Dr M Karthikeyan

Scientist Information Division, National Chemical Laboratory Dr. Homi Bhabha Road Pune 411008, INDIA. Tel. 91 20 25893457 Fax. 91 20 25893973 E-mail: m.karthikeyan@ncl.res.in

Current area of research : ChemoInformatics    (http://moltable.ncl.res.in) National Chemical Laboratory has initiated work in the emerging area called "chemoinformatics".  Chemoinformatics is a generic term that covers “ the design, creation, organization, storage, management, retrieval, analysis, dissemination, visualization and use” of chemical information. Chemoinformatics activities accelerate the transformation of data into knowledge by processing the information associated with the data.   q       Standards and Efficiency in Chemical Structure Representation q       Building Large Molecular structure database with associated data q       Technology Development (Encoding Methods) q       Chemical Data Mining Application in Chemistry  q       Design of knowledge based combi-libraries for drug-likeliness q       Database of chemical structures with predicted properties (solubility etc.,) q       Building Chemoinformatics portals to access molecular information q       Using Automation technology for chemical inventory (Barcodes, RF) q       High Performance Environment for Distributed / Grid Computing applications in chemistry q       Protein Databank Analysis for drug discovery q       Emerging Areas in Chem-Bioinformatics q       Machine Learning and Statistical Techniques for QSAR, QSPR and QSTR q       Design and development of Electronic Laboratory Notebooks q       Chemical Literature and Patent Analysis (Top Selling Drugs) q       Text based Mining & Information Harversting q       Open Archive Initiatives (Chemical Information Analysis) q       Training program in Chemoinformatics   q       Risk Assessment of Strategic Chemicals in the Environment

My group's interest is in developing tools, standards and protocols that would help in development of .. Recent Publications in peer reviewed journals Encoding and Decoding Graphical Chemical Structures as Two-Dimensional (PDF417) Barcodes J. Chem. Inf. Model.; 2005; 45(3) pp 572 - 580; (Article) DOI: 10.1021/ci049758i Abstract   Full:  HTML /  PDF (709K)  Supporting Info  General Melting Point Prediction Based on a Diverse Compound Data Set and Artificial Neural Networks J. Chem. Inf. Model.; 2005; 45(3) pp 581 - 590; (Article) DOI: 10.1021/ci0500132 Abstract   Full:  HTML /  PDF (421K)  Supporting Info  Patents / Copyrights -Interactive Chemical Information System (1996) -Computer Generated Automatic Chemical Structure Data-Base (CG-ACS-DB) 1999 -Internet Compatible BarCoding (ICBC) 2000 Awards BOYSCAST:(Better Opportunity for Young Scientist in the Chosen Areas of Science and Technology) fellowship at University of NorthCarolina at Chapel Hill, USA (2003-04) sponsored by Department of Science and Technology, New Delhi DRDO-New Delhi, Commendations (1999) Chemical Structure Association Trust Award (1996) Memberships in academies/societies/professional bodies My Group [Click] My Group   Projects  Details   Barcoding Chemical Structure Molecular inventory: Inventory of organic molecules synthesized by scientists needs to be analyzed for purity, authentication and activity. During this process tracking of the sample requires unique identification system. NCL has established a unique and direct barcording of truly computable chemical structures for direct molecular entries into various computational programs and inventory systems. The linear representation of molecules either in standard SMILES format or in-house developed compact ACS (Automatic Chemical Structure) format is directly barcoded and attached with chemical samples for inventory and tracking applications. The same encoding strategy can be used for emerging wireless based RF   Publication: Encoding and Decoding Molecular Structures as Two-Dimensional (PDF417) Barcodes   Predicting Properties of Molecules     Recently we successfully established a QSPR strategy for Predicting Melting point of diverse class of organic molecules from chemical structures using artificial neural network.  Publications: General Melting Point Prediction Based on a Diverse Compound Data Set and Artificial Neural Networks   The machine learning technique with sample of 5000+ organic molecules with experimental melting points along with computed 2D and 3D molecular descriptors were used for building the model. The similar strategy is applied for the analysis of biological activity data on molecular structures to predict biological activity (QSAR): Study on NCI-Cancer dataset of about 32,000 molecular structures and  NCI-AIDS dataset of about 40,000 molecular structures with activity profiles. Study on druglike compounds ~53,000 molecules with multiple “Therapeutic category”   National Repository of Molecules (NRM)   The main objective of this project is to build a web based chemical information system to handle electronic submission of analytical data and chemical structures as a national molecular repository at NCL. The truly computable chemical structures available for searching and inventory management. The samples will be barcoded for tracking the physical location and also to retrieve relevant data such as origin, handling, analytical, literature and computed data. The data distinguished as text data (meta-data), chemical structural data (truly computable) and analysis data either collected from specific instrument such as NMR, IR, MASS, HPLC etc., in standard re-usable format will be archived for future analysis. ChemXtreme   A new approach to search the search engines over the internet to analyse 8 billion pages containing chemical information and retrieve re-usable information from research and development activities in chemistry. The analysed data is being archived at NCL along with source of data such as URLs for future use and chemical Datamining. All the analysed data on evaluation and authentication will be made available to public. The same platform is being used for distributed computing applications in chemistry. Presentation: 7th International Conference on Chemical Structure, The Netherlands, 4-9 June 2005     In-silico Combinatorial Library Design: The Databases of chemical structures retrieved from private and public resources with drugs and drug likeliness molecules were analyzed for substructure, scaffolds and functional groups. The scaffolds derived from these datasets were further populated with additional functional groups to generate combi-libraries. These libraries of chemical structures were further analyzed by generation of molecular descriptors and screening algorithms for discovery drug like molecules. http://www.molecularsociety.org http://moltable.ncl.res.in Dspace@NCL Harvesting chemical information from PhD theses. thesis_1130.jsp [Source: Photoinduced Electron Transfer (Pet) Promoted Carboannulation Strategy- Arene Radical Cation In Carbon-Carbon Bond Formation Reaction (1998)] Support:  Dr S Sivaram (Director), Dr S Krishnan (Head, Inf. Divn) Dr KN Ganesh, Dr S Pal, Dr Vijayamohan, Dr Idage   Organizations: CSIR, NewDelhi, India COSTED, Chennai, India NISSAT, NewDelhi, India DST, NewDelhi, India DRDO, NewDelhi, India   Potential Sponsors/collaborators: Jubilant Biosys, Bangalore Strand Genomics, Bangalore ChemBiotek, Calcutta VlifeSciences, Pune   Collaborators & Contributors   Prof. Alexander Tropsha, (+ team) School of Pharmacy, UNC-Chapel Hill, USA Prof. RC Glen, Prof. Peter Murray Rust, Andreas Bender, Unilever Center for Molecular Informatics, Cambridge University, UK Prof. Christoph Steinbeck, Max Plank Institute of Chemical Ecology, Bioinformatics, Jena, Germany Prof. Jacques R. Chretien, Center of Innovation, Orleans, France Ferenc, ChemAxon, Hungary     [Home]