In Silico Design in Homogeneous Catalysis Using Descriptor Modelling

• Main Authors: Gadi Rothenberg, Enrico Burello
• Format: Article
• Language: English
• Published: MDPI AG 2006-09-01
• Series: International Journal of Molecular Sciences
• Subjects: Catalyst Design; Combinatorial Catalysis; QSAR; Artificial Neural Networks; Partial Least Squares Analysis; Data Analysis.
• Online Access: http://www.mdpi.com/1422-0067/7/9/375/

This review summarises the state-of-the-art methodologies used for designinghomogeneous 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 reviewassesses the use of molecular modelling tools, from descriptor models based onsemiempirical and molecular mechanics calculations, to 2D topological descriptors andgraph theory methods. Different techniques are compared based on their computational andtime cost, output level, problem relevance and viability. We also review the application ofvarious data mining tools, including artificial neural networks, linear regression, andclassification trees. The future of homogeneous catalysis discovery and optimisation isdiscussed in the light of these developments.