Prior Funding

The annual number of new drug approvals is on the decline while the development costs for new drugs are increasing exponentially. As a solution to this problem, we propose to use the exponential growth in computing power to speed up the development of novel drugs and therefore lower their cost.

Drugs that act on a specific molecular target are often more potent and have fewer side effects than their non-specific counterparts. For example, imatinib (also called Gleevec) specifically inhibits a malfunctioning signaling protein. Imatinib is clinically extremely successful and has reduced the number of deaths from a specific cancer type by 80%. To develop therapies like imatinib, computational methods often treat molecular drug targets as inflexible “locks”, define a desired drug binding site as a “keyhole” and simulate docking of drug candidates (“keys”). However, in reality drug targets are very flexible. To repeat the success of imatinib we need to treat drug targets as flexible molecules in computational methods. Here, we are simulating and verifying the entire process of a drug binding to the fully flexible drug target.