Use of drug discovery tools in rational organometallic catalyst design

A computational procedure is detailed where techniques common in the drug discovery process-2D- and 3D-quantitative structure-activity relationships (QSAR)-are applied to rationalize the catalytic activity of a synthetically flexible, Ti-N=P ethylene polymerization catalyst system. Once models relating molecular properties to catalyst activity are built with the two QSAR approaches, two database mining approaches are used to select a small number of ligands from a larger database that are likely to produce catalysts with high activity when grafted onto the Ti-N=P framework. The software employed throughout this work is freely available, is easy to use, and was applied in a "black box" approach to highlight areas where the drug discovery tools, designed to address organic molecules, have difficulty in addressing issues arising from the presence of a metal atom. In general, 3D-QSAR offers an efficient way to screen new potential ligands and separate those likely to lead to poor catalysts from those that are likely to contribute to highly active catalysts. The results for 2D-QSAR appear to be quantitatively unreliable, likely due to the presence of a metal atom; nonetheless, there is evidence that qualitative predictions from different models may be reliable. Pitfalls in the database mining techniques are identified, none of which are insurmountable. The lessons learned about the potential uses and drawbacks of the techniques described herein are readily applicable to other catalyst frameworks, thereby enabling a rational approach to catalyst improvement and design.