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In Silico Design in Homogeneous Catalysis Using Descriptor Modelling
Van ’t Hoff Institute for Molecular Sciences, University of Amsterdam, Nieuwe Achtergracht 166, 1018 WV Amsterdam, The Netherlands.
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Received: 31 July 2006; in revised form: 5 September 2006 / Accepted: 20 September 2006 / Published: 28 September 2006
Abstract: This review summarises the state-of-the-art methodologies used for designing homogeneous catalysts and optimising reaction conditions (e.g. choosing the right solvent). We focus on computational techniques that can complement the current advances in high-throughput experimentation, covering the literature in the period 1996-2006. The review assesses the use of molecular modelling tools, from descriptor models based on semiempirical and molecular mechanics calculations, to 2D topological descriptors and graph theory methods. Different techniques are compared based on their computational and time cost, output level, problem relevance and viability. We also review the application of various data mining tools, including artificial neural networks, linear regression, and classification trees. The future of homogeneous catalysis discovery and optimisation is discussed in the light of these developments.
Keywords: Catalyst Design; Combinatorial Catalysis; QSAR; Artificial Neural Networks; Partial Least Squares Analysis; Data Analysis.
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MDPI and ACS Style
Burello, E.; Rothenberg, G. In Silico Design in Homogeneous Catalysis Using Descriptor Modelling. Int. J. Mol. Sci. 2006, 7, 375-404.
Burello E, Rothenberg G. In Silico Design in Homogeneous Catalysis Using Descriptor Modelling. International Journal of Molecular Sciences. 2006; 7(9):375-404.
Burello, Enrico; Rothenberg, Gadi. 2006. "In Silico Design in Homogeneous Catalysis Using Descriptor Modelling." Int. J. Mol. Sci. 7, no. 9: 375-404.