 

  6. 6.

    Wold S, Johansson E, Cocchi M (1993) PLS: partial least squares projections to latent structures in 3D QSAR in drug design: theory, methods and applications. 3D QSAR in drug design: theory, methods and applications. ESCOM Science Publishers, Leiden

  7. 7.

    Dearden John C (2016) The history and development of quantitative structure-activity relationships (QSARs). Int J Quant Struct-Prop Relat (IJQSPR) 1(1):1–44. https://doi.org/10.4018/IJQSPR.2016010101

    Article  Google Scholar 

  8. 8.

    Free SM Jr, Wilson JW (1964) A mathematical contribution to structure-activity studies. J Med Chem 7:395–399

    CAS  Article  Google Scholar 

  9. 9.

    Hansch C, Fujita T (1964) p-σ-π analysis. A method for the correlation of biological activity and chemical structure. J Am Chem Soc 86(8):1616–1626. https://doi.org/10.1021/ja01062a035

    CAS  Article  Google Scholar 

  10. 10.

    Cherkasov A, Muratov EN, Fourches D, Varnek A, Baskin II, Cronin M, Dearden J, Gramatica P, Martin YC, Todeschini R, Consonni V, Kuz'min VE, Cramer R, Benigni R, Yang C, Rathman J, Terfloth L, Gasteiger J, Richard A, Tropsha A (2014) QSAR modeling: where have you been? Where are you going to? J Med Chem 57(12):4977–5010. https://doi.org/10.1021/jm4004285

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  11. 11.

    Stanton DT (2003) On the physical interpretation of QSAR models. J Chem Inf Comput Sci 43(5):1423–1433. https://doi.org/10.1021/ci0340658

    CAS  Article  PubMed  Google Scholar 

  12. 12.

    Carhart RE, Smith DH, Gray NAB, Nourse JG, Djerassi C (1981) Applications of artificial intelligence for chemical inference. 37. GENOA: a computer program for structure elucidation utilizing overlapping and alternative substructures. J Org Chem 46(8):1708–1718. https://doi.org/10.1021/jo00321a037

    CAS  Article  Google Scholar 

  13. 13.

    Wise M, Cramer RD, Smith D, Exman IA (1983) Progress in three-dimensional drug design: the use of real time color graphics and computer postulation of bioactive molecules in DYLOMMS. In: Deardon JC (ed) Quantitative approaches to drug design. Elsevier, Amsterdam, pp 145–146

  14. 14.

    Cramer RD, Patterson DE, Bunce JD (1988) Comparative molecular-field analysis (CoMFA). 1. Effect of shape on binding of steroids to carrier proteins. J Am Chem Soc 110(18):5959–5967

    CAS  Article  Google Scholar 

  15. 15.

    Goodford PJ (1985) A computational procedure for determining energetically favorable binding sites on biologically important macromolecules. J Med Chem 28(7):849–857

    CAS  Article  Google Scholar 

  16. 16.

    Artese A, Cross S, Costa G, Distinto S, Parrotta L, Alcaro S, Ortuso F, Cruciani G (2013) Molecular interaction fields in drug discovery: recent advances and future perspectives. Wiley Interdiscip Rev: Comput Mol Sci 3(6):594–613. https://doi.org/10.1002/wcms.1150

    CAS  Article  Google Scholar 

  17. 17.

    Jones JE, Chapman S (1924) On the determination of molecular fields. —II. From the equation of state of a gas. Proc R Soc London Ser A 106(738):463–477. https://doi.org/10.1098/rspa.1924.0082

    CAS  Article  Google Scholar 

  18. 18.

    Cruciani G (2006) Molecular interaction fields: applications in drug discovery and ADME prediction, vol 27. doi:10.1002/3527607676

    Google Scholar 

  19. 19.

    Merz KM, Ringe D, Reynolds CH (2010) Drug design: structure- and ligand-based approaches. Cambridge University Press, Cambridge

    Google Scholar 

  20. 20.

    Belvisi L, Bravi G, Catalano G, Mabilia M, Salimbeni A, Scolastico C (1996) A 3D QSAR CoMFA study of non-peptide angiotensin II receptor antagonists. J Comput Aided Mol Des 10(6):567–582

    CAS  Article  Google Scholar 

  21. 21.

    Zhang N, Jiang Y, Zou J, Zhang B, Jin H, Wang Y, Yu Q (2006) 3D QSAR for GSK-3beta inhibition by indirubin analogues. Eur J Med Chem 41(3):373–378. https://doi.org/10.1016/j.ejmech.2005.10.018

    CAS  Article  PubMed  Google Scholar 

  22. 22.

    Kubinyi H, Folkers G, Martin YC (1998) 3D QSAR in drug design. Qdsar, vol 2. Kluwer Academic, Dordrecht

    Google Scholar 

  23. 23.

    Kellogg GE, Semus SF (2003) 3D QSAR in modern drug design. EXS 93:223–241

    CAS  Google Scholar 

  24. 24.

    Bostrom J, Bohm M, Gundertofte K, Klebe G (2003) A 3D QSAR study on a set of dopamine D4 receptor antagonists. J Chem Inf Comput Sci 43(3):1020–1027. https://doi.org/10.1021/ci034004+

    CAS  Article  PubMed  Google Scholar 

  25. 25.

    Martin YC (1998) 3D QSAR: current state, scope, and limitations. Perspect Drug Discov 12:3–23

    Article  Google Scholar 

  26. 26.

    Jewell NE, Turner DB, Willett P, Sexton GJ (2001) Automatic generation of alignments for 3D QSAR analyses. J Mol Graph Model 20(2):111–121. https://doi.org/10.1016/S1093-3263(01)00110-3

    CAS  Article  PubMed  Google Scholar 

  27. 27.

    Coats EA (1998) The CoMFA steroids as a benchmark dataset for development of 3D QSAR methods. In: Kubinyi H, Folkers G, Martin YC (eds) 3D QSAR in drug design: recent advances. Springer, Dordrecht, pp 199–213. https://doi.org/10.1007/0-306-46858-1_13

    Google Scholar 

  28. 28.

    Tervo AJ, Nyronen TH, Ronkko T, Poso A (2004) Comparing the quality and predictiveness between 3D QSAR models obtained from manual and automated alignment. J Chem Inf Comput Sci 44(3):807–816. https://doi.org/10.1021/ci0342268

    CAS  Article  PubMed  Google Scholar 

  29. 29.

    Kubinyi H (1997) QSAR and 3D QSAR in drug design. 1. Methodology. Drug Discov Today 2(11):457–467. https://doi.org/10.1016/S1359-6446(97)01079-9

    CAS  Article  Google Scholar 

  30. 30.

    Kubinyi H (1997) QSAR and 3D QSAR in drug design. 2. Applications and problems. Drug Discov Today 2(12):538–546. https://doi.org/10.1016/S1359-6446(97)01084-2

    CAS  Article  Google Scholar 

  31. 31.

    Wildman SA, Crippen GM (2003) Validation of DAPPER for 3D QSAR: conformational search and chirality metric. J Chem Inf Comput Sci 43(2):629–636. https://doi.org/10.1021/ci0256081

    CAS  Article  PubMed  Google Scholar 

  32. 32.

    Tropsha A (2010) Best practices for QSAR model development, validation, and exploitation. Mol Inform 29(6–7):476–488. https://doi.org/10.1002/minf.201000061

    CAS  Article  PubMed  Google Scholar 

  33. 33.

    Topliss JG, Costello RJ (1972) Chance correlations in structure-activity studies using multiple regression analysis. J Med Chem 15(10):1066–1068. https://doi.org/10.1021/jm00280a017

    CAS  Article  PubMed  Google Scholar 

  34. 34.

    Topliss JG, Edwards RP (1979) Chance factors in studies of quantitative structure-activity relationships. J Med Chem 22(10):1238–1244. https://doi.org/10.1021/jm00196a017

    CAS  Article  PubMed  Google Scholar 

  35. 35.

    Clark M, Cramer RD (1993) The probability of chance correlation using partial least-squares (Pls). Quant Struct-Act Rel 12(2):137–145

    CAS  Article  Google Scholar 

  36. 36.

    Kohavi R (2001) A study of cross-validation and bootstrap for accuracy estimation and model selection. 14

  37. 37.

    Xu Y, Goodacre R (2018) On splitting training and validation set: a comparative study of cross-validation, bootstrap and systematic sampling for estimating the generalization performance of supervised learning. J Anal Test 2(3):249–262. https://doi.org/10.1007/s41664-018-0068-2

    Article  PubMed  PubMed Central  Google Scholar 

  38. 38.

    Klebe G, Abraham U, Mietzner T (1994) Molecular similarity indices in a comparative analysis (CoMSIA) of drug molecules to correlate and predict their biological activity. J Med Chem 37(24):4130–4146. https://doi.org/10.1021/jm00050a010

    CAS  Article  PubMed  Google Scholar 

  39. 39.

    Cruciani G, Watson KA (1994) Comparative molecular field analysis using GRID force-field and GOLPE variable selection methods in a study of inhibitors of glycogen phosphorylase b. J Med Chem 37(16):2589–2601. https://doi.org/10.1021/jm00042a012

    CAS  Article  PubMed  Google Scholar 

  40. 40.

    Ragno R, Simeoni S, Valente S, Massa S, Mai A (2006) 3-D QSAR studies on histone deacetylase inhibitors. A GOLPE/GRID approach on different series of compounds. J Chem Inform Model 46(3):1420–1430. https://doi.org/10.1021/ci050556b

    CAS  Article  Google Scholar 

  41. 41.

    Tosco P, Balle T (2011) Open3DQSAR: a new open-source software aimed at high-throughput chemometric analysis of molecular interaction fields. J Mol Model 17(1):201–208. https://doi.org/10.1007/s00894-010-0684-x

    Article  PubMed  Google Scholar 

  42. 42.

    Akamatsu M (2002) Current state and perspectives of 3D-QSAR. Curr Top Med Chem 2(12):1381–1394

    CAS  Article  Google Scholar 

  43. 43.

    Mor M, Rivara S, Lodola A, Lorenzi S, Bordi F, Plazzi PV, Spadoni G, Bedini A, Duranti A, Tontini A, Tarzia G (2005) Application of 3D-QSAR in the rational design of receptor ligands and enzyme inhibitors. Chem Biodivers 2(11):1438–1451. https://doi.org/10.1002/cbdv.200590117

    CAS  Article  PubMed  Google Scholar 

  44. 44.

    Verma J, Khedkar VM, Coutinho EC (2010) 3D-QSAR in drug design—a review. Curr Top Med Chem 10(1):95–115

    CAS  Article  Google Scholar 

  45. 45.

    Ballante F, Ragno R (2012) 3-D QSAutogrid/R: an alternative procedure to build 3-D QSAR models. Methodologies and applications. J Chem Inf Model 52(6):1674–1685. https://doi.org/10.1021/ci300123x

    CAS  Article  PubMed  Google Scholar 

  46. 46.

    Perkel JM (2015) Programming: pick up python. Nature 518(7537):125–126. https://doi.org/10.1038/518125a

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  47. 47.

    Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, Vanderplas J, Passos A, Cournapeau D, Brucher M, Perrot M, Duchesnay E (2011) Scikit-learn: machine learning in python. J Mach Learn Res 12:2825–2830

  48. 48.

    Rucker C, Rucker G, Meringer M (2007) y-Randomization and its variants in QSPR/QSAR. J Chem Inf Model 47(6):2345–2357. https://doi.org/10.1021/ci700157b

    CAS  Article  PubMed  Google Scholar 

  49. 49.

    Schrodinger, LLC (2010) The PyMOL Molecular Graphics System, Version 1.3r1

  50. 50.

    Paavola S, Hakkarainen K (2005) The knowledge creation metaphor—an emergent epistemological approach to learning. Sci Educ. https://doi.org/10.1007/s11191-004-5157-0

    Article  Google Scholar 

  51. 51.

    Murray-Rust P, Rzepa HS, Williamson MJ, Willighagen EL (2004) Chemical markup, XML, and the World Wide Web. 5. Applications of chemical metadata in RSS aggregators. J Chem Inf Comput Sci 44(2):462–469. https://doi.org/10.1021/ci034244p

    CAS  Article  PubMed  Google Scholar 

  52. 52.

    Herráez A (2006) Biomolecules in the computer: Jmol to the rescue. Biochem Mol Biol Educ 34(4):255–261. https://doi.org/10.1002/bmb.2006.494034042644

    Article  PubMed  Google Scholar 

  53. 53.

    McDaniel KF, Wang L, Soltwedel T, Fidanze SD, Hasvold LA, Liu D, Mantei RA, Pratt JK, Sheppard GS, Bui MH, Faivre EJ, Huang X, Li L, Lin X, Wang R, Warder SE, Wilcox D, Albert DH, Magoc TJ, Rajaraman G, Park CH, Hutchins CW, Shen JJ, Edalji RP, Sun CC, Martin R, Gao W, Wong S, Fang G, Elmore SW, Shen Y, Kati WM (2017) Discovery of N-(4-(2,4-difluorophenoxy)-3-(6-methyl-7-oxo-6,7-dihydro-1H-pyrrolo[2,3-c]pyridin -4-yl)phenyl)ethanesulfonamide (ABBV-075/Mivebresib), a Potent and Orally Available Bromodomain and Extraterminal Domain (BET) Family Bromodomain Inhibitor. J Med Chem 60(20):8369–8384. https://doi.org/10.1021/acs.jmedchem.7b00746

    CAS  Article  PubMed  Google Scholar 

  54. 54.

    Hanson RM, Prilusky J, Renjian Z, Nakane T, Sussman JL (2013) JSmol and the next-generation web-based representation of 3D molecular structure as applied to proteopedia. Isr J Chem 53(3–4):207–216. https://doi.org/10.1002/ijch.201300024

    CAS  Article  Google Scholar 

  55. 55.

    Cherkasov A, Ban F, Santos-Filho O, Thorsteinson N, Fallahi M, Hammond GL (2008) An updated steroid benchmark set and its application in the discovery of novel nanomolar ligands of sex hormone-binding globulin. J Med Chem 51(7):2047–2056. https://doi.org/10.1021/jm7011485

    CAS  Article  PubMed  Google Scholar 

  56. 56.

    Polanski J, Gieleciak R, Magdziarz T, Bak A (2004) GRID formalism for the comparative molecular surface analysis: application to the CoMFA benchmark steroids, azo dyes, and HEPT derivatives. J Chem Inf Comput Sci 44(4):1423–1435. https://doi.org/10.1021/ci049960l

    CAS  Article  PubMed  Google Scholar 

  57. 57.

    Depriest SA, Mayer D, Naylor CB, Marshall GR (1993) 3d-Qsar of angiotensin-converting enzyme and thermolysin inhibitors—a comparison of Comfa models based on deduced and experimentally determined active-site geometries. J Am Chem Soc 115(13):5372–5384

    CAS  Article  Google Scholar 

  58. 58.

    Sutherland JJ, O'Brien LA, Weaver DF (2004) A comparison of methods for modeling quantitative structure-activity relationships. J Med Chem 47(22):5541–5554. https://doi.org/10.1021/jm0497141

    CAS  Article  PubMed  Google Scholar 

  59. 59.

    Golbraikh A, Bernard P, Chretien JR (2000) Validation of protein-based alignment in 3D quantitative structure-activity relationships with CoMFA models. Eur J Med Chem 35(1):123–136

    CAS  Article  Google Scholar 

  60. 60.

    Maddalena DJ, Johnston GAR (1995) Prediction of receptor properties and binding-affinity of ligands to benzodiazepine/gaba(a) receptors using artificial neural networks. J Med Chem 38(4):715–724

    CAS  Article  Google Scholar 

  61. 61.

    Gohlke H, Klebe G (2002) DrugScore meets CoMFA: adaptation of fields for molecular comparison (AFMoC) or how to tailor knowledge-based pair-potentials to a particular protein. J Med Chem 45(19):4153–4170

    CAS  Article  Google Scholar 

  62. 62.

    Chavatte P, Yous S, Marot C, Baurin N, Lesieur D (2001) Three-dimensional quantitative structure-activity relationships of cyclo-oxygenase-2 (COX-2) inhibitors: a comparative molecular field analysis. J Med Chem 44(20):3223–3230

    CAS  Article  Google Scholar 

  63. 63.

    Sutherland JJ, Weaver DF (2004) Three-dimensional quantitative structure-activity and structure-selectivity relationships of dihydrofolate reductase inhibitors. J Comput Aid Mol Des 18(5):309–331

    CAS  Article  Google Scholar 

  64. 64.

    Klebe G, Abraham U, Mietzner T (1994) Molecular similarity indexes in a comparative-analysis (Comsia) of drug molecules to correlate and predict their biological-activity. J Med Chem 37(24):4130–4146

    CAS  Article  Google Scholar 

  65. 65.

    Nayyar A, Malde A, Jain R, Coutinho E (2006) 3D-QSAR study of ring-substituted quinoline class of anti-tuberculosis agents. Bioorg Med Chem 14(3):847–856. https://doi.org/10.1016/j.bmc.2005.09.018

    CAS  Article  PubMed  Google Scholar 

  66. 66.

    Aher YD, Agrawal A, Bharatam PV, Garg P (2007) 3D-QSAR studies of substituted 1-(3, 3-diphenylpropyl)-piperidinyl amides and ureas as CCR5 receptor antagonists. J Mol Model 13(4):519–529. https://doi.org/10.1007/s00894-007-0173-z

    CAS  Article  PubMed  Google Scholar 

  67. 67.

    Hu X, Stebbins CE (2005) Molecular docking and 3D-QSAR studies of Yersinia protein tyrosine phosphatase YopH inhibitors. Bioorg Med Chem 13(4):1101–1109. https://doi.org/10.1016/j.bmc.2004.11.026

    CAS  Article  PubMed  Google Scholar 

  68. 68.

    Li W, Tang Y, Zheng YL, Qiu ZB (2006) Molecular modeling and 3D-QSAR studies of indolomorphinan derivatives as kappa opioid antagonists. Bioorg Med Chem 14(3):601–610. https://doi.org/10.1016/j.bmc.2005.08.052

    CAS  Article  PubMed  Google Scholar 

  69. 69.

    Bang SJ, Cho SJ (2004) Comparative molecular field analysis (CoMFA) and comparative molecular similarity index analysis (CoMSIA) study of mutagen X. B Kor Chem Soc 25(10):1525–1530

    CAS  Article  Google Scholar 

  70. 70.

    Yuan HB, Kozikowski AP, Petukhov PA (2004) CoMFA study of piperidine analogues of cocaine at the dopamine transporter: exploring the binding mode of the 3 alpha-substituent of the piperidine ring using pharmacophore-based flexible alignment. J Med Chem 47(25):6137–6143. https://doi.org/10.1021/Jm049544s

    CAS  Article  PubMed  Google Scholar 

  71. 71.

    Jensen LH, Liang H, Shoemaker R, Grauslund M, Sehested M, Hasinoff BB (2006) A three-dimensional quantitative structure-activity relationship study of the inhibition of the ATPase activity and the strand passing catalytic activity of topoisomerase II alpha by substituted purine analogs. Mol Pharmacol 70(5):1503–1513. https://doi.org/10.1124/mol.106.026856

    CAS  Article  PubMed  Google Scholar 

  72. 72.

    Salo OM, Savinainen JR, Parkkari T, Nevalainen T, Lahtela-Kakkonen M, Gynther J, Laitinen JT, Jarvinen T, Poso A (2006) 3D-QSAR studies on cannabinoid CB1 receptor agonists: G-protein activation as biological data. J Med Chem 49(2):554–566. https://doi.org/10.1021/jm0505157

    CAS  Article  PubMed  Google Scholar 

  73. 73.

    Sulea T, Oprea TI, Muresan S, Chan SL (1997) A different method for steric field evaluation in CoMFA improves model robustness. J Chem Inf Comp Sci 37(6):1162–1170

    CAS  Article  Google Scholar 

  74. 74.

    Oprea TI, Garcia AE (1996) Three-dimensional quantitative structure-activity relationships of steroid aromatase inhibitors. J Comput Aided Mol Des 10(3):186–200

    CAS  Article  Google Scholar 

  75. 75.

    Mittal RR, Harris L, McKinnon RA, Sorich MJ (2009) Partial charge calculation method affects CoMFA QSAR prediction accuracy. J Chem Inf Model 49(3):704–709. https://doi.org/10.1021/ci800390m

    CAS  Article  PubMed  Google Scholar 

  76. 76.

    Polychronopoulos P, Magiatis P, Skaltsounis AL, Myrianthopoulos V, Mikros E, Tarricone A, Musacchio A, Roe SM, Pearl L, Leost M, Greengard P, Meijer L (2004) Structural basis for the synthesis of indirubins as potent and selective inhibitors of glycogen synthase kinase-3 and cyclin-dependent kinases. J Med Chem 47(4):935–946. https://doi.org/10.1021/jm031016d

    CAS  Article  PubMed  Google Scholar 

  77. 77.

    Wang RX, Gao Y, Liu L, Lai LH (1998) All-orientation search and all-placement search in comparative molecular field analysis. J Mol Model 4(8):276–283

    CAS  Article  Google Scholar 

  78. 78.

    Melville JL, Hirst JD (2004) On the stability of CoMFA models. J Chem Inf Comput Sci 44(4):1294–1300. https://doi.org/10.1021/ci049944o

    CAS  Article  PubMed  Google Scholar 

  79. 79.

    Wong G, Koehler KF, Skolnick P, Gu ZQ, Ananthan S, Schonholzer P, Hunkeler W, Zhang W, Cook JM (1993) Synthetic and computer-assisted analysis of the structural requirements for selective, high-affinity ligand binding to diazepam-insensitive benzodiazepine receptors. J Med Chem 36(13):1820–1830

    CAS  Article  Google Scholar 

  80. 80.

    Bohm M, Sturzebecher J, Klebe G (1999) Three-dimensional quantitative structure-activity relationship analyses using comparative molecular field analysis and comparative molecular similarity indices analysis to elucidate selectivity differences of inhibitors binding to trypsin, thrombin, and factor Xa. J Med Chem 42(3):458–477

    CAS  Article  Google Scholar 

Download references


Many thanks are due to Alessio Ragno and Dylan Savoia, two former software engineering bachelor students that technically supported the development of www.3d-qsar.com. Many thanks are also due to the RCMD former PhD students Manuela Sabatino and Alexandros Patsilinakos for their help in testing the Py-CoMFA module.

Author information



Corresponding author

Correspondence to Rino Ragno.

Additional information

This article is dedicated to Prof Maurizio Botta who left us prematurely. This work would have not even conceived without his help in introducing RR into molecular modeling.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 28 kb)

Supplementary file2 (ZIP 3584 kb)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Ragno, R. www.3d-qsar.com: a web portal that brings 3-D QSAR to all electronic devices—the Py-CoMFA web application as tool to build models from pre-aligned datasets. J Comput Aided Mol Des 33, 855–864 (2019). https://doi.org/10.1007/s10822-019-00231-x

Download citation


  • CoMFA
  • 3D QSAR
  • Ligand based drug design