September 23, Tue 2010
1:00 pm, MRB 200 Conference Room
Dr. Kyle V. Camdera
Department of Chemical and Petroleum Engineering, University of Kansas
Applications of Computational Molecular Design
Computational molecular design (CMD) is a methodology which applies optimization techniques to develop novel lead compounds for a variety of applications. The product design framework developed in this work seeks to accelerate the commonly used experimental trial-and-error approach by searching through large molecular spaces to provide a set of chemical structures likely to match a set of desired property targets. In this presentation, an overview of CMD methods used in our group is presented. Two major challenges are defined: the prediction of physical, chemical and biological properties of various molecular systems, and the determination of chemical structures matching a set of property targets within a large molecular space. To predict the physical and chemical properties of a specific class of molecules, quantitative structure-property relations (QSPRs) are developed which predict values of such properties as solubility, diffusivity, toxicity, polymer glass transition temperature, critical properties, and melting and decomposition temperatures. The electronic structure of a molecule is quantified using modified connectivity indices, which describe bonding environments, charge distribution, orbital hybridization and steric interactions. The resulting property prediction model is then integrated within a computational molecular design framework, which combines the QSPRs with structural feasibility constraints in a combinatorial optimization problem.
Two example systems are described in this presentation. The first is ionic liquids (ILs), which are organic salts with a liquidous region over a wide temperature range. These substances are being considered for use as environmentally-benign solvents, but the molecularly-tunable nature of ILs yields an extraordinary number of possible cation and anion combinations, the majority of which have never been synthesized. Quantitative structure-property relations (QSPRs) have been developed which correlate experimental values of solubility, diffusivity, and viscosity with connectivity indices. This property prediction model is combined with structural feasibility constraints in a combinatorial optimization problem, which is solved using Tabu Search to give near-optimal solutions, which represent candidate ionic liquid structures. Examples are given for the design of ionic liquids for use as solvents in absorption refrigeration systems, and as reaction media.
The second product design problem discussed is the search for novel excipients for stabilizing protein drugs. Many promising new drugs are proteins, which are highly effective if they can be delivered in a properly folded state. However, these compounds suffer from a tendency to degrade, either via chemical reactions or aggregation. Excipients are small molecules used as additives for pharmaceuticals, which are used to improve properties such as shelf life. This project seeks to understand the mechanism of protein aggregation for model protein drugs in the solid-state, such that computational molecular design may be applied to develop novel excipient formulations which are both effective and practically useful.