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List of Updated Publications (click) MILESTONES
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We are working in the area of chemoinformatics for the past two decades especially to develop tools for academic/industrial research. In this direction we made several predictive studies related to Drug Discovery Research (QSAR, QSPR and QSTR) . We applied QSPR strategy for predicting Melting point of diverse class of organic molecules . As part of innovative research we developed a methodology for molecular encoding as barcodes [click] with truly computable structures for inventory management. In order to handle high data we developed a program ChemXtreme to harvest chemical information from entire Internet using search engines like Google and extracted data such molecular properties, activities, and toxicity of molecules were converted in to specialized databases. ChemStar(5) is another program developed to handle large amount of molecular data using Distributed computing environment (Ref 4-5) and applied for calculating molecular properties for the entire collection of PubChem database. We also contributed in compiling MSDS datasheets for Central Pollution Control Board-New Delhi. Chemical Data mining of Indian Medicinal Plants and Traditional Chinese medicine from Scientific literature covering past four decades to build DoMINE (in progress).
Text Book : Practical Chemoinformatics , Springer 2014 (ISBN: 978-81-322-1779-4) http://www.springer.com/chemistry/book/978-81-322-1779-4
To create awareness and promote CHEMOINFORMATICS among the academic community and other scientific funding agencies in India, we recently organized an International Conference on Chemoinformatics (Jan 22-24, 2007) at National Chemical Laboratory, first of its kind in the country [photos] [Read More..]. We also provide academic and industrial training in building chemical-biological databases related to chemoinformatics and other In-house built Databases and commercial databases and Molecular Informatics Tools.
What is new ?
Chemoinformatics for virtual screening and drug discovery [Epub ahead of print] Comb Chem High Throughput Screen. 2015 Jul 3.
1: M Karthikeyan, Pandit D, Vyas R. Protein ligand complex guided approach for virtual screening. Comb Chem High Throughput Screen. 2015 Jul 3. PubMed PMID: 26138572.
2: Karthikeyan M, Vyas R. Role of open source tools and resources in virtual screening for drug discovery. Comb Chem High Throughput Screen. 2015 Jul 3. [Epubahead of print] PubMed PMID: 26138575.
3: Karthikeyan M, Pandit D, Vyas R. ChemScreener: A Distributed Computing Tool for Scaffold based Virtual Screening. Comb Chem High Throughput Screen. 2015 Jul 3. PubMed PMID: 26138574.
4: Vyas R, Bapat S, Jain E, Tambe SS, Karthikeyan M, Kulkarni BD. A Study of Applications of Machine Learning Based Classification methods for VirtualScreening of Lead Molecules. Comb Chem High Throughput Screen. 2015 Jul 3. PubMed PMID: 26138573.
5: Vyas R, Karthikeyan M, Nainaru G, M Muthukrishnan. Pharmacophore and docking based virtual screening of validated Mycobacterium Tuberculosis targets. Comb Chem High Throughput Screen. 2015 Jul 3.PubMed PMID: 26138571.
6: Karthikeyan M, Nimje D, Pahujani R, Tyagi K, Bapat S, Vyas R,Padmakumar K. Chemoinformatics approach for building molecular networks from marine organisms. Comb Chem High Throughput Screen.2015 Jul 3. PubMed PMID: 26138570.
7: Karthikeyan M, Vyas R, Tambe SS, Radhamohan D, Kulkarni BD. Role of Chemical Reactivity and Transition State Modeling for Virtual Screening. Comb Chem High Throughput Screen. 2015 Jul 3. PubMed PMID:26138569.
8: Karthikeyan M, Rajamohanan PR, Vyas R. Prediction of bioactive compounds using computed NMR chemical shifts. Comb Chem High Throughput Screen. 2015 Jul 3. PubMed PMID: 26138568.
9: Karthikeyan M, Pandit Y, Pandit D, Vyas R. MegaMiner: A tool for lead identification through text mining using chemoinformatics tools and cloud computing environment. Comb Chem High Throughput Screen.2015 Jul 3. PubMed PMID: 26138567.
10: Karthikeyan M, Pandit D, Bhavasar A, Vyas R. Design and Development of ChemInfoCloud: An Integrated Cloud Enabled Platform for Virtual Screening. Comb Chem High Throughput Screen. 2015 Jul 3. PubMed PMID: 26138566.
Government Sponsored Projects:
Working in Molecular Informatics (Application of HPC tools using distributed and cloud computing architecture to handle large scale molecular data ~100 millions+ defining virtual chemical space of selected protein targets or therapeutic category, Organic reaction modeling using QM & QC and extension to biological systems (INSPIRE Project 12 FYP 2012-17), predictive QSAR (properties, toxicity, activity) , artificial neural networks and other machine learning tools, textmining, Visual computing for molecular informatics and application in drug design, lead optimization, materials, Education, Research and Management). Inventory and automation for sample tracking (NORMS 12 FYP 2012-17), chemical risk assessment and hazard analysis (Industrial Safety Processess modeling and simulations). Millions of docking being performed in a HPC enviroment to understand protein-ligand interactions by insilico studies (Figure). Practical Chemoinformatics (from Springer) highlights the power of programming computers for chemoinformatics applications.
Compute Molecular Descriptors using Moltable Portal! [Click]
International Conference on Chemoinformatics
Relevant Publications: (Big Data Challenges in Chemoinformatics: 10 billion web pages, 80 million molecules, 40 million Docking, Chemically intelligent Digital Eye)
Practical Chemoinformatics [BOOK] Springer 2014
1. WO WO/2013/030850 - CHEMICAL STRUCTURE RECOGNITION TOOL 07.03.2013 G06F 19/00 PCT/IN2012/000567 COUNCIL OF SCIENTIFIC & INDUSTRIAL RESEARCH KARTHIKEYAN, Muthukumarasamy
A method of extracting and then reusing / remodeling chemical data from a hand written or digital input image without manual inputs using Chemical Structure Recognition Tool (CSRT) is disclosed herein. It comprises loading said input image, converting said input image into a grayscale image i.e. stretching of loaded input image, converting said grayscale image into a binary image i.e. binarisation, smoothing to reduce noise within said binary image, recognizing circle bond to identify presence of a circle inside a ring, predicting OCR region to find zones containing text, image thinning to identify specific shapes within said binary image, edge detection to detect image contrast, detecting double and triple bond, and obtaining output files.
2. WO WO/2014/207670 - SIMULATED CARBON AND PROTON NMR CHEMICAL SHIFTS BASED BINARY FINGERPRINTS FOR VIRTUAL SCREENING 31.12.2014 G06F 19/00 PCT/IB2014/062585 COUNCIL OF SCIENTIFIC & INDUSTRIAL RESEARCH KARTHIKEYAN, Muthukumarasamy The invention discloses a method to generate and analyze NMR chemical shift based binary fingerprints for virtual high throughput screening in drug discovery. Further, the invention provides a method to analyze NMR chemical shifts based binary fingerprints that has implications for encoding several properties of a molecule besides the basic framework or scaffold and determine its propensity towards a particular bioactivity class.
Muthukumarasamy Karthikeyan Ph.D,
For Complete Profile:[CV-PDF]
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Muthukumarasamy Karthikeyan Ph.D, MBA
Principal Scientist, (CHEMOINFORMATICS)
Digital Information Resource Center (DIRC) & Centre of Excellence in Scientific Computing
[click to read PhD thesis (Organic Synthesis) Pune University ] [MBA: Marketing Generic Drugs in India (IGNOU)] && [Expertise: MSc Computer Science (FOSS) : ChemRobot: Visual Computing for Molecular Informatics (Anna University)]