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Welcome to Molecular Biosilico Discovery MultiSystem (MoBiosD) Hub!!!!

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This website was created as a platform to publish advanced Chem-Bio-Med-Informatics tools. Nowadays, Chem-Bio-Med-Informatics has become an independent discipline by itself. MoBiosD-hub provides innovative software (and other resources) to the chemical, medical, biological, biotechnological and pharmaceutical industry/academia for molecular modeling, drug (molecular) design and Chem-Bio-Med-based research as well as delivering informatics infrastructure for life science investigations.
The MoBiosD-hub initiative launched by the Unit of Computer-Aided Molecular “Biosilico” Discovery and Bioinformatic Research (CAMD-BIR Unit) dates back in 2006 and has had as a fundamental mission to provide novel tools, methods, theories and databases for the academy/industry. The CAMD-BIR Unit founded by Prof.

has over the years evolved into a multidisciplinary and international research consortium, comprised of experts from several research departments and/or centers worldwide working closely together in the development, testing and validation of novel chem-bio-med-informatic methods in real application scenarios. In this sense, for greater inclusiveness for partners worldwide the denomination CAMD-BIR International has been adapted.
Advancements in the MoBiosD network need joint efforts of scientists from different academic backgrounds. This fact is evocative of the phrase stated by Freeman Dyson that, “some scientists are birds, others are frogs” (Notices Amer. Math. Soc., 2009, 56 (2), 212-223). “Birds fly high in the air and survey broad vistas of mathematics out to the far horizon”. Birds are delighted “in concepts that unify our thinking and bring together diverse problems from different parts of the landscape”. Otherwise, “Frogs live in the mud below and see only the flowers that grow nearby”. Frogs are delighted “in the details of particular objects, and they solve problems one at a time (…).” Finally, he said “Mathematics needs both birds and frogs. Mathematics is rich and beautiful because birds give it broad visions and frogs give it intricate details”. Similar behavior typifies Chem-Bio-Med-Informatics research, as it continuously requires a concerted effort through collaborations with many research groups/labs with excellent communication and adequate interpersonal skills in order to build Chem-Bio-Med-Informatics infrastructure in the world of molecular science and to keep up with current advances in this integral and multidisciplinary field.

Main Research Topics

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  • Novel molecular descriptors (indices) for chem-bio-med-informatics investigations.
  • 2D to 3D conversion, pattern recognition analysis and clustering of large compound libraries.
  • Molecular visualization & 2/3D database searching.
  • Substructure search systems
  • Selection/identification and de novo design of new lead compounds.
  • Rational (computer-aided) drug/vaccines and material design.
  • Computational (virtual and in silico) screening and drug repositioning.
  • Integrating wet-lab and in silico discovery techniques.
  • e-science, data integration and high-performance computing
  • Structure and ligand-based screening.
  • Physchem prediction methods
  • Early pharmacokinetics (ADME) and toxicity prediction.
  • Chemometric and QSAR/QSPR studies.
  • Characterization of molecular similarity.
  • Generation of pharmacophore models (pharmacophore modeling) for the design of new bio-active compounds.
  • Lead optimization and scaffold hopping.

About Products & Key Goals

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The MoBiosD-hub’s portfolio of molecular modeling applications is presented as a workflow involving ligand- and structure-based design strategies. The toolkits employed for these tasks are programming libraries used to create customized applications with object-oriented accessibility to a given set of capabilities. Tools on this website are and will remain free for all interested users. Our aim is to develop information technologies to analyze important bio-molecules in life science (e.g., genes, proteins, compounds, drugs) in order to understand their functions in the biological system. That is, we develop novel statistical/machine learning methods for the prediction of the pharmacokinetics (ADME), pharmacological, biochemical, PhysChem, medical and toxicity endpoints or/and to predict various molecular interaction networks (protein (or compound)-protein interactions, metabolic pathways, etc.) from omics data in genome, transcriptome, proteome, metabolome, and phenome (microarrays). A general trend through our research is to permit scientists to gain access to data, methods, and new technology in Chem-Bio-Med-Informatics and e-Science.

What does the Hub Do?

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MoBiosD is a webpage-based Hub, which serves as a central framework to connect many research topics and permit to dock the main results of researchers affiliated to the CAMD-BIR International network. This virtual pocket handles, amplifies and transmits several research frames. This hub guarantees access to suites of integrative Chem-Bio-Med-Informatics applications (open source software and open access, all resources developed by our teams are free!!!) and covers different areas using modern molecular (drug/material/vaccine/biomarker, etc.) design methods (to support this wet-dry cycle).
CAMD-BIR International is devoted to the development of novel computational methods for handling chemical, biological and medical information, database mining for hit and lead compounds, and the generation of pharmacophore models for the design of new bio-active compounds. Some in-house software systems to address the needs of today’s research disciplines including QSAR/QSPR studies, protein modeling, structure and ligand-based design, high throughput discovery, molecular modeling and simulations, Virtual Molecular Screening (mining HTS and eHTS data for compound selection).
Currently, one of core strengths of CAMD-BIR International is in the definition of new molecular and bio-macro-molecular descriptors and the biosilico discovery of novel chemical entities (NCE) as well as early pharmacokinetics (ADME), PhysChem and toxicity prediction. In addition, it is leveraging its know-how and proprietary technology to expand its research activities in the area of docking and scoring functions, protein-protein interactions, synthesis driven combinatorial library design and prediction of the viability of synthetic routes for chemical compounds. We also aim to use in silico technology (namely, approaches based on machine learning and specialized pattern recognition techniques) for medical and pharmaceutical applications such as prediction of drug targets and drug side effects in the chemogenomics and pharmacogenomics framework and prediction of new drug indications for “drug repositioning”.