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.

[1]  H. Kubinyi.  QSAR and 3D QSAR in drug design Part 1: methodology , 1997 .

[2]  Tudor I. Oprea,et al.  Three-dimensional quantitative structure-activity relationships of steroid aromatase inhibitors , 1996, J. Comput. Aided Mol. Des..

[3]  Paolo Tosco,et al.  Open3DQSAR: a new open-source software aimed at high-throughput chemometric analysis of molecular interaction fields , 2011, Journal of molecular modeling.

[4]  Rino Ragno,et al.  3-D QSAutogrid/R: An Alternative Procedure To Build 3-D QSAR Models. Methodologies and Applications , 2012, J. Chem. Inf. Model..

[5]  G. Klebe,et al.  A 3D QSAR Study on a Set of Dopamine D4 Receptor Antagonists. , 2003 .

[6]  P. Bharatam,et al.  3D-QSAR studies of substituted 1-(3, 3-diphenylpropyl)-piperidinyl amides and ureas as CCR5 receptor antagonists , 2007, Journal of molecular modeling.

[7]  A. Golbraikh,et al.  Validation of protein-based alignment in 3D quantitative structure-activity relationships with CoMFA models. , 2000, European journal of medicinal chemistry.

[8]  Matthew Clark,et al.  The Probability of Chance Correlation Using Partial Least Squares (PLS) , 1993 .

[9]  Garland R. Marshall,et al.  3D-QSAR of angiotensin-converting enzyme and thermolysin inhibitors: A comparison of CoMFA models based on deduced and experimentally determined active site geometries , 1993 .

[10]  Yvonne C. Martin,et al.  3D QSAR: current state, scope, and limitations , 1998 .

[11]  Janet E. Jones.  On the determination of molecular fields. —II. From the equation of state of a gas , 1924 .

[12]  J. Dearden.  The History and Development of Quantitative Structure-Activity Relationships (QSARs) , 2016 .

[13]  Dennis H. Smith,et al.  Applications of artificial intelligence for chemical inference. 37. GENOA: a computer program for structure elucidation utilizing overlapping and alternative substructures , 1981 .

[14]  Gerd Folkers,et al.  3D QSAR in drug design. Vol. 2, Ligand-protein interactions andmolecular similarity , 1998 .

[15]  Renxiao Wang,et al.  All-Orientation Search and All-Placement Search in Comparative Molecular Field Analysis , 1998 .

[16]  G. Cruciani,et al.  Comparative molecular field analysis using GRID force-field and GOLPE variable selection methods in a study of inhibitors of glycogen phosphorylase b. , 1994, Journal of medicinal chemistry.

[17]  Karl Pearson F.R.S..  LIII. On lines and planes of closest fit to systems of points in space , 1901 .

[18]  Jitender Verma,et al.  3D-QSAR in drug design--a review. , 2010, Current topics in medicinal chemistry.

[19]  J. Sutherland,et al.  A comparison of methods for modeling quantitative structure-activity relationships. , 2004, Journal of medicinal chemistry.

[20]  Kenneth M. Merz,et al.  Drug Design : Structure-and Ligand-Based Approaches , 2017 .

[21]  Jaroslaw Polanski,et al.  GRID Formalism for the Comparative Molecular Surface Analysis: Application to the CoMFA Benchmark Steroids, Azo Dyes, and HEPT Derivatives. , 2004 .

[22]  J. Topliss,et al.  Change correlations in structure-activity studies using multiple regression analysis. , 1972, Journal of Medicinal Chemistry.

[23]  R. Shoemaker,et al.  A Three-Dimensional Quantitative Structure-Activity Relationship Study of the Inhibition of the ATPase Activity and the Strand Passing Catalytic Activity of Topoisomerase IIα by Substituted Purine Analogs , 2006, Molecular Pharmacology.

[24]  R. Cramer,et al.  Recent advances in comparative molecular field analysis (CoMFA). , 1989, Progress in clinical and biological research.

[25]  S. Free,et al.  A MATHEMATICAL CONTRIBUTION TO STRUCTURE-ACTIVITY STUDIES. , 1964, Journal of medicinal chemistry.

[26]  Seung Joo Cho,et al.  Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Index Analysis (CoMSIA) Study of Mutagen X , 2004 .

[27]  G. Klebe,et al.  Molecular similarity indices in a comparative analysis (CoMSIA) of drug molecules to correlate and predict their biological activity. , 1994, Journal of medicinal chemistry.

[28]  Na Zhang,et al.  3D QSAR for GSK-3beta inhibition by indirubin analogues. , 2006, European journal of medicinal chemistry.

[29]  A. Duranti,et al.  Application of 3D‐QSAR in the Rational Design of Receptor Ligands and Enzyme Inhibitors , 2005, Chemistry & biodiversity.

[30]  Jonathan D. Hirst,et al.  On the Stability of CoMFA Models , 2004, J. Chem. Inf. Model..

[31]  H. Hotelling.  The relations of the newer multivariate statistical methods to factor analysis. , 1957 .

[32]  Lisa Harris,et al.  Partial Charge Calculation Method Affects CoMFA QSAR Prediction Accuracy , 2009, J. Chem. Inf. Model..

[33]  Rahul Jain,et al.  3D-QSAR study of ring-substituted quinoline class of anti-tuberculosis agents. , 2006, Bioorganic & medicinal chemistry.

[34]  Gordon M. Crippen,et al.  Validation of DAPPER for 3D QSAR: Conformational Search and Chirality Metric , 2003, J. Chem. Inf. Comput. Sci..

[35]  N E Jewell,et al.  Automatic generation of alignments for 3D QSAR analyses. , 2001, Journal of molecular graphics & modelling.

[36]  Eugene A. Coats,et al.  The CoMFA Steroids as a Benchmark Dataset for Development of 3D QSAR Methods , 1998 .

[37]  Angel Herráez,et al.  Biomolecules in the computer: Jmol to the rescue , 2006, Biochemistry and molecular biology education : a bimonthly publication of the International Union of Biochemistry and Molecular Biology.

[38]  Silvio Massa,et al.  3-D QSAR Studies on Histone Deacetylase Inhibitors. A GOLPE/GRID Approach on Different Series of Compounds , 2006, J. Chem. Inf. Model..

[39]  Miki Akamatsu,et al.  Current state and perspectives of 3D-QSAR. , 2002, Current topics in medicinal chemistry.

[40]  P. Skolnick,et al.  Synthetic and computer-assisted analysis of the structural requirements for selective, high-affinity ligand binding to diazepam-insensitive benzodiazepine receptors. , 1993, Journal of medicinal chemistry.

[41]  J. Sussman,et al.  JSmol and the Next-Generation Web-Based Representation of 3D Molecular Structure as Applied to Proteopedia , 2013 .

[42]  H. Kubinyi.  QSAR and 3D QSAR in drug design Part 2: applications and problems , 1997 .

[43]  P. Greengard,et al.  Structural basis for the synthesis of indirubins as potent and selective inhibitors of glycogen synthase kinase-3 and cyclin-dependent kinases. , 2004, Journal of medicinal chemistry.

[44]  D. Maddalena,et al.  Prediction of receptor properties and binding affinity of ligands to benzodiazepine/GABAA receptors using artificial neural networks. , 1995, Journal of medicinal chemistry.

[45]  G Klebe,et al.  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. , 1999, Journal of medicinal chemistry.

[46]  Yun Xu,et al.  On Splitting Training and Validation Set: A Comparative Study of Cross-Validation, Bootstrap and Systematic Sampling for Estimating the Generalization Performance of Supervised Learning , 2018, Journal of Analysis and Testing.

[47]  J. Topliss,et al.  CHANCE FACTORS IN STUDIES OF QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIPS , 1980 .

[48]  Gisbert Schneider,et al.  Active-learning strategies in computer-assisted drug discovery. , 2015, Drug discovery today.

[49]  Chaohong Sun,et al.  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. , 2017, Journal of Medicinal Chemistry.

[50]  Yong-Jun Jiang,et al.  3D QSAR for GSK-3β inhibition by indirubin analogues , 2006 .

[51]  Jeffrey J. Sutherland,et al.  Three-dimensional quantitative structure-activity and structure-selectivity relationships of dihydrofolate reductase inhibitors , 2004, J. Comput. Aided Mol. Des..

[52]  D. Lesieur,et al.  Three-dimensional quantitative structure-activity relationships of cyclo-oxygenase-2 (COX-2) inhibitors: a comparative molecular field analysis. , 2001, Journal of medicinal chemistry.

[53]  Jacob Cohen,et al.  Applied multiple regression/correlation analysis for the behavioral sciences , 1979 .

[54]  Vasant Ranade.  Molecular Interaction Fields , 2006 .

[55]  Artem Cherkasov,et al.  An updated steroid benchmark set and its application in the discovery of novel nanomolar ligands of sex hormone-binding globulin. , 2008, Journal of medicinal chemistry.

[56]  Hongbin Yuan,et al.  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. , 2004, Journal of medicinal chemistry.

[57]  R. Cramer,et al.  Comparative molecular field analysis (CoMFA). 1. Effect of shape on binding of steroids to carrier proteins. , 1988, Journal of the American Chemical Society.

[58]  P. Goodford.  A computational procedure for determining energetically favorable binding sites on biologically important macromolecules. , 1985, Journal of medicinal chemistry.

[59]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[60]  Jeffrey M. Perkel,et al.  Programming: Pick up Python , 2015, Nature.

[61]  Tommi H. Nyrönen,et al.  Comparing the Quality and Predictiveness between 3D QSAR Models Obtained from Manual and Automated Alignment , 2004, J. Chem. Inf. Model..

[62]  H. Kubinyi,et al.  3D QSAR in drug design. , 2002 .

[63]  Stefano Alcaro,et al.  Molecular interaction fields in drug discovery: recent advances and future perspectives , 2013 .

[64]  Ron Kohavi,et al.  A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection , 1995, IJCAI.

[65]  G. Klebe,et al.  DrugScore meets CoMFA: adaptation of fields for molecular comparison (AFMoC) or how to tailor knowledge-based pair-potentials to a particular protein. , 2002, Journal of medicinal chemistry.

[66]  W. Delano.  The PyMOL Molecular Graphics System , 2002 .

[67]  Antti Poso,et al.  3D-QSAR studies on cannabinoid CB1 receptor agonists: G-protein activation as biological data. , 2006, Journal of medicinal chemistry.

[68]  Gabriele Cruciani,et al.  Molecular interaction fields : applications in drug discovery and ADME prediction , 2005 .

[69]  Tudor I. Oprea,et al.  A Different Method for Steric Field Evaluation in CoMFA Improves Model Robustness , 1997, J. Chem. Inf. Comput. Sci..

[70]  J. Topliss,et al.  Chance correlations in structure-activity studies using multiple regression analysis , 1972 .

[71]  Gerta Rücker,et al.  y-Randomization and Its Variants in QSPR/QSAR , 2007, J. Chem. Inf. Model..

[72]  Egon L. Willighagen,et al.  Chemical Markup, XML, and the World Wide Web. 5. Applications of Chemical Metadata in RSS Aggregators , 2004, J. Chem. Inf. Model..

[73]  Xin Hu,et al.  Molecular docking and 3D-QSAR studies of Yersinia protein tyrosine phosphatase YopH inhibitors. , 2005, Bioorganic & medicinal chemistry.

[74]  S. Wold,et al.  PLS: Partial Least Squares Projections to Latent Structures , 1993 .

[75]  Yun Tang,et al.  Molecular modeling and 3D-QSAR studies of indolomorphinan derivatives as kappa opioid antagonists. , 2006, Bioorganic & medicinal chemistry.

[76]  Alexander Tropsha,et al.  Best Practices for QSAR Model Development, Validation, and Exploitation , 2010, Molecular informatics.

[77]  J. Dearden,et al.  QSAR modeling: where have you been? Where are you going to? , 2014, Journal of medicinal chemistry.

[78]  K. Hakkarainen,et al.  The Knowledge Creation Metaphor – An Emergent Epistemological Approach to Learning , 2005 .

[79]  David T. Stanton,et al.  On the Physical Interpretation of QSAR Models , 2003, J. Chem. Inf. Comput. Sci..

[80]  C. Hansch,et al.  p-σ-π Analysis. A Method for the Correlation of Biological Activity and Chemical Structure , 1964 .

[81]  Carlo Scolastico,et al.  A 3D QSAR CoMFA study of non-peptide angiotensin II receptor antagonists , 1996, J. Comput. Aided Mol. Des..

[82]  Glen E Kellogg,et al.  3D QSAR in modern drug design. , 2003, EXS.