Assistant Professor David Minh received a grant award for $337,373 from the National Institutes of Health for his project entitled “Sound-stage Virtual Screening Based on Implicit Ligand Theory.” With this grant, he plans to develop new computational tools that will make faster and more accurate predictions about interactions between proteins and small molecules. The tools will be useful for drug discovery and understanding how chemicals influence human biology.

Virtual screening of chemical libraries is a common starting point for discovering ligands, such as drug leads. The primary tool for virtual screening is molecular docking, which is fast but makes a number of serious approximations about how small molecules interact with proteins. Methods based on rigorous statistical physics are much slower and more accurate. Minh has derived a new statistical physics theory for binding that is based on multiple rigid structures of a receptor: implicit ligand theory. Computational methods based on this theory have the potential to carefully compromise between the speed of docking and the accuracy of other rigorous methods.

In this project, Minh and his team plan to develop and assess computational tools to predict whether a molecule will bind to a protein or not, how tightly it binds, and where it binds. Specifically, they will first test whether binding to a single rigid structure is sufficient to distinguish active from inactive molecules. They will then assess the ability of different molecular simulation methods to generate receptor structures relevant to ligand binding and lead to accurate binding affinity predictions. Last, they will develop algorithms to rank binding poses based on their theory. Minh anticipates that their methods will be used in the second stage of a virtual screen; after docking millions of compounds using a more inaccurate method, their method can then be used to rank several thousand of the best molecules from the previous method. From there, a few dozen can be tested in the lab.