Catalyst TM What is Catalyst TM ? Structural databases Designing structural databases Generating conformational models Building multi-conformer databases.

Slides:



Advertisements
Similar presentations
Pharmaceutical Salt Selection Suzanna Ward BRAINFEST II.
Advertisements

6th lecture Modern Methods in Drug Discovery WS10/11 1 More QSAR Problems: Which descriptors to use How to test/validate QSAR equations (continued from.
Analysis of High-Throughput Screening Data C371 Fall 2004.
Christopher Reynolds Supervisor: Prof. Michael Sternberg Bioinformatics Department Division of Molecular Biosciences Imperial College London.
1 Sequential Screening S. Stanley Young NISS HTS Workshop October 25, 2002.
Everardo Macias, Patrick Tomboc Eamonn F. Healy, Chemistry Department,
Enzymes.  Describe the characteristics of biological catalysts (enzymes).  Compare inorganic catalysts and biological catalysts (enzymes).  Describe.
INTRODUCTION TO THE BEILSTEIN AND GMELIN DATABASES Margarete Bower Chemistry Library.
3D Molecular Structures C371 Fall Morgan Algorithm (Leach & Gillet, p. 8)
CHEMISTRY MATTER ELEMENT ATOM COMPOUND MOLECULE 1.A _____ is a substance made of atoms of more than one element bound together. 2.An _____ is the smallest.
Mining Graphs.
GraphSig: Mining Significant Substructures in Compound Libraries 1.
Establishing a Successful Virtual Screening Process Stephen Pickett Roche Discovery Welwyn.
Association Analysis (7) (Mining Graphs)
Jeffery Loo NLM Associate Fellow ’03 – ’05 chemicalinformaticsforlibraries.
M. Wagener 3D Database Searching and Scaffold Hopping Markus Wagener NV Organon.
Quantitative Structure-Activity Relationships (QSAR) Comparative Molecular Field Analysis (CoMFA) Gijs Schaftenaar.
Bioinformatics IV Quantitative Structure-Activity Relationships (QSAR) and Comparative Molecular Field Analysis (CoMFA) Martin Ott.
8 th Iranian workshop of Chemometrics 7-9 February 2009 Progress of Chemometrics in Iran Mehdi Jalali-Heravi February 2009 In the Name of God.
Design of Small Molecule Drugs Targeted to RNA RNA Ontology Group May
Chapter 12 Inferring from the Data. Inferring from Data Estimation and Significance testing.
What do all of these have in common?. Natural Products Drug Discovery Searching for Cures in the Plant Kingdom They all contain natural products…
ENZYMES Enzymes are biological substances (proteins) that occur as catalyst and help complex reactions occur everywhere in life.
Pharmacophore and FTrees
ClusPro: an automated docking and discrimination method for the prediction of protein complexes Stephen R. Comeau, David W.Gatchell, Sandor Vajda, and.
Topological Summaries: Using Graphs for Chemical Searching and Mining Graphs are a flexible & unifying model Scalable similarity searches through novel.
Similarity Methods C371 Fall 2004.
Evolutionary Algorithms BIOL/CMSC 361: Emergence Lecture 4/03/08.
CS 790 – Bioinformatics Introduction and overview.
Discovering Dynamic Models Lecture 21. Dynamic Models: Introduction Dynamic models can describe how variables change over time or explain variation by.
Designing a Tri-Peptide based HIV-1 protease inhibitor Presented by, Sushil Kumar Singh IBAB,Bangalore Submitted to Dr. Indira Ghosh AstraZeneca India.
1. Chemometrices:  Signal processing  Classification & pattern reccognation  Experimental design  Multivariative calibration  Quantitative Structure.
New approaches to elucidating Structure Activity Relationships Chris Petersen Technical Manager, Informatics.
Use of Machine Learning in Chemoinformatics Irene Kouskoumvekaki Associate Professor December 12th, 2012 Biological Sequence Analysis course.
20/03/2008 Dept. of Pharmaceutics 1. Use of BIOINFORMATICS in Pharmaciutics 2  Presented By  Shafnan Nazar  Hamid Nasir 
3D- QSAR. QSAR A QSAR is a mathematical relationship between a biological activity of a molecular system and its physicochemical parameters. QSAR attempts.
Vision-based human motion analysis: An overview Computer Vision and Image Understanding(2007)
Structural Browsing Indices, Spotfire and Drug Discovery Mark Johnson 1 and Yong-jin Xu 2 1 Pannanugget Consulting; 2 Pharmacia, Inc. Spotfire Users Conference.
QSAR Study of HIV Protease Inhibitors Using Neural Network and Genetic Algorithm Akmal Aulia, 1 Sunil Kumar, 2 Rajni Garg, * 3 A. Srinivas Reddy, 4 1 Computational.
SimFinder: A Unique Topology- based Approach to Similarity Searching 1.
Virtual Screening C371 Fall INTRODUCTION Virtual screening – Computational or in silico analog of biological screening –Score, rank, and/or filter.
Computer-aided drug discovery (CADD)/design methods have played a major role in the development of therapeutically important small molecules for several.
Enzymes Biological Catalysts Proteins that change the rate of cellular reactions without being consumed in the reaction.
Use of Machine Learning in Chemoinformatics
Molecular Cell Biology Logic and Approaches to Research Cooper.
Discriminating between Drugs and Nondrugs by Prediction of Activity Spectra for Substances (PASS) Soheila Anzali, Gerhard Barnickel, Bertram Cezanne, Michael.
Enzymes. What is an enzyme? Organic catalyst Protein molecule.
Chapter Nonlinear models. Objectives O Classify scatterplots O Use scatterplots and a graphing utility to find models for data and choose the model.
Identification of structurally diverse Growth Hormone Secretagogue (GHS) agonists by virtual screening and structure-activity relationship analysis of.
Mrs. Tuma SBI 4UI. Thermodynamics: First Law of Thermodynamics: The total amount of energy in the universe is constant, although the energy may change.
Computational Approach for Combinatorial Library Design Journal club-1 Sushil Kumar Singh IBAB, Bangalore.
Bioassay Optimization and Robustness Using Design of Experiments Methodology 2015 NBC, San Francisco June 8, 2015 Kevin Guo.
Lesson 5 Enzymes. Catalyst: something that increases the rate of reactions Enzymes are biological catalysts Often ends with –ase Most enzymes are proteins.
Frequent Sub-Structure-Based Approaches for Classifying Chemical Compounds Mukund Deshpande, Michihiro Kuramochi, George Karypis University of Minnesota,
Enzymes HL IB Biology. STARTER: As a group discuss possible definitions for the key terms below Competitive inhibition Non-competitive inhibition Activation.
Molecular Modeling in Drug Discovery: an Overview
Indiana University School of Indiana University ECCR Summary Infrastructure: Cheminformatics web service infrastructure made available as a community resource.
Ingenuity Pathway Analysis Alex Pico. Description "IPA is a software application that enables researchers to analyze and understand the complex biological.
Page 1 Computer-aided Drug Design —Profacgen. Page 2 The most fundamental goal in the drug design process is to determine whether a given compound will.
Toxicity vs CHEMICAL space
Nuclear magnetic resonance NMR spectroscopy is a key analytical technique for structure elucidation of a wide range of materials from small molecules to.
APPLICATIONS OF BIOINFORMATICS IN DRUG DISCOVERY
CHEMISTRY MATTER ELEMENT ATOM COMPOUND MOLECULE
The costs of organization
Building Hypotheses and Searching Databases
Virtual Screening.
Probability & Statistics Probability Theory Mathematical Probability Models Event Relationships Distributions of Random Variables Continuous Random.
Current Status at BioChemtek
Enzymes Biological catalyst – it speeds up reactions without being permanently changed.
o They are mainly proteins o They are biological catalysts that speed up the rate of the biochemical reaction.
Presentation transcript:

Catalyst TM What is Catalyst TM ? Structural databases Designing structural databases Generating conformational models Building multi-conformer databases Database searching and structure mapping

Hypothesis A critical task in drug discovery is building a model of the characteristics of the drug you are trying to develop. In Catalyst TM this model is called an hypothesis. The hypothesis is a set of characteristics that distinguishes a group of molecules.

Hypothesis: What does it represent? If you have a set of compounds that have been assayed for a particular biological activity, Catalyst TM will generate an hypothesis that represents the activity of these compounds.

What does this mean? If you are looking for a refined ACE inhibitor and you have the structures and activities of 20 compounds that exhibit inhibition to ACE, Catalyst TM will generate an hypothesis that represents the structure-activity relationships of the 20 compounds and correlates their structures with ACE inhibition.

What data is contained in the hypothesis? 3D data 2D data (topological data) 1D parameters Constraint descriptors Chemical functionality data.

What data is contained in the hypothesis? The hypothesis is built to contain an assembly of substructures and chemical functions locate at specific positions in space. Variation in the substructure at specific positions may be included in the hypothesis e.g. the substructure might contain a phenyl ring with either N, O or F at position 4.

The chemical structure hypothesis?

The chemical functions hypothesis.

Using the hypothesis to search the database?

Compound 1

Compound 2

Compound 3

Compound 4

Compound 5

Comparing the molecule to the hypothesis?

Using the hypothesis to estimate the activity of compounds? The hypothesis.

Using the hypothesis to estimate the activity of compounds? The hypothesis and the molecule.

Using the hypothesis to estimate the activity of compounds? The hypothesis and the molecule fit.

Using the hypothesis to estimate the activity of compounds? The hypothesis and the molecule.

Using the hypothesis to estimate the activity of compounds? The hypothesis and the molecule fit.

Using the hypothesis to estimate the activity of compounds? The molecules.

Summary What is an hypothesis and how is it used in drug discovery? How do you search a database with an hypothesis? Fitting a compound to an hypothesis? Estimating the activity of a compound from the hypothesis?