TCOF 3 :Repositioning of Chemical compounds From Different Classes as part of Virtual Screening Under the Guidance of PI: Dr UCA JALEEL, Dr Bheemarao Ugarkar.

Slides:

Advertisements

Similar presentations

Distributed Drug Discovery Indiana University Purdue University, Indianapolis.

Advertisements

EndNote Web Reference Management Software (module 5.1)

EndNote Web Reference Management Software (module 5)

Creating a reading list in Moodle Learning Technologists, Centre for Learning Technology This work is licensed under a Creative Commons Attribution-ShareAlike.

School of Nursing and Midwifery Studies Research Review and Ethics Screening Committee Overview The purpose of this interactive flowchart is to assist.

Analysis of High-Throughput Screening Data C371 Fall 2004.

Miroslav Brumovský28th July 2011 Miroslav Brumovský 28 th July 2011 Methods using polarization for in silico fragment-based drug design.

Random Forest Predrag Radenković 3237/10

C2D Cheminformatics : Methods,Tools and Results By OSDD-Cheminformatics team.

1 PharmID: A New Algorithm for Pharmacophore Identification Stan Young Jun Feng and Ashish Sanil NISSMPDM 3 June 2005.

Jeffery Loo NLM Associate Fellow ’03 – ’05 chemicalinformaticsforlibraries.

CoolMolecules: A Molecular Structure Explorer Robert M. Hanson, Melanie Casavant, Michael McGuan.

August 29, 2002InforMax Confidential1 Vector PathBlazer Product Overview.

Simon Briggs Department of Clinical Pharmacology University of Oxford 13 th June 2008 Data management – A researchers prospective.

Active Learning Strategies for Drug Screening 1. Introduction At the intersection of drug discovery and experimental design, active learning algorithms.

1 BrainWave Biosolutions Limited Accelerating Life Science Research through Technology.

Customer Satisfaction/Loyalty Turna Koksal. Goal Characterize the customer of a bank Customer satisfaction Customer loyalty Relationship between satisfaction.

20/03/2008 Dept. of Pharmaceutics 1 APPLICATIONS OF BIOINFORMATICS IN DRUG DISCOVERY AND PROCESS RESEARCH Dr. Basavaraj K. Nanjwade M.Pharm., Ph.D Associate.

ICT TEACHERS` COMPETENCIES FOR THE KNOWLEDGE SOCIETY

CS 5604 Spring 2015 Classification Xuewen Cui Rongrong Tao Ruide Zhang May 5th, 2015.

GGAGATTCTGGGCCACTTTGGTTCCCCATGAGCCAAGACGGCACTTCTAATTTGCATTCCCTACCGGAGTCCCTGTCTGTAGCCAGCCTGGCTTTCAGCTGGTGCCCAAAGTGACAAATGTATCTGCAATGACAAAGGTAC CCTGGAAGGGCTCGCCCTCTGCGGAATTTCAGTTCATGCAGGCCTTGGTGCTTCCACATCTGTCCAAGGGCCTTTCAAATGTGACTTTTAACTCTGTGGATTGATTTGCCCGG

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

Asia’s Largest Global Software & Services Company Genomes to Drugs: A Bioinformatics Perspective Sharmila Mande Bioinformatics Division Advanced Technology.

Topological Summaries: Using Graphs for Chemical Searching and Mining Graphs are a flexible & unifying model Scalable similarity searches through novel.

ANTI-MICROBIALS – MEDICINAL CHEMISTRY Timothy Curd - Sunderland University - Supervised by Mark Ashton and Dr Yu Gong. Special acknowledgement to the Nuffield.

Appendix: The WEKA Data Mining Software

Introduction to Chemoinformatics Irene Kouskoumvekaki Associate Professor December 12th, 2012 Biological Sequence Analysis course.

EXPLORING CHEMICAL SPACE FOR DRUG DISCOVERY Daniel Svozil Laboratory of Informatics and Chemistry.

IBM Global Services © 2010 IBM Corporation RTC Work Item Importing Anoop

High-Throughput Virtual Molecular Docking: Hadoop Implementation of AutoDock4 on a Private Cloud Sally R. Ellingson Graduate Research Assistant Center.

Group work based assessment in distance learning Dr Clare Sansom, CDE Fellow 20 July 2015.

Protein Local 3D Structure Prediction by Super Granule Support Vector Machines (Super GSVM) Dr. Bernard Chen Assistant Professor Department of Computer.

1 G. P. S. Raghava Institute of Microbial Technology, Chandigarh.

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

Hands-on predictive models and machine learning for software Foutse Khomh, Queen’s University Segla Kpodjedo, École Polytechnique de Montreal PASED - Canadian.

20/03/2008 Dept. of Pharmaceutics 1. Use of BIOINFORMATICS in Pharmaciutics 2  Presented By  Shafnan Nazar  Hamid Nasir 

“Emergency discovery” of novel antimicrobials among known drugs in response to new and re-emerging infectious threats A. Cherkasov UBC / VGH Infectious.

Open source software and web services for designing therapeutic molecules G. P. S. Raghava, Head Bioinformatics Centre, Institute of Microbial Technology,

Machine Learning with Weka Cornelia Caragea Thanks to Eibe Frank for some of the slides.

Page 1 SCAI Dr. Marc Zimmermann Department of Bioinformatics Fraunhofer Institute for Algorithms and Scientific Computing (SCAI) Grid-enabled drug discovery.

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

Anis Karimpour-Fard ‡, Ryan T. Gill †,

PREDICTION OF CATALYTIC RESIDUES IN PROTEINS USING MACHINE-LEARNING TECHNIQUES Natalia V. Petrova (Ph.D. Student, Georgetown University, Biochemistry Department),

A collaborative tool for sequence annotation. Contact:

December 1, Classification Analysis of HIV RNase H Bioassay Lianyi Han Computational Biology Branch NCBI/NLM/NIH Rocky ‘07.

Kaggle Competition Prudential Life Insurance Assessment

An Exercise in Machine Learning

Introduction to Chemoinformatics and Drug Discovery Irene Kouskoumvekaki Associate Professor February 15 th, 2013.

***Classification Model*** Hosam Al-Samarraie, PhD. CITM-USM.

Discovering functional interaction patterns in Protein-Protein Interactions Networks   Authors: Mehmet E Turnalp Tolga Can Presented By: Sandeep Kumar.

Competition II: Springleaf Sha Li (Team leader) Xiaoyan Chong, Minglu Ma, Yue Wang CAMCOS Fall 2015 San Jose State University.

Use of Machine Learning in Chemoinformatics

Final Report (30% final score) Bin Liu, PhD, Associate Professor.

Developed by 360 Technologies Pvt Ltd. EMTTS(Express Mail Track and Trace System) is an application of Pakistan Post aimed to -Assist bookings. -Delivery.

Investigate Plan Design Create Evaluate (Test it to objective evaluation at each stage of the design cycle) state – describe - explain the problem some.

2014 Using machine learning to predict binding sites in proteins Jenelle Bray Stanford University October 10, 2014 #GHC

TCOF 3 :Repositioning of Chemical compounds From Different Classes as part of Virtual Screening Under the Guidance of PI: Dr UCA JALEEL (IISc Research.

Improving compound–protein interaction prediction by building up highly credible negative samples Toward more realistic drug-target interaction predictions.

Docking and Virtual Screening Using the BMI cluster

HP-SEE In the search of the HDAC-1 inhibitors. The preliminary results of ligand based virtual screening Ilija N. Cvijetić, Ivan O. Juranić,

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.

The CompTox Chemistry Dashboard: an informational data hub at the

A Smart Tool to Predict Salary Trends of H1-B Holders

Risk scoring tool Prague – June 2017

Smart Health Prediction System

APPLICATIONS OF BIOINFORMATICS IN DRUG DISCOVERY

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

Machine Learning with Weka

Introduction to Bioinformatic

Machine Learning for Cyber

Presentation transcript:

TCOF 3 :Repositioning of Chemical compounds From Different Classes as part of Virtual Screening Under the Guidance of PI: Dr UCA JALEEL, Dr Bheemarao Ugarkar (IISc Research Unit, Bangalore) Yatindra Nath Yadav 3.4TCOF Fellow (MSc Tech Bioinformatics, WBUT,KOLKATA) Blog Url: yatindradotnet.wordpress.com

The aim of this project is to develop classes of anti MTb compounds and reposition them by screening pesticides which are found active against TB which we can further proceed with clinical trials. Repositioning of Chemical compound database divided under three sub classes:- 1)Pesticides 2)Antimicrobial molecules 3)Phytomolecules -> Me and My Group worked on Pesticides showing anti TB activity: In search of Pesticide database we started with many search engine like Pubchem,PAN (Pesticide Action Network) pesticide database,Eu Pesticide Database & finally our search comes to an end with EPA (Environmental Protection Agency).Environmental Protection Agency In EPA we got some 654 pesticide molecules out of which we have structure and SDF file for 487 molecules remaining structure is drawn by (Ayisha safeeda) with the help of “MARVIN” and saved in SDF file format.

Data Scientific Authentication For getting scientific background against 657 EPA registered Pesticide molecules we dropped couple of mails to following contacts:- 1) 2) 3) 4) Next Slide will give an over view of the Project in the form of Flowchart to Explain the process.

AID 1332 Upload the sdf file All compounds sdf file Generate descriptor file Open the CSV file in Excel Testing TrainingFile splitting Remove the useless attributes Select the actives and inactive compounds Apply classifier algorithms Selection of best classifier model TP %, FP 70% Append the bioassay result corresponding to the compounds PubChem PowerMV Excel WEKA (machine learning) Module – Work Flow

Current Stage of Project is Tuning of Model Generated by WEKA: We are trying to Tune the Model (selecting best classifier)to the Most Stable state Applying the Cost Matrix on it. We have generated the Results using different Classifiers like Naïve bayes and Random Forest We are trying to Tune the Model giving the Cost Matrix to it as shown in above excel sheet. Next Stage is to Go for Screening and then We will proceed Further ….

References: 1) Schierz AC. Virtual screening of bioassay data. J Cheminform Dec 22;1:21. doi: / PubMed PMID: ) Periwal V etal., Predictive models for anti-tubercular molecules using machine learning on high-throughput biological screening datasets. BMC Res Notes Nov18;4:504. doi: / PubMed PMID: ) Enviornmental Protection Agency (EPA).

We are indebted to and earnestly acknowledge Prof Dr Samir K Bramachari Dr TS Balganesh Dr. U.C. Jaleel, PI (TOCF 3) Dr Bheemarao Ugarkar IISc Research Unit, Bangalore OSDD open lab team Group Members (Swati shah,Ayisha Safeeda & Nufail)

Some Feed Back Needed Regarding Proposed Idea can we follow this approach….????

Some feed backs needed on this Idea and software from Team & PI SAR (Structural Activity Relationship) w.r.t field Points Concept:-  Seeing the Ligand the way they experienced by protein receptor:-Taking 3D Molecular Electrostatic Potential(Field points) viz:-Positive charge, negative charge,shape,hydrophocity.

Now the present of Field points suggest that this is an area of the ligand which will form the favorable interaction with protein receptor provided off course the protein receptor have complimentary features.

Cluster View

DISPARITY MATRIXS

Activity Miner

Thanks for your patience OSDD fleets towards the ultimate TB drug