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| Alan Gibbs, Johnson & Johnson Pharmaceutical R & D |
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| Alan Gibbs earned a B.Sc. in Biochemistry (1996) at the University of Calgary. He went on to pursue graduate studies at the University of Alberta and completed his Ph.D. in Medicinal Chemistry (2001) under the supervision of Professor David Wishart. His doctoral thesis focused on the characterization of anti-bacterial peptide structure and function via the use of multi-dimensional NMR and molecular dynamics simulations. After completion of his thesis, he joined 3-Dimensional Pharmaceuticals Inc., based in Exton PA, as a computational chemist (2001). At 3-Dimensional Pharmaceuticals, Alan enjoyed many areas of computational drug design, in particular: Protein/ligand docking, molecular dynamics simulations, QSAR modeling, and quantum methods. In 2003, 3-Dimensional Pharmaceuticals was acquired by Johnson & Johnson Pharmaceutical Research & Development L.L.C. and Alan joined the Molecular Design and Informatics group. Since then, he has continued doing structure based drug design as well as combinatorial library design and diversity analysis.
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A New Clustering Algorithm for General Metric Spaces
Alan Gibbs, Johnson & Johnson Pharmaceutical Research & Development, L.L.C., Molecular Design and Informatics, 8 Clarke Drive, Cranbury, NJ 08512, USA
Clustering has been one of the most widely used techniques in chemical informatics. Unfortunately, most clustering algorithms have poor scaling characteristics and can only be applied to data sets of small or moderate size. In addition, many of them require vectorial representations, and cannot be used with popular molecular similarity metrics, such as Tanimoto coefficients based on binary fingerprints. In this presentation, we present a new algorithm for clustering large datasets that works with any similarity function and descriptor representation. We discuss the performance of the algorithm using examples from library enhancement and screening deck design, and demonstrate its use in Third Dimension Explorer and ABCD, the new discovery informatics platform of J&JPRD.
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