SlideShare a Scribd company logo
Cheminformatics at
 geneXplain GmbH
         Introducing GUSAR
geneXplain GmbH




Products
   Bioinformatics and systems biology: geneXplain platform
   Bioinformatics and NGS: IMC and GenomeTraveler
   Cheminformatics: PASS, PharmaExpert, GUSAR



                           info@genexplain.com | www.genexplain.com   2
Bioinformatics, Systems Biology, NGS

   geneXplain platform
    ◦ Collection, storage and analysis of experimental data
    ◦ Network clustering and search for master regulators
    ◦ Features: graphical programming of workflows and the possibility to
      write new scripts and add-ons
   IMC
    ◦ Handling of whole genome data
    ◦ Feature mapping and annotation
    ◦ PCR primer calculation
   GenomeTraveler
    ◦ Handling of next generation sequencing (NGS) data
    ◦ Genome analysis
    ◦ All functions of IMC



                                  info@genexplain.com | www.genexplain.com   3
geneXplain GmbH




Products
   Bioinformatics and systems biology: geneXplain
    platform
   Bioinformatics and NGS: IMC and GenomeTraveler
   Cheminformatics: PASS, PharmaExpert, GUSAR


                         info@genexplain.com | www.genexplain.com   4
Contents
   GUSAR                      Models
    ◦ General                   ◦ Acute Rat Toxicity
                                ◦ Antitargets
    ◦ Key Data
    ◦ MNA Descriptors
    ◦ QNA Descriptors
                               Why you should
    ◦ Substructures and         give GUSAR a try
      Activity Prediction
    ◦ Summary



                                info@genexplain.com |
                                  www.genexplain.com    5
GUSAR
General Unrestricted
Structure-Activity
Relationships




       info@genexplain.com |
         www.genexplain.com    6
General Information
   GUSAR…
    ◦ creates models on quantitative structure-activity
      relationships.
    ◦ uses 2D chemical structures from SD files for model
      building and activity prediction.
    ◦ can work with large data sets containing up to 30,000
      chemical compounds.
    ◦ lets you select the most predictive models.


     authorship and copyright by Zakharov A.V., Filimonov D.A., Poroikov V.V.,
                           Lagunin A.A., Moscow, Russia
                       distributed by the geneXplain GmbH




                                  info@genexplain.com | www.genexplain.com       7
Key Data
   Self-consistent regression
    ◦ Unique algorithm based on the statistical regularization of ill-
      posed problems
    ◦ Allows the selection of the best set of descriptors for robust and
      reliable QSAR models


   „Star Track“ approach
    ◦ Also unique to GUSAR
    ◦ Represents any molecule as a set of points (atoms) in a 2D
      QNA descriptor space


   MNA and QNA descriptors

Filimonov D.A., Akimov D.V., Poroikov V.V. (2004) Pharmaceutical Chemistry Journal 38:21-24.
Filimonov D.A., Zakharov A.V., Lagunin A.A., Poroikov V.V. (2009) SAR QSAR Environ. Res. 20:679-709.
                                         info@genexplain.com | www.genexplain.com                      8
MNA Descriptors
Multilevel Neighborhood of Atoms




Filimonov D.A. et al. (1999) J. Chem. Inform. Computer Sci., 39, 666.
Filimonov D.A., Poroikov V.V. (2008) Chemoinformatics Approaches to Virtual Screening. RSC Publ.,
p.182-216.
                                          info@genexplain.com | www.genexplain.com                  9
QNA Descriptors
Quantitative
Neighborhood of Atoms
Descriptors
   ◦ Reflect the nature of
     intermolecular interactions
   ◦ Describe every atom and
     the atoms’ influences on
     each other
   ◦ Are based on P and Q
     values calculated with a                          EA = electron affinity; IP = ionization potential;
                                                       A, B = variables.
     connectivity matrix (see d)
     in the example on the
     right)


Filimonov D.A., Zakharov A.V., Lagunin A.A., Poroikov V.V. (2009) SAR QSAR Environ. Res. 20:679-709.


                                         info@genexplain.com | www.genexplain.com                           10
Substructures and Activity Prediction




•   For green atoms, the predicted value is     Consensus model of 4 MNA and 2 QNA
    equal to that of the whole molecule.        models:

•   For blue atoms, the predicted value is      •    Numbers are the characteristics of the
    less than that of the whole molecule.            consensus model, including the number
                                                     of substances used and statistical values.
•   For red atoms, the predicted value is       •    The graph shows predicted versus
    higher than that of the whole molecule.          observed values from the SAR base.



                                      info@genexplain.com | www.genexplain.com                    11
Summary

   Create multiple (Q)SAR             Select the most
    models for large data          predictive QNA or MNA
            sets.                          models.


                  GUSAR is a QSAR tool
                  with unique algorithms
                  for model creation and
                    activity prediction.

   Upload SD files for batch      Predictions made with
    prediction or evaluate       GUSAR were comparable
   results using the GUSAR        or better than those of
           interface.             other QSAR methods.




                        info@genexplain.com | www.genexplain.com   12
Models
Acute Rat Toxicity
Affinity to Antitargets




        info@genexplain.com |
          www.genexplain.com    13
   Two ready-trained QSAR model bases can
    be provided additionally to the software
    ◦ Acute rat toxicity
    ◦ Affinity to antitargets (off-targets)




                          info@genexplain.com | www.genexplain.com   14
   Activities for rat toxicity
    in LD50
    ◦ LD50 = 50% of lethal dose

   4 types of
    administration:
    ◦   intravenous
    ◦   subcutaneous
    ◦   intraperitoneal
    ◦   oral

   Between 5 and 50
    models using QNA or
    MNA descriptors

                                             info@genexplain.com |
                             info@genexplain.com | www.genexplain.com
                                               www.genexplain.com       15
   32 activities in 3 values
    ◦ IC50 = half-maximal
      inhibitory concentration
    ◦ Ki = dissociation constant
    ◦ Kact = activation constant


   18 different antitarget
    proteins

   Between 2 and 20
    models using QNA or
    MNA descriptors



                                              info@genexplain.com |
                              info@genexplain.com | www.genexplain.com
                                                www.genexplain.com       16
Summary


      Acute Rat Toxicity               Affinity to Antitargets



                   Two additional SAR
                 bases to predict specific
                    activities can be
                  provided additionally.


   Save the time needed to          Explore GUSAR functions
    create models and SAR           using the diverse, ready-
            bases.                       trained models.




                           info@genexplain.com | www.genexplain.com   17
Why You Should Give
       GUSAR a Try
       Advantages of the Software




              info@genexplain.com |
                www.genexplain.com    18
Advantages of GUSAR
   Only the structural formula of a compound is required to
    predict its biological activity.
   The software is installed locally and runs on any ordinary PC.
   Additionally to the QNA and MNA models you create, we can
    provide you with ready-trained GUSAR models on
    ◦ acute rat toxicity (oral, intraperitoneal, intravenous and subcutaneous
      routes of administration) and
    ◦ affinity to certain antitargets that might cause adverse/toxic effects.

   The algorithm is fast and can handle large data sets.
   In comparison with a number of 2D and 3D QSAR methods,
    the predictivity of GUSAR was superior to that of most other
    methods on both heterogeneous and homogenous data sets.
Filimonov D.A., Zakharov A.V., Lagunin A.A., Poroikov V.V. (2009) SAR QSAR Environ. Res. 20:679-709.


                                         info@genexplain.com | www.genexplain.com                      19

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GUSAR - General Unrestricted Structure-Activity Relationships

  • 1. Cheminformatics at geneXplain GmbH Introducing GUSAR
  • 2. geneXplain GmbH Products  Bioinformatics and systems biology: geneXplain platform  Bioinformatics and NGS: IMC and GenomeTraveler  Cheminformatics: PASS, PharmaExpert, GUSAR info@genexplain.com | www.genexplain.com 2
  • 3. Bioinformatics, Systems Biology, NGS  geneXplain platform ◦ Collection, storage and analysis of experimental data ◦ Network clustering and search for master regulators ◦ Features: graphical programming of workflows and the possibility to write new scripts and add-ons  IMC ◦ Handling of whole genome data ◦ Feature mapping and annotation ◦ PCR primer calculation  GenomeTraveler ◦ Handling of next generation sequencing (NGS) data ◦ Genome analysis ◦ All functions of IMC info@genexplain.com | www.genexplain.com 3
  • 4. geneXplain GmbH Products  Bioinformatics and systems biology: geneXplain platform  Bioinformatics and NGS: IMC and GenomeTraveler  Cheminformatics: PASS, PharmaExpert, GUSAR info@genexplain.com | www.genexplain.com 4
  • 5. Contents  GUSAR  Models ◦ General ◦ Acute Rat Toxicity ◦ Antitargets ◦ Key Data ◦ MNA Descriptors ◦ QNA Descriptors  Why you should ◦ Substructures and give GUSAR a try Activity Prediction ◦ Summary info@genexplain.com | www.genexplain.com 5
  • 6. GUSAR General Unrestricted Structure-Activity Relationships info@genexplain.com | www.genexplain.com 6
  • 7. General Information  GUSAR… ◦ creates models on quantitative structure-activity relationships. ◦ uses 2D chemical structures from SD files for model building and activity prediction. ◦ can work with large data sets containing up to 30,000 chemical compounds. ◦ lets you select the most predictive models. authorship and copyright by Zakharov A.V., Filimonov D.A., Poroikov V.V., Lagunin A.A., Moscow, Russia distributed by the geneXplain GmbH info@genexplain.com | www.genexplain.com 7
  • 8. Key Data  Self-consistent regression ◦ Unique algorithm based on the statistical regularization of ill- posed problems ◦ Allows the selection of the best set of descriptors for robust and reliable QSAR models  „Star Track“ approach ◦ Also unique to GUSAR ◦ Represents any molecule as a set of points (atoms) in a 2D QNA descriptor space  MNA and QNA descriptors Filimonov D.A., Akimov D.V., Poroikov V.V. (2004) Pharmaceutical Chemistry Journal 38:21-24. Filimonov D.A., Zakharov A.V., Lagunin A.A., Poroikov V.V. (2009) SAR QSAR Environ. Res. 20:679-709. info@genexplain.com | www.genexplain.com 8
  • 9. MNA Descriptors Multilevel Neighborhood of Atoms Filimonov D.A. et al. (1999) J. Chem. Inform. Computer Sci., 39, 666. Filimonov D.A., Poroikov V.V. (2008) Chemoinformatics Approaches to Virtual Screening. RSC Publ., p.182-216. info@genexplain.com | www.genexplain.com 9
  • 10. QNA Descriptors Quantitative Neighborhood of Atoms Descriptors ◦ Reflect the nature of intermolecular interactions ◦ Describe every atom and the atoms’ influences on each other ◦ Are based on P and Q values calculated with a EA = electron affinity; IP = ionization potential; A, B = variables. connectivity matrix (see d) in the example on the right) Filimonov D.A., Zakharov A.V., Lagunin A.A., Poroikov V.V. (2009) SAR QSAR Environ. Res. 20:679-709. info@genexplain.com | www.genexplain.com 10
  • 11. Substructures and Activity Prediction • For green atoms, the predicted value is Consensus model of 4 MNA and 2 QNA equal to that of the whole molecule. models: • For blue atoms, the predicted value is • Numbers are the characteristics of the less than that of the whole molecule. consensus model, including the number of substances used and statistical values. • For red atoms, the predicted value is • The graph shows predicted versus higher than that of the whole molecule. observed values from the SAR base. info@genexplain.com | www.genexplain.com 11
  • 12. Summary Create multiple (Q)SAR Select the most models for large data predictive QNA or MNA sets. models. GUSAR is a QSAR tool with unique algorithms for model creation and activity prediction. Upload SD files for batch Predictions made with prediction or evaluate GUSAR were comparable results using the GUSAR or better than those of interface. other QSAR methods. info@genexplain.com | www.genexplain.com 12
  • 13. Models Acute Rat Toxicity Affinity to Antitargets info@genexplain.com | www.genexplain.com 13
  • 14. Two ready-trained QSAR model bases can be provided additionally to the software ◦ Acute rat toxicity ◦ Affinity to antitargets (off-targets) info@genexplain.com | www.genexplain.com 14
  • 15. Activities for rat toxicity in LD50 ◦ LD50 = 50% of lethal dose  4 types of administration: ◦ intravenous ◦ subcutaneous ◦ intraperitoneal ◦ oral  Between 5 and 50 models using QNA or MNA descriptors info@genexplain.com | info@genexplain.com | www.genexplain.com www.genexplain.com 15
  • 16. 32 activities in 3 values ◦ IC50 = half-maximal inhibitory concentration ◦ Ki = dissociation constant ◦ Kact = activation constant  18 different antitarget proteins  Between 2 and 20 models using QNA or MNA descriptors info@genexplain.com | info@genexplain.com | www.genexplain.com www.genexplain.com 16
  • 17. Summary Acute Rat Toxicity Affinity to Antitargets Two additional SAR bases to predict specific activities can be provided additionally. Save the time needed to Explore GUSAR functions create models and SAR using the diverse, ready- bases. trained models. info@genexplain.com | www.genexplain.com 17
  • 18. Why You Should Give GUSAR a Try Advantages of the Software info@genexplain.com | www.genexplain.com 18
  • 19. Advantages of GUSAR  Only the structural formula of a compound is required to predict its biological activity.  The software is installed locally and runs on any ordinary PC.  Additionally to the QNA and MNA models you create, we can provide you with ready-trained GUSAR models on ◦ acute rat toxicity (oral, intraperitoneal, intravenous and subcutaneous routes of administration) and ◦ affinity to certain antitargets that might cause adverse/toxic effects.  The algorithm is fast and can handle large data sets.  In comparison with a number of 2D and 3D QSAR methods, the predictivity of GUSAR was superior to that of most other methods on both heterogeneous and homogenous data sets. Filimonov D.A., Zakharov A.V., Lagunin A.A., Poroikov V.V. (2009) SAR QSAR Environ. Res. 20:679-709. info@genexplain.com | www.genexplain.com 19