Current Status at BioChemtek

Slides:

Advertisements

Similar presentations

Scientific & technical presentation Calculator Plugins January 2011.

Advertisements

ChemAxon in 3D Gábor Imre, Adrián Kalászi and Miklós Vargyas Solutions for Cheminformatics.

1 Miklós Vargyas, Judit Papp May, 2005 MarvinSpace – live demo.

Analysis of High-Throughput Screening Data C371 Fall 2004.

Case Study: Dopamine D 3 Receptor Anthagonists Chapter 3 – Molecular Modeling 1.

AutoDock 4 and AutoDock Vina -Brief Intruction

Improving enrichment rates A practical solution to an impractical problem Noel O’Boyle Cambridge Crystallographic Data Centre

Bioinformatics Vol. 21 no (Pages ) Reporter: Yu Lun Kuo (D )

Background Goals Methods Results Conclusions Implications.

6th lecture Modern Methods in Drug Discovery WS07/08 1 More QSAR Problems: Which descriptors to use How to test/validate QSAR equations (continued from.

Molecular dynamics refinement and rescoring in WISDOM virtual screenings Gianluca Degliesposti University of Modena and Reggio Emilia Molecular Modelling.

Cheminformatics II Apr 2010 Postgrad course on Comp Chem Noel M. O’Boyle.

EMBIO – Cambridge Particle Swarm Optimization applied to Automated Docking Automated docking of a ligand to a macromolecule Particle Swarm Optimization.

2. Modeling of small systems Building the model What is the optimal conformation of a molecule? What is the relative energy of a given conformation? What.

Summary Molecular surfaces QM properties presented on surface Compound screening Pattern matching on surfaces Martin Swain Critical features Dave Whitley.

Quantative Structure- Activity Relationships. Why QSAR? The number of compounds required for synthesis in order to place 10 different groups in 4 positions.

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.

An Integrated Approach to Protein-Protein Docking

Molecular Docking Using GOLD Tommi Suvitaival Seppo Virtanen S Basics for Biosystems of the Cell Fall 2006.

Protein Structure and Drug Discovery Workshop To be held at Monash University, Mebourne, Australia October 3 rd to 4 th 2006 Molecular Visualization Learn.

Quantitative Structure- Activity Relationships (QSAR)

Inverse Kinematics for Molecular World Sadia Malik April 18, 2002 CS 395T U.T. Austin.

Lecture 7: Computer aided drug design: Statistical approach. Lecture 7: Computer aided drug design: Statistical approach. Chen Yu Zong Department of Computational.

Predicting Highly Connected Proteins in PIN using QSAR Art Cherkasov Apr 14, 2011 UBC / VGH THE UNIVERSITY OF BRITISH COLUMBIA.

QSAR Qualitative Structure-Activity Relationships Can one predict activity (or properties in QSPR) simply on the basis of knowledge of the structure of.

Computational Techniques in Support of Drug Discovery October 2, 2002 Jeffrey Wolbach, Ph. D.

Cédric Notredame (30/08/2015) Chemoinformatics And Bioinformatics Cédric Notredame Molecular Biology Bioinformatics Chemoinformatics Chemistry.

ClusPro: an automated docking and discrimination method for the prediction of protein complexes Stephen R. Comeau, David W.Gatchell, Sandor Vajda, and.

A genetic algorithm for structure based de-novo design Scott C.-H. Pegg, Jose J. Haresco & Irwin D. Kuntz February 21, 2006.

y-sa/2.0/. Integrating the Data Prof:Rui Alves Dept Ciencies Mediques Basiques, 1st.

Chem 1140; Molecular Modeling Molecular Mechanics Semiempirical QM Modeling CaCHE.

1 1 Slide © 2008 Thomson South-Western. All Rights Reserved Chapter 15 Multiple Regression n Multiple Regression Model n Least Squares Method n Multiple.

Molecular Modeling: Conformational Molecular Field Analysis (CoMFA)

Use of Machine Learning in Chemoinformatics Irene Kouskoumvekaki Associate Professor December 12th, 2012 Biological Sequence Analysis course.

3D- QSAR. QSAR A QSAR is a mathematical relationship between a biological activity of a molecular system and its physicochemical parameters. QSAR attempts.

Protein Classification II CISC889: Bioinformatics Gang Situ 04/11/2002 Parts of this lecture borrowed from lecture given by Dr. Altman.

SimBioSys Inc.© Slide #1 Enrichment and cross-validation studies of the eHiTS high throughput screening software package.

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.

Solution of a Partial Differential Equations using the Method of Lines

Altman et al. JACS 2008, Presented By Swati Jain.

Rational Drug Design : HIV Integrase. A process for drug design which bases the design of the drug upon the structure of its protein target. 1.Structural.

Virtual Screening C371 Fall INTRODUCTION Virtual screening – Computational or in silico analog of biological screening –Score, rank, and/or filter.

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

Regression Analysis1. 2 INTRODUCTION TO EMPIRICAL MODELS LEAST SQUARES ESTIMATION OF THE PARAMETERS PROPERTIES OF THE LEAST SQUARES ESTIMATORS AND ESTIMATION.

Use of Machine Learning in Chemoinformatics

CoMFA Study of Piperidine Analogues of Cocaine at the Dopamine Transporter: Exploring the Binding Mode of the 3  -Substituent of the Piperidine Ring Using.

Molecular mechanics Classical physics, treats atoms as spheres Calculations are rapid, even for large molecules Useful for studying conformations Cannot.

Lipinski’s rule of five

PRO-CHEMIST VERSION 6.5 MOLECULAR MODELING (MECHANICS SEMI-EMPIRICAL)

Simplified picture of the principles used for multiple copy simultaneous search (MCSS) and for computational combinatorial ligand design (CCLD). Simplified.

Ligand binding sites Roman Laskowski.

Complete automation in CCP4 What do we need and how to achieve it?

Proteon Scientists from Creative Biolabs are willing to provide custom antibody affinity measurement using ProteOn system. The system is a surface plasmon.

Molecular Docking Profacgen. The interactions between proteins and other molecules play important roles in various biological processes, including gene.

Building Hypotheses and Searching Databases

Virtual Screening.

Prediction of inhibitory activities of Hsp90 inhibitors

Ligand Docking to MHC Class I Molecules

Machine Learning to Predict Experimental Protein-Ligand Complexes

An Integrated Approach to Protein-Protein Docking

BIOINFORMATICS Summary

Volume 23, Issue 12, Pages (December 2015)

Volume 10, Issue 4, Pages (October 2002)

Volume 23, Issue 12, Pages (December 2015)

Mr.Halavath Ramesh 16-MCH-001 Dept. of Chemistry Loyola College University of Madras-Chennai.

Mr.Halavath Ramesh 16-MCH-001 Dept. of Chemistry Loyola College University of Madras-Chennai.

Mr.Halavath Ramesh 16-MCH-001 Dept. of Chemistry Loyola College University of Madras-Chennai.

Mr.Halavath Ramesh 16-MCH-001 Dept. of Chemistry Loyola College University of Madras-Chennai.

Julia Salas Case Study, CS379a

Presentation transcript:

Current Status at BioChemtek Cheminformatics: Current Status at BioChemtek

Current Cheminformtics Software Available TSARTM Cerius2 Catalyst4.6 InsightII

Quantitative Structure-Activity Relationships TsarTM Quantitative Structure-Activity Relationships

Steroid electrostatic similarity indices linked to TsarTM Steroid electrostatic similarity indices linked to Corticosteroid-binding globulin binding affinity Testosterone-binding globulin binding affinity

Molecule Manipulation

Correlation Matrix

Fungicidal and Herbicidal Thiolcarbamates Predictive Activity Information

Structure Property Calculations Mass, surface area, volume Moments of inertia Dipoles Lipophilicity Verloop parameters Counts of atoms, rings, groups, H-bond donors etc Similarity indices (Asp programme) Electrostatic parameters (Vamp programme) etc, etc

Substituent Database

Partial Least Squares Predictive Analysis

Numerical Techniques Clustering techniques Nearest neibors Cluster analysis Discriminant analysis Cluster significance analysis Regression techniques Cross validation of results Stepping multiple regression Partial least squares Neural networks

Cerius2 Cheminformatics/Bioinformatics interface Rational drug design Ligand based drug design Target based drug design Numerical substrate docking algorithms Molecular dynamics and energy minimization

Catalyst 4.6 Structural databases Designing structural databases Generating conformational models Building multiconformer databases Database searching and structure mapping