The objective of our research is to learn about and apply relationships between structure and properties of molecules. We are interested in all sorts of molecules, but have a special interest in pharmaceutically relevant molecules.
Most medicines exert their therapeutic effect by interaction with a protein or nucleic acid. The part of the macromolecule where a particular drug binds is the receptor. A small molecule, such as a drug, that binds to a receptor is called a ligand. The interaction of ligand and receptor involves electrostatic attraction and repulsion, hydrogen bonding, and van der Waals contacts. These forces are the result of electronic distribution, steric shape, and conformational properties. Modern computer-based methods, such as molecular modeling, are well suited for predicting these molecular properties. The strength of the interactions between ligand and receptor will determine recognition and affinity. The greater the strength and the more specific the interactions, the greater the potency of the biologically active molecule and the less likely it will cause side effects.
The economics of drug discovery has led to a prominent role for computer-aided drug design (CADD) methodologies in pharmaceutical companies. The cost of bringing a new pharmaceutical from the laboratory to the patient has risen rapidly. The high cost is related to the low odds of successfully finding safe and effective new medicines: tens of thousands of compounds may be made and tested to find one with the attributes to be a significant therapeutic product. CADD helps determine which structures are more promising as drug candidates. Besides cost, there is another factor driving the increased use of CADD. The causes of disease are being unraveled at the molecular level by genomics. These experiments reveal which biomacromolecules play a role in a disease state. Computational chemistry is uniquely positioned to maximize the usefulness of this molecular-level information.