Computer-Aided Drug Design: Methods and Applications

Although pharmaceuticals have saved or improved countless lives, drug discovery remains an inexact science that involves much trial and error. Most pharmaceuticals are small organic molecules that work via noncovalent interactions with biological macromolecules. The main focus of my research group is the development of computer modeling tools to quickly characterize these types of interactions. Most of our tools are based on implicit ligand theory, a theoretical framework that I derived to predict how tightly molecules bind and how they influence the population of conformations accessed by their targets. At this point, we have established that our methods are able to reproduce results of more computationally expensive approaches. We are working on making them more efficient and feasible to use with large libraries of chemical compounds. We have also advanced the theory of end-point binding free energy methods, in which binding affinity is predicted based on molecular simulations of the bound complex.

I will also highlight our major contributions in several related areas. These include (1) enhanced sampling algorithms, which facilitate the modeling of slow conformations in molecular simulations; (2) molecular modeling of biomedical problems, most successfully in predicting the binding site and characterizing the bacterial ion pump NQR; and (3) Bayesian analysis of biophysical measurements, which enables a more accurate quantification of uncertainty.