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Int. J. Mol. Sci. 2006, 7(9), 375-404; doi:10.3390/i7090375
Review

In Silico Design in Homogeneous Catalysis Using Descriptor Modelling

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Received: 31 July 2006 / Revised: 5 September 2006 / Accepted: 20 September 2006 / Published: 28 September 2006
(This article belongs to the Special Issue Virtual Combinatorial Synthesis and Drug Design)
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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. Catalyst Design; Combinatorial Catalysis; QSAR; Artificial Neural Networks; Partial Least Squares Analysis; Data Analysis.
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Burello, E.; Rothenberg, G. In Silico Design in Homogeneous Catalysis Using Descriptor Modelling. Int. J. Mol. Sci. 2006, 7, 375-404.

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Int. J. Mol. Sci. EISSN 1422-0067 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert