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Chemoinformatics Platform Boasts 900,000 Samples Available for Experimenting

By IGI Global Scientific Publishing on Aug 20, 2010
BioFocus, a company specializing in drug discovery technologies and services, has recently announced an expansion of their available platforms used for genomic study. High-throughput screening allows researchers to utilize robotics, advanced software, and detection devices to conduct rapid tests in drug discovery. BioFocus concentrates on target validation, in order for appropriate molecules to be the focal point in screening processes.

Dr. Doris Hafenbradl, the senior director of biology and natural products at BioFocus explains the potential of target validation and the use of intelligent technologies by chemists and engineers, "We use surface plasmon resonance extensively in our screening platforms This is especially relevant for screening of our diverse fragment library in which label-free technologies are especially convenient. We are also well-positioned for designing drugs using our chemo-informatics library of 900,000 compounds. Using high-throughput screening, we have identified a large range of novel targets." ( www.genengnews.com/gen-articles/multiplexing-advances-redefine-hts/3302/?page=2)

The Society for Biomolecular Sciences recently explored various systems such as the one created by BioFocus at their annual meeting. Similar technologies and applications are also discussed in one of the latest releases by IGI Global, publisher of information science and technology research: Chemoinformatics and Advanced Machine Learning Perspectives: Complex Computational Methods and Collaborative Techniques.

Editors Dr. Huma Lodhi, of Imperial College, UK and Dr. Yoshihiro Yamanishi of Kyoto University, Japan, elaborate on the importance of this research, "Recent advances in high throughput technologies and generation of large amount of data have generated huge interest to design, analyze and apply novel computational and more specifically learning methodologies to solving chemical problems. The advances in machine learning for chemoinformatics establish a need for a comprehensive text on the subject."

Their reference fills that void by presenting cutting-edge tools and strategies such as Bayesian statistics, nonlinear partial least squares, graph kernels, and support vector machines, applied to problems in chemoinformatics. To learn more, please visit www.igi-global.com/Bookstore/TitleDetails.aspx?TitleId=37360.		Chemoinformatics and Advanced Machine Learning Perspectives: Complex Computational Methods and Collaborative Techniques

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