Using Phase for Pharmacophore Modelling. 5th European Life Science Bootcamp March, 2017

Size: px
Start display at page:

Download "Using Phase for Pharmacophore Modelling. 5th European Life Science Bootcamp March, 2017"

Transcription

1 Using Phase for Pharmacophore Modelling 5th European Life Science Bootcamp March, 2017

2 Phase: Our Pharmacohore generation tool Significant improvements to Phase methods in 2016 New highly interactive interface in Maestro 11 New common pharmacophore perception algorithm based on pharmacophore-based shape New PhaseHypoScore metric for rank-ordering hypotheses Updated feature definition defaults Simplified command line usage Previously introduced in our last bootcamp series Recording available from

3 Overview Brief introduction of what is a pharmacophore model Basic concept Key considerations How and why pharmacophores can be a very efficient tool in your research How can we use pharmacophores using 2 example cases Create models Validate the models Apply the models

4 What is a pharmacophore model? Pharmacophore model "an ensemble of steric and electronic features that is necessary to ensure the optimal supramolecular interactions with a specific biological target and to trigger (or block) its biological response [1] Abstract concept Collection of features in a specific spatial arrangement Describes a specific binding mode [1] Wermuth CG, Ganellin CR, Lindberg P, Mitscher LA (1998). "Glossary of terms used in medicinal chemistry (IUPAC Recommendations 1998)". Pure and Applied Chemistry. 70 (5):

5 Hit identification Core hopping Lead optimization How can they be used? Can be applicable in both ligand-based as well as structurebased projects Versatile Quick

6 Typical Workflow Analyse and Prepare Dataset Create Model Apply Validate Ligand Preparation (Protein preparation if applicable) Conformation Sampling Feature Preception Select method Feature presets Hypothesis settings Required features, number of features etc Visual inspection of the alignment Feature Mapping Binding mode Actives/Inactive Enrichment Validate wit Test Set Database screening Lead optimization Core hopping

7 Pharmacophore Feature Definitions Complete control over feature definitions via SMARTS patterns Updated default feature definitions More reliable assignment of features for conjugated heteroatoms (amidines) All possible hydrophobic features available Feature definition presets allow common modifications without having to change definitions manually or to regenerate sites in database

8 Several Ways to Create Pharmacophore Hypotheses From multiple active ligands From one ligand conformation From apo protein 2 From protein-ligand complex 1 1 Salam, N., Nuti, R., Sherman, W., J Chem Inf Model, 2009, 49(10), Loving, K., Salam, N., Sherman, W., J Comput-Aid Design, 2009, 23(8), 541.

9 Several Ways to Create Pharmacophore Hypotheses From multiple active ligands From one ligand conformation From apo protein 2 From protein-ligand complex 1 1 Salam, N., Nuti, R., Sherman, W., J Chem Inf Model, 2009, 49(10), Loving, K., Salam, N., Sherman, W., J Comput-Aid Design, 2009, 23(8), 541.

10 Phase Common Pharmacophore Generation Algorithm Reference Conformer 1. Generate conformers for all active ligands 2. Pairwise align by pharmacophore-based shape all pairs of active conformers 3. Generate consensus alignments per active ligand 4. Score and rank-order sites in consensus alignments 5. Refine surviving hypothesis by traditional pharmacophore screening 6. Return top-n hypothesis Technical advantages of new common pharmacophore generation algorithm Generate hypotheses with between 3 and 8 sites in a single run Use large numbers of active ligands (>100) Well suited to find hypotheses that match a small fraction of active ligands Highly distributable calculation; short time to hypotheses (<15 min) Refined Alignments Pharmacophore-based Shape Consensus Alignments Consensus Pharmacophore

11 Generating Consensus Alignments Each conformer a i from active A yields a consensus alignment that contains one conformer from each of the other actives B, C, etc. The conformer chosen from B, C, etc. is the one that yields the highest pharmacophore-based shape similarity to a i Shape similarity is computed as a sum of hard-sphere overlaps between pharmacophore sites of the same type: Type Radius Projected Spheres* Acceptor 2.0 Donor 2.0 Hydrophobic 3.0 Negative Ionic 2.0 Positive Ionic 2.0 Aromatic Ring 3.0 * 2.0 Å projected spheres are included to reward alignments with similar orientations of these vector features

12 Why Use Pharmacophore Shape Alignment? Avoids superpositions that would lead to inconsistent ligandreceptor interactions Acceptors point in different directions Preserves the overall shape of a hypothetical binding pocket across all ligands

13 Generating Consensus Pharmacophore A consensus pharmacophore site arises when the required number of actives each contribute a site that lies within 2Å of the corresponding reference site The consensus site is always one of the reference sites (rather than, e.g., the centroid of the sites) If hypotheses containing up to n sites are desired, but the consensus pharmacophore contains more than n sites, all possible subsets of n consensus sites are considered

14 Test Set BEDROC(20) Test Set BEDROC(20) Rank Order Hypothesis by Screening Performance Rank order hypotheses by interpretability and selectivity in virtual screening New PhaseHypoScore metric combines Phase Survival Score with enrichment against the Glide decoy set measured by BEDROC(α=20) 1,2 1 R 2 = 0.10 R 2 = ,2 1 0,8 0,8 0,6 0,6 0,4 0,4 0,2 0, Survival Score 0 0 0,2 0,4 0,6 0,8 1 1,2 1,4 1,6 PhaseHypoScore Truchon, J., Bayly, C. J. Chem. Inf. Model. 2007, 47,

15 Pharmacophore Hypothesis Generation with E-Pharmacophores from Protein-Ligand Complex -0.6 NA Refine native ligand with Glide XP NA NA NA NA Assess atom-based energy of each feature Hypothesis with desired number of sites

16 Pharmacophore Hypothesis Generation with E-Pharmacophores from Apo Protein Dock 548 fragments into apo site keeping up to 100 poses per fragment Assess atom-based energy of fragments for each fragment Cluster top-scoring 50 fragments by volume and keep most diverse, topscoring fragment from each cluster -0.7 Hypothesis

17 Example Case 1: Generating pharmacophore from proteinligand complex Target: Leukotriene A-4 hydrolase PDB code: 3chp

18 Example Case 2: Identifying Common Pharmacophore Hypotheses Target: Leukotriene A-4 hydrolase PDB code: 3chp Dataset for exercise taken from DUD-E database (dude.docking.org) Training set: 58 ligands

19 Phase Databases Generate PhaseDB without license limitations Improved speed and robustness of Phase DB generation Epik ~ 4x faster than 1 year ago New ConfGen ~25x faster than 1 year ago Phase DB internals are ~5x faster than 1 year ago emolecules Phase DB available Updated monthly with easy upgrade process Lead-, drug-, and fragment-like subsets Screen compounds in DB with Glide, Shape Screening, and Phase

20 Percent of Compounds ConfGen - A New Fast 3D Conformation Generator Employs a fragment-lookup-and-join approach Process ~10 compounds/sec 25x faster than ConfGen Intermediate Good recovery of bioactive conformations Time Per Compound (sec.) Reproduction of Bioactive Conformations New ConfGen ConfGen Intermediate ConfGen Comprehensive New ConfGen ConfGen Intermediate ConfGen Comprehensive RMSD Cutoff (Angstroms)

21 Summary Comprehensive set of tools for generating pharmacohores both from ligand-based as well as structure-based Easy database generation for virtual screening Workflow for generating common pharmacophore hypotheses using large number of active ligands Screening multiple hypotheses/databases in single job

22 Product page Help and support materials Videos Documention &Tutorials Directly accessible from Maestro Help > Tutorials Help > Help Online Support Contact us:

23 Thank you & Questions This concludes our 5th European Life Science Bootcamp series Recordings will be made available on our website shortly Thank you very much for your attention!

Schrodinger ebootcamp #3, Summer EXPLORING METHODS FOR CONFORMER SEARCHING Jas Bhachoo, Senior Applications Scientist

Schrodinger ebootcamp #3, Summer EXPLORING METHODS FOR CONFORMER SEARCHING Jas Bhachoo, Senior Applications Scientist Schrodinger ebootcamp #3, Summer 2016 EXPLORING METHODS FOR CONFORMER SEARCHING Jas Bhachoo, Senior Applications Scientist Numerous applications Generating conformations MM Agenda http://www.schrodinger.com/macromodel

More information

The Schrödinger KNIME extensions

The Schrödinger KNIME extensions The Schrödinger KNIME extensions Computational Chemistry and Cheminformatics in a workflow environment Jean-Christophe Mozziconacci Volker Eyrich Topics What are the Schrödinger extensions? Workflow application

More information

Creating a Pharmacophore Query from a Reference Molecule & Scaffold Hopping in CSD-CrossMiner

Creating a Pharmacophore Query from a Reference Molecule & Scaffold Hopping in CSD-CrossMiner Table of Contents Creating a Pharmacophore Query from a Reference Molecule & Scaffold Hopping in CSD-CrossMiner Introduction... 2 CSD-CrossMiner Terminology... 2 Overview of CSD-CrossMiner... 3 Features

More information

Performing a Pharmacophore Search using CSD-CrossMiner

Performing a Pharmacophore Search using CSD-CrossMiner Table of Contents Introduction... 2 CSD-CrossMiner Terminology... 2 Overview of CSD-CrossMiner... 3 Searching with a Pharmacophore... 4 Performing a Pharmacophore Search using CSD-CrossMiner Version 2.0

More information

Introduction to Structure Preparation and Visualization

Introduction to Structure Preparation and Visualization Introduction to Structure Preparation and Visualization Created with: Release 2018-4 Prerequisites: Release 2018-2 or higher Access to the internet Categories: Molecular Visualization, Structure-Based

More information

Docking. GBCB 5874: Problem Solving in GBCB

Docking. GBCB 5874: Problem Solving in GBCB Docking Benzamidine Docking to Trypsin Relationship to Drug Design Ligand-based design QSAR Pharmacophore modeling Can be done without 3-D structure of protein Receptor/Structure-based design Molecular

More information

The Schrödinger KNIME extensions

The Schrödinger KNIME extensions The Schrödinger KNIME extensions Computational Chemistry and Cheminformatics in a workflow environment Jean-Christophe Mozziconacci Volker Eyrich KNIME UGM, Berlin, February 2015 The Schrödinger Extensions

More information

Ligand Scout Tutorials

Ligand Scout Tutorials Ligand Scout Tutorials Step : Creating a pharmacophore from a protein-ligand complex. Type ke6 in the upper right area of the screen and press the button Download *+. The protein will be downloaded and

More information

The PhilOEsophy. There are only two fundamental molecular descriptors

The PhilOEsophy. There are only two fundamental molecular descriptors The PhilOEsophy There are only two fundamental molecular descriptors Where can we use shape? Virtual screening More effective than 2D Lead-hopping Shape analogues are not graph analogues Molecular alignment

More information

Bioengineering & Bioinformatics Summer Institute, Dept. Computational Biology, University of Pittsburgh, PGH, PA

Bioengineering & Bioinformatics Summer Institute, Dept. Computational Biology, University of Pittsburgh, PGH, PA Pharmacophore Model Development for the Identification of Novel Acetylcholinesterase Inhibitors Edwin Kamau Dept Chem & Biochem Kennesa State Uni ersit Kennesa GA 30144 Dept. Chem. & Biochem. Kennesaw

More information

Biologically Relevant Molecular Comparisons. Mark Mackey

Biologically Relevant Molecular Comparisons. Mark Mackey Biologically Relevant Molecular Comparisons Mark Mackey Agenda > Cresset Technology > Cresset Products > FieldStere > FieldScreen > FieldAlign > FieldTemplater > Cresset and Knime About Cresset > Specialist

More information

User Guide for LeDock

User Guide for LeDock User Guide for LeDock Hongtao Zhao, PhD Email: htzhao@lephar.com Website: www.lephar.com Copyright 2017 Hongtao Zhao. All rights reserved. Introduction LeDock is flexible small-molecule docking software,

More information

Pose and affinity prediction by ICM in D3R GC3. Max Totrov Molsoft

Pose and affinity prediction by ICM in D3R GC3. Max Totrov Molsoft Pose and affinity prediction by ICM in D3R GC3 Max Totrov Molsoft Pose prediction method: ICM-dock ICM-dock: - pre-sampling of ligand conformers - multiple trajectory Monte-Carlo with gradient minimization

More information

DOCKING TUTORIAL. A. The docking Workflow

DOCKING TUTORIAL. A. The docking Workflow 2 nd Strasbourg Summer School on Chemoinformatics VVF Obernai, France, 20-24 June 2010 E. Kellenberger DOCKING TUTORIAL A. The docking Workflow 1. Ligand preparation It consists in the standardization

More information

MD Simulation in Pose Refinement and Scoring Using AMBER Workflows

MD Simulation in Pose Refinement and Scoring Using AMBER Workflows MD Simulation in Pose Refinement and Scoring Using AMBER Workflows Yuan Hu (On behalf of Merck D3R Team) D3R Grand Challenge 2 Webinar Department of Chemistry, Modeling & Informatics Merck Research Laboratories,

More information

Virtual Libraries and Virtual Screening in Drug Discovery Processes using KNIME

Virtual Libraries and Virtual Screening in Drug Discovery Processes using KNIME Virtual Libraries and Virtual Screening in Drug Discovery Processes using KNIME Iván Solt Solutions for Cheminformatics Drug Discovery Strategies for known targets High-Throughput Screening (HTS) Cells

More information

Part 6. 3D Pharmacophore Modeling

Part 6. 3D Pharmacophore Modeling 279 Part 6 3D Pharmacophore Modeling 281 20 3D Pharmacophore Modeling Techniques in Computer Aided Molecular Design Using LigandScout Thomas Seidel, Sharon D. Bryant, Gökhan Ibis, Giulio Poli, and Thierry

More information

est Drive K20 GPUs! Experience The Acceleration Run Computational Chemistry Codes on Tesla K20 GPU today

est Drive K20 GPUs! Experience The Acceleration Run Computational Chemistry Codes on Tesla K20 GPU today est Drive K20 GPUs! Experience The Acceleration Run Computational Chemistry Codes on Tesla K20 GPU today Sign up for FREE GPU Test Drive on remotely hosted clusters www.nvidia.com/gputestd rive Shape Searching

More information

CSD. CSD-Enterprise. Access the CSD and ALL CCDC application software

CSD. CSD-Enterprise. Access the CSD and ALL CCDC application software CSD CSD-Enterprise Access the CSD and ALL CCDC application software CSD-Enterprise brings it all: access to the Cambridge Structural Database (CSD), the world s comprehensive and up-to-date database of

More information

Computational Chemistry in Drug Design. Xavier Fradera Barcelona, 17/4/2007

Computational Chemistry in Drug Design. Xavier Fradera Barcelona, 17/4/2007 Computational Chemistry in Drug Design Xavier Fradera Barcelona, 17/4/2007 verview Introduction and background Drug Design Cycle Computational methods Chemoinformatics Ligand Based Methods Structure Based

More information

Conformational Searching using MacroModel and ConfGen. John Shelley Schrödinger Fellow

Conformational Searching using MacroModel and ConfGen. John Shelley Schrödinger Fellow Conformational Searching using MacroModel and ConfGen John Shelley Schrödinger Fellow Overview Types of conformational searching applications MacroModel s conformation generation procedure General features

More information

Virtual screening in drug discovery

Virtual screening in drug discovery Virtual screening in drug discovery Pavel Polishchuk Institute of Molecular and Translational Medicine Palacky University pavlo.polishchuk@upol.cz Drug development workflow Vistoli G., et al., Drug Discovery

More information

Kd = koff/kon = [R][L]/[RL]

Kd = koff/kon = [R][L]/[RL] Taller de docking y cribado virtual: Uso de herramientas computacionales en el diseño de fármacos Docking program GLIDE El programa de docking GLIDE Sonsoles Martín-Santamaría Shrödinger is a scientific

More information

Similarity Search. Uwe Koch

Similarity Search. Uwe Koch Similarity Search Uwe Koch Similarity Search The similar property principle: strurally similar molecules tend to have similar properties. However, structure property discontinuities occur frequently. Relevance

More information

Fragment based drug discovery in teams of medicinal and computational chemists. Carsten Detering

Fragment based drug discovery in teams of medicinal and computational chemists. Carsten Detering Fragment based drug discovery in teams of medicinal and computational chemists Carsten Detering BioSolveIT Quick Facts Founded in 2001 by the developers of FlexX ~20 people Core expertise: docking, screening,

More information

LigandScout. Automated Structure-Based Pharmacophore Model Generation. Gerhard Wolber* and Thierry Langer

LigandScout. Automated Structure-Based Pharmacophore Model Generation. Gerhard Wolber* and Thierry Langer LigandScout Automated Structure-Based Pharmacophore Model Generation Gerhard Wolber* and Thierry Langer * E-Mail: wolber@inteligand.com Pharmacophores from LigandScout Pharmacophores & the Protein Data

More information

GC and CELPP: Workflows and Insights

GC and CELPP: Workflows and Insights GC and CELPP: Workflows and Insights Xianjin Xu, Zhiwei Ma, Rui Duan, Xiaoqin Zou Dalton Cardiovascular Research Center, Department of Physics and Astronomy, Department of Biochemistry, & Informatics Institute

More information

Human and Server CAPRI Protein Docking Prediction Using LZerD with Combined Scoring Functions. Daisuke Kihara

Human and Server CAPRI Protein Docking Prediction Using LZerD with Combined Scoring Functions. Daisuke Kihara Human and Server CAPRI Protein Docking Prediction Using LZerD with Combined Scoring Functions Daisuke Kihara Department of Biological Sciences Department of Computer Science Purdue University, Indiana,

More information

Retrieving hits through in silico screening and expert assessment M. N. Drwal a,b and R. Griffith a

Retrieving hits through in silico screening and expert assessment M. N. Drwal a,b and R. Griffith a Retrieving hits through in silico screening and expert assessment M.. Drwal a,b and R. Griffith a a: School of Medical Sciences/Pharmacology, USW, Sydney, Australia b: Charité Berlin, Germany Abstract:

More information

5.1. Hardwares, Softwares and Web server used in Molecular modeling

5.1. Hardwares, Softwares and Web server used in Molecular modeling 5. EXPERIMENTAL The tools, techniques and procedures/methods used for carrying out research work reported in this thesis have been described as follows: 5.1. Hardwares, Softwares and Web server used in

More information

Receptor Based Drug Design (1)

Receptor Based Drug Design (1) Induced Fit Model For more than 100 years, the behaviour of enzymes had been explained by the "lock-and-key" mechanism developed by pioneering German chemist Emil Fischer. Fischer thought that the chemicals

More information

Computational chemical biology to address non-traditional drug targets. John Karanicolas

Computational chemical biology to address non-traditional drug targets. John Karanicolas Computational chemical biology to address non-traditional drug targets John Karanicolas Our computational toolbox Structure-based approaches Ligand-based approaches Detailed MD simulations 2D fingerprints

More information

Comparative Analysis of Pharmacophore Screening Tools

Comparative Analysis of Pharmacophore Screening Tools pubs.acs.org/jcim Comparative Analysis of Pharmacophore Screening Tools Marijn P. A. Sanders,,# Armeńio J. M. Barbosa, Barbara Zarzycka, Gerry A.F. Nicolaes, Jan P.G. Klomp, Jacob de Vlieg,, and Alberto

More information

Towards Physics-based Models for ADME/Tox. Tyler Day

Towards Physics-based Models for ADME/Tox. Tyler Day Towards Physics-based Models for ADME/Tox Tyler Day Overview Motivation Application: P450 Site of Metabolism Application: Membrane Permeability Future Directions and Applications Motivation Advantages

More information

Structural biology and drug design: An overview

Structural biology and drug design: An overview Structural biology and drug design: An overview livier Taboureau Assitant professor Chemoinformatics group-cbs-dtu otab@cbs.dtu.dk Drug discovery Drug and drug design A drug is a key molecule involved

More information

Navigation in Chemical Space Towards Biological Activity. Peter Ertl Novartis Institutes for BioMedical Research Basel, Switzerland

Navigation in Chemical Space Towards Biological Activity. Peter Ertl Novartis Institutes for BioMedical Research Basel, Switzerland Navigation in Chemical Space Towards Biological Activity Peter Ertl Novartis Institutes for BioMedical Research Basel, Switzerland Data Explosion in Chemistry CAS 65 million molecules CCDC 600 000 structures

More information

The Long and Rocky Road from a PDB File to a Protein Ligand Docking Score. Protein Structures: The Starting Point for New Drugs 2

The Long and Rocky Road from a PDB File to a Protein Ligand Docking Score. Protein Structures: The Starting Point for New Drugs 2 The Long and Rocky Road from a PDB File to a Protein Ligand Docking Score Protein Structures: The Starting Point for New Drugs 2 Matthias Rarey Stefan Bietz Nadine Schneider Sascha Urbaczek University

More information

Medicinal Chemistry/ CHEM 458/658 Chapter 4- Computer-Aided Drug Design

Medicinal Chemistry/ CHEM 458/658 Chapter 4- Computer-Aided Drug Design Medicinal Chemistry/ CHEM 458/658 Chapter 4- Computer-Aided Drug Design Bela Torok Department of Chemistry University of Massachusetts Boston Boston, MA 1 Computer Aided Drug Design - Introduction Development

More information

Virtual Screening: How Are We Doing?

Virtual Screening: How Are We Doing? Virtual Screening: How Are We Doing? Mark E. Snow, James Dunbar, Lakshmi Narasimhan, Jack A. Bikker, Dan Ortwine, Christopher Whitehead, Yiannis Kaznessis, Dave Moreland, Christine Humblet Pfizer Global

More information

Fragment Hotspot Maps: A CSD-derived Method for Hotspot identification

Fragment Hotspot Maps: A CSD-derived Method for Hotspot identification Fragment Hotspot Maps: A CSD-derived Method for Hotspot identification Chris Radoux www.ccdc.cam.ac.uk radoux@ccdc.cam.ac.uk 1 Introduction Hotspots Strongly attractive to organic molecules Organic molecules

More information

CMPS 6630: Introduction to Computational Biology and Bioinformatics. Structure Comparison

CMPS 6630: Introduction to Computational Biology and Bioinformatics. Structure Comparison CMPS 6630: Introduction to Computational Biology and Bioinformatics Structure Comparison Protein Structure Comparison Motivation Understand sequence and structure variability Understand Domain architecture

More information

Softwares for Molecular Docking. Lokesh P. Tripathi NCBS 17 December 2007

Softwares for Molecular Docking. Lokesh P. Tripathi NCBS 17 December 2007 Softwares for Molecular Docking Lokesh P. Tripathi NCBS 17 December 2007 Molecular Docking Attempt to predict structures of an intermolecular complex between two or more molecules Receptor-ligand (or drug)

More information

Hit Finding and Optimization Using BLAZE & FORGE

Hit Finding and Optimization Using BLAZE & FORGE Hit Finding and Optimization Using BLAZE & FORGE Kevin Cusack,* Maria Argiriadi, Eric Breinlinger, Jeremy Edmunds, Michael Hoemann, Michael Friedman, Sami Osman, Raymond Huntley, Thomas Vargo AbbVie, Immunology

More information

Development of Pharmacophore Model for Indeno[1,2-b]indoles as Human Protein Kinase CK2 Inhibitors and Database Mining

Development of Pharmacophore Model for Indeno[1,2-b]indoles as Human Protein Kinase CK2 Inhibitors and Database Mining Development of Pharmacophore Model for Indeno[1,2-b]indoles as Human Protein Kinase CK2 Inhibitors and Database Mining Samer Haidar 1, Zouhair Bouaziz 2, Christelle Marminon 2, Tiomo Laitinen 3, Anti Poso

More information

A Brief Guide to All Atom/Coarse Grain Simulations 1.1

A Brief Guide to All Atom/Coarse Grain Simulations 1.1 A Brief Guide to All Atom/Coarse Grain Simulations 1.1 Contents 1. Introduction 2. AACG simulations 2.1. Capabilities and limitations 2.2. AACG commands 2.3. Visualization and analysis 2.4. AACG simulation

More information

What is Protein-Ligand Docking?

What is Protein-Ligand Docking? MOLECULAR DOCKING Definition: What is Protein-Ligand Docking? Computationally predict the structures of protein-ligand complexes from their conformations and orientations. The orientation that maximizes

More information

BioSolveIT. A Combinatorial Approach for Handling of Protonation and Tautomer Ambiguities in Docking Experiments

BioSolveIT. A Combinatorial Approach for Handling of Protonation and Tautomer Ambiguities in Docking Experiments BioSolveIT Biology Problems Solved using Information Technology A Combinatorial Approach for andling of Protonation and Tautomer Ambiguities in Docking Experiments Ingo Dramburg BioSolve IT Gmb An der

More information

A Tiered Screen Protocol for the Discovery of Structurally Diverse HIV Integrase Inhibitors

A Tiered Screen Protocol for the Discovery of Structurally Diverse HIV Integrase Inhibitors A Tiered Screen Protocol for the Discovery of Structurally Diverse HIV Integrase Inhibitors Rajarshi Guha, Debojyoti Dutta, Ting Chen and David J. Wild School of Informatics Indiana University and Dept.

More information

Using AutoDock for Virtual Screening

Using AutoDock for Virtual Screening Using AutoDock for Virtual Screening CUHK Croucher ASI Workshop 2011 Stefano Forli, PhD Prof. Arthur J. Olson, Ph.D Molecular Graphics Lab Screening and Virtual Screening The ultimate tool for identifying

More information

Dock Ligands from a 2D Molecule Sketch

Dock Ligands from a 2D Molecule Sketch Dock Ligands from a 2D Molecule Sketch March 31, 2016 Sample to Insight CLC bio, a QIAGEN Company Silkeborgvej 2 Prismet 8000 Aarhus C Denmark Telephone: +45 70 22 32 44 www.clcbio.com support-clcbio@qiagen.com

More information

MM-GBSA for Calculating Binding Affinity A rank-ordering study for the lead optimization of Fxa and COX-2 inhibitors

MM-GBSA for Calculating Binding Affinity A rank-ordering study for the lead optimization of Fxa and COX-2 inhibitors MM-GBSA for Calculating Binding Affinity A rank-ordering study for the lead optimization of Fxa and COX-2 inhibitors Thomas Steinbrecher Senior Application Scientist Typical Docking Workflow Databases

More information

October 6 University Faculty of pharmacy Computer Aided Drug Design Unit

October 6 University Faculty of pharmacy Computer Aided Drug Design Unit October 6 University Faculty of pharmacy Computer Aided Drug Design Unit CADD@O6U.edu.eg CADD Computer-Aided Drug Design Unit The development of new drugs is no longer a process of trial and error or strokes

More information

The Conformation Search Problem

The Conformation Search Problem Jon Sutter Senior Manager Life Sciences R&D jms@accelrys.com Jiabo Li Senior Scientist Life Sciences R&D jli@accelrys.com CAESAR: Conformer Algorithm based on Energy Screening and Recursive Buildup The

More information

Dr. Sander B. Nabuurs. Computational Drug Discovery group Center for Molecular and Biomolecular Informatics Radboud University Medical Centre

Dr. Sander B. Nabuurs. Computational Drug Discovery group Center for Molecular and Biomolecular Informatics Radboud University Medical Centre Dr. Sander B. Nabuurs Computational Drug Discovery group Center for Molecular and Biomolecular Informatics Radboud University Medical Centre The road to new drugs. How to find new hits? High Throughput

More information

The reuse of structural data for fragment binding site prediction

The reuse of structural data for fragment binding site prediction The reuse of structural data for fragment binding site prediction Richard Hall 1 Motivation many examples of fragments binding in a phenyl shaped pocket or a kinase slot good shape complementarity between

More information

A Deep Convolutional Neural Network for Bioactivity Prediction in Structure-based Drug Discovery

A Deep Convolutional Neural Network for Bioactivity Prediction in Structure-based Drug Discovery AtomNet A Deep Convolutional Neural Network for Bioactivity Prediction in Structure-based Drug Discovery Izhar Wallach, Michael Dzamba, Abraham Heifets Victor Storchan, Institute for Computational and

More information

SCULPT 3.0. Using SCULPT to Gain Competitive Insights. Brings 3D Visualization to the Lab Bench SPECIAL REPORT. 4 Molecular Connection Fall 1999

SCULPT 3.0. Using SCULPT to Gain Competitive Insights. Brings 3D Visualization to the Lab Bench SPECIAL REPORT. 4 Molecular Connection Fall 1999 SPECIAL REPORT SCULPT 3.0 Brings 3D Visualization to the Lab Bench ith the acquisition of Interactive Simulations, Inc., last April, MDL added to its arsenal of discovery informatics tools a powerful analysis

More information

Focusing Conformational Ensembles on Bioactive-Like Conformations

Focusing Conformational Ensembles on Bioactive-Like Conformations Focusing Conformational Ensembles on Bioactive-Like Conformations Hannah H. Avgy, Boaz Musafia, Hanoch Senderowitz Department of Chemistry Bar-Ilan University 3rd Strasbourg Summer School on Chemoinformatics,

More information

The Schrödinger KNIME extensions

The Schrödinger KNIME extensions The Schrödinger KNIME extensions Computational Chemistry and Cheminformatics in a workflow environment Jean-Christophe Mozziconacci Volker Eyrich KNIME UGM, Zurich, February 2014 The Schrödinger extensions

More information

DISCRETE TUTORIAL. Agustí Emperador. Institute for Research in Biomedicine, Barcelona APPLICATION OF DISCRETE TO FLEXIBLE PROTEIN-PROTEIN DOCKING:

DISCRETE TUTORIAL. Agustí Emperador. Institute for Research in Biomedicine, Barcelona APPLICATION OF DISCRETE TO FLEXIBLE PROTEIN-PROTEIN DOCKING: DISCRETE TUTORIAL Agustí Emperador Institute for Research in Biomedicine, Barcelona APPLICATION OF DISCRETE TO FLEXIBLE PROTEIN-PROTEIN DOCKING: STRUCTURAL REFINEMENT OF DOCKING CONFORMATIONS Emperador

More information

Drug Informatics for Chemical Genomics...

Drug Informatics for Chemical Genomics... Drug Informatics for Chemical Genomics... An Overview First Annual ChemGen IGERT Retreat Sept 2005 Drug Informatics for Chemical Genomics... p. Topics ChemGen Informatics The ChemMine Project Library Comparison

More information

PharmDock: A Pharmacophore-Based Docking Program

PharmDock: A Pharmacophore-Based Docking Program Purdue University Purdue e-pubs Department of Medicinal Chemistry and Molecular Pharmacology Faculty Publications Department of Medicinal Chemistry and Molecular Pharmacology 4-16-2014 PharmDock: A Pharmacophore-Based

More information

Introduction to Chemoinformatics and Drug Discovery

Introduction to Chemoinformatics and Drug Discovery Introduction to Chemoinformatics and Drug Discovery Irene Kouskoumvekaki Associate Professor February 15 th, 2013 The Chemical Space There are atoms and space. Everything else is opinion. Democritus (ca.

More information

Cross Discipline Analysis made possible with Data Pipelining. J.R. Tozer SciTegic

Cross Discipline Analysis made possible with Data Pipelining. J.R. Tozer SciTegic Cross Discipline Analysis made possible with Data Pipelining J.R. Tozer SciTegic System Genesis Pipelining tool created to automate data processing in cheminformatics Modular system built with generic

More information

BioSolveIT. A Combinatorial Docking Approach for Dealing with Protonation and Tautomer Ambiguities

BioSolveIT. A Combinatorial Docking Approach for Dealing with Protonation and Tautomer Ambiguities BioSolveIT Biology Problems Solved using Information Technology A Combinatorial Docking Approach for Dealing with Protonation and Tautomer Ambiguities Ingo Dramburg BioSolve IT Gmb An der Ziegelei 75 53757

More information

ICM-Chemist-Pro How-To Guide. Version 3.6-1h Last Updated 12/29/2009

ICM-Chemist-Pro How-To Guide. Version 3.6-1h Last Updated 12/29/2009 ICM-Chemist-Pro How-To Guide Version 3.6-1h Last Updated 12/29/2009 ICM-Chemist-Pro ICM 3D LIGAND EDITOR: SETUP 1. Read in a ligand molecule or PDB file. How to setup the ligand in the ICM 3D Ligand Editor.

More information

Generating Small Molecule Conformations from Structural Data

Generating Small Molecule Conformations from Structural Data Generating Small Molecule Conformations from Structural Data Jason Cole cole@ccdc.cam.ac.uk Cambridge Crystallographic Data Centre 1 The Cambridge Crystallographic Data Centre About us A not-for-profit,

More information

Schrödinger Workshop 2013

Schrödinger Workshop 2013 Schrödinger Workshop 2013 Structure Based Virtual Screening -Various Approaches Jas Bhachoo Schrodinger Senior Applications Scientist Your Files for Today 4 main directories Open the latest *prjzip file

More information

Binding Response: A Descriptor for Selecting Ligand Binding Site on Protein Surfaces

Binding Response: A Descriptor for Selecting Ligand Binding Site on Protein Surfaces J. Chem. Inf. Model. 2007, 47, 2303-2315 2303 Binding Response: A Descriptor for Selecting Ligand Binding Site on Protein Surfaces Shijun Zhong and Alexander D. MacKerell, Jr.* Computer-Aided Drug Design

More information

In silico pharmacology for drug discovery

In silico pharmacology for drug discovery In silico pharmacology for drug discovery In silico drug design In silico methods can contribute to drug targets identification through application of bionformatics tools. Currently, the application of

More information

Introduction to FBDD Fragment screening methods and library design

Introduction to FBDD Fragment screening methods and library design Introduction to FBDD Fragment screening methods and library design Samantha Hughes, PhD Fragments 2013 RSC BMCS Workshop 3 rd March 2013 Copyright 2013 Galapagos NV Why fragment screening methods? Guess

More information

ESPRESSO (Extremely Speedy PRE-Screening method with Segmented compounds) 1

ESPRESSO (Extremely Speedy PRE-Screening method with Segmented compounds) 1 Vol.2016-MPS-108 o.18 Vol.2016-BI-46 o.18 ESPRESS 1,4,a) 2,4 2,4 1,3 1,3,4 1,3,4 - ESPRESS (Extremely Speedy PRE-Screening method with Segmented cmpounds) 1 Glide HTVS ESPRESS 2,900 200 ESPRESS: An ultrafast

More information

STRUCTURAL BIOINFORMATICS II. Spring 2018

STRUCTURAL BIOINFORMATICS II. Spring 2018 STRUCTURAL BIOINFORMATICS II Spring 2018 Syllabus Course Number - Classification: Chemistry 5412 Class Schedule: Monday 5:30-7:50 PM, SERC Room 456 (4 th floor) Instructors: Ronald Levy, SERC 718 (ronlevy@temple.edu)

More information

Implementation of novel tools to facilitate fragment-based drug discovery by NMR:

Implementation of novel tools to facilitate fragment-based drug discovery by NMR: Implementation of novel tools to facilitate fragment-based drug discovery by NMR: Automated analysis of large sets of ligand-observed NMR binding data and 19 F methods Andreas Lingel Global Discovery Chemistry

More information

CHAPTER-2. Drug discovery is a comprehensive approach wherein several disciplines

CHAPTER-2. Drug discovery is a comprehensive approach wherein several disciplines 36 CAPTER-2 Molecular Modeling Analog Based Studies Drug discovery is a comprehensive approach wherein several disciplines are used to design or discover the drugs. The R&D expenditure incurred to bring

More information

Molecular Interactions F14NMI. Lecture 4: worked answers to practice questions

Molecular Interactions F14NMI. Lecture 4: worked answers to practice questions Molecular Interactions F14NMI Lecture 4: worked answers to practice questions http://comp.chem.nottingham.ac.uk/teaching/f14nmi jonathan.hirst@nottingham.ac.uk (1) (a) Describe the Monte Carlo algorithm

More information

Practical QSAR and Library Design: Advanced tools for research teams

Practical QSAR and Library Design: Advanced tools for research teams DS QSAR and Library Design Webinar Practical QSAR and Library Design: Advanced tools for research teams Reservationless-Plus Dial-In Number (US): (866) 519-8942 Reservationless-Plus International Dial-In

More information

Structure-Activity Modeling - QSAR. Uwe Koch

Structure-Activity Modeling - QSAR. Uwe Koch Structure-Activity Modeling - QSAR Uwe Koch QSAR Assumption: QSAR attempts to quantify the relationship between activity and molecular strcucture by correlating descriptors with properties Biological activity

More information

Using Bayesian Statistics to Predict Water Affinity and Behavior in Protein Binding Sites. J. Andrew Surface

Using Bayesian Statistics to Predict Water Affinity and Behavior in Protein Binding Sites. J. Andrew Surface Using Bayesian Statistics to Predict Water Affinity and Behavior in Protein Binding Sites Introduction J. Andrew Surface Hampden-Sydney College / Virginia Commonwealth University In the past several decades

More information

Detection of Protein Binding Sites II

Detection of Protein Binding Sites II Detection of Protein Binding Sites II Goal: Given a protein structure, predict where a ligand might bind Thomas Funkhouser Princeton University CS597A, Fall 2007 1hld Geometric, chemical, evolutionary

More information

Conformational Sampling of Druglike Molecules with MOE and Catalyst: Implications for Pharmacophore Modeling and Virtual Screening

Conformational Sampling of Druglike Molecules with MOE and Catalyst: Implications for Pharmacophore Modeling and Virtual Screening J. Chem. Inf. Model. 2008, 48, 1773 1791 1773 Conformational Sampling of Druglike Molecules with MOE and Catalyst: Implications for Pharmacophore Modeling and Virtual Screening I-Jen Chen* and Nicolas

More information

Identifying Interaction Hot Spots with SuperStar

Identifying Interaction Hot Spots with SuperStar Identifying Interaction Hot Spots with SuperStar Version 1.0 November 2017 Table of Contents Identifying Interaction Hot Spots with SuperStar... 2 Case Study... 3 Introduction... 3 Generate SuperStar Maps

More information

Structural Bioinformatics (C3210) Molecular Docking

Structural Bioinformatics (C3210) Molecular Docking Structural Bioinformatics (C3210) Molecular Docking Molecular Recognition, Molecular Docking Molecular recognition is the ability of biomolecules to recognize other biomolecules and selectively interact

More information

Reaxys Medicinal Chemistry Fact Sheet

Reaxys Medicinal Chemistry Fact Sheet R&D SOLUTIONS FOR PHARMA & LIFE SCIENCES Reaxys Medicinal Chemistry Fact Sheet Essential data for lead identification and optimization Reaxys Medicinal Chemistry empowers early discovery in drug development

More information

Accuracy Assessment of Protein-Based Docking Programs against RNA Targets

Accuracy Assessment of Protein-Based Docking Programs against RNA Targets 1134 J. Chem. Inf. Model. 2010, 50, 1134 1146 Accuracy Assessment of Protein-Based Docking Programs against RNA Targets Yaozong Li, Jie Shen, Xianqiang Sun, Weihua Li, Guixia Liu, and Yun Tang* Department

More information

Prediction and refinement of NMR structures from sparse experimental data

Prediction and refinement of NMR structures from sparse experimental data Prediction and refinement of NMR structures from sparse experimental data Jeff Skolnick Director Center for the Study of Systems Biology School of Biology Georgia Institute of Technology Overview of talk

More information

Progress of Compound Library Design Using In-silico Approach for Collaborative Drug Discovery

Progress of Compound Library Design Using In-silico Approach for Collaborative Drug Discovery 21 th /June/2018@CUGM Progress of Compound Library Design Using In-silico Approach for Collaborative Drug Discovery Kaz Ikeda, Ph.D. Keio University Self Introduction Keio University, Tokyo, Japan (Established

More information

Introduction to Spark

Introduction to Spark 1 As you become familiar or continue to explore the Cresset technology and software applications, we encourage you to look through the user manual. This is accessible from the Help menu. However, don t

More information

Joana Pereira Lamzin Group EMBL Hamburg, Germany. Small molecules How to identify and build them (with ARP/wARP)

Joana Pereira Lamzin Group EMBL Hamburg, Germany. Small molecules How to identify and build them (with ARP/wARP) Joana Pereira Lamzin Group EMBL Hamburg, Germany Small molecules How to identify and build them (with ARP/wARP) The task at hand To find ligand density and build it! Fitting a ligand We have: electron

More information

Machine learning for ligand-based virtual screening and chemogenomics!

Machine learning for ligand-based virtual screening and chemogenomics! Machine learning for ligand-based virtual screening and chemogenomics! Jean-Philippe Vert Institut Curie - INSERM U900 - Mines ParisTech In silico discovery of molecular probes and drug-like compounds:

More information

Integrated Cheminformatics to Guide Drug Discovery

Integrated Cheminformatics to Guide Drug Discovery Integrated Cheminformatics to Guide Drug Discovery Matthew Segall, Ed Champness, Peter Hunt, Tamsin Mansley CINF Drug Discovery Cheminformatics Approaches August 23 rd 2017 Optibrium, StarDrop, Auto-Modeller,

More information

Scoring functions for of protein-ligand docking: New routes towards old goals

Scoring functions for of protein-ligand docking: New routes towards old goals 3nd Strasbourg Summer School on Chemoinformatics Strasbourg, June 25-29, 2012 Scoring functions for of protein-ligand docking: New routes towards old goals Christoph Sotriffer Institute of Pharmacy and

More information

Drug Design 2. Oliver Kohlbacher. Winter 2009/ QSAR Part 4: Selected Chapters

Drug Design 2. Oliver Kohlbacher. Winter 2009/ QSAR Part 4: Selected Chapters Drug Design 2 Oliver Kohlbacher Winter 2009/2010 11. QSAR Part 4: Selected Chapters Abt. Simulation biologischer Systeme WSI/ZBIT, Eberhard-Karls-Universität Tübingen Overview GRIND GRid-INDependent Descriptors

More information

Tutorial. Getting started. Sample to Insight. March 31, 2016

Tutorial. Getting started. Sample to Insight. March 31, 2016 Getting started March 31, 2016 Sample to Insight CLC bio, a QIAGEN Company Silkeborgvej 2 Prismet 8000 Aarhus C Denmark Telephone: +45 70 22 32 44 www.clcbio.com support-clcbio@qiagen.com Getting started

More information

QSAR Modeling of ErbB1 Inhibitors Using Genetic Algorithm-Based Regression

QSAR Modeling of ErbB1 Inhibitors Using Genetic Algorithm-Based Regression APPLICATION NOTE QSAR Modeling of ErbB1 Inhibitors Using Genetic Algorithm-Based Regression GAINING EFFICIENCY IN QUANTITATIVE STRUCTURE ACTIVITY RELATIONSHIPS ErbB1 kinase is the cell-surface receptor

More information

Targeting protein-protein interactions: A hot topic in drug discovery

Targeting protein-protein interactions: A hot topic in drug discovery Michal Kamenicky; Maria Bräuer; Katrin Volk; Kamil Ödner; Christian Klein; Norbert Müller Targeting protein-protein interactions: A hot topic in drug discovery 104 Biomedizin Innovativ patientinnenfokussierte,

More information

Protein Structure Prediction and Protein-Ligand Docking

Protein Structure Prediction and Protein-Ligand Docking Protein Structure Prediction and Protein-Ligand Docking Björn Wallner bjornw@ifm.liu.se Jan. 24, 2014 Todays topics Protein Folding Intro Protein structure prediction How can we predict the structure of

More information

Cheminformatics platform for drug discovery application

Cheminformatics platform for drug discovery application EGI-InSPIRE Cheminformatics platform for drug discovery application Hsi-Kai, Wang Academic Sinica Grid Computing EGI User Forum, 13, April, 2011 1 Introduction to drug discovery Computing requirement of

More information

New approaches to scoring function design for protein-ligand binding affinities. Richard A. Friesner Columbia University

New approaches to scoring function design for protein-ligand binding affinities. Richard A. Friesner Columbia University New approaches to scoring function design for protein-ligand binding affinities Richard A. Friesner Columbia University Overview Brief discussion of advantages of empirical scoring approaches Analysis

More information

Docking with Water in the Binding Site using GOLD

Docking with Water in the Binding Site using GOLD Docking with Water in the Binding Site using GOLD Version 2.0 November 2017 GOLD v5.6 Table of Contents Docking with Water in the Binding Site... 2 Case Study... 3 Introduction... 3 Provided Input Files...

More information