R43GM150314

Project Summary The value of computational chemistry to commercial drug discovery is now well-established. Virtual screening (including molecular docking) now jumpstarts most discovery efforts. More recently, a combination of GPU and cloud based computing has vastly increased the realistic computational throughput available for drug discovery. In turn, this has ignited substantial interest in relative free energy calculations (e.g. Free Energy Perturbation, FEP) for drug lead enhancement. FEP has been applied at the fringes of drug discovery for decades, but massive parallelism in the more recent past has moved FEP to center stage, and FEP has helped shave months or years off discovery efforts where these calculations are reliable. The catch is that FEP calculations are not always reliable. While for some systems, the error in a FEP result is much less than one kcal/mol--and they have successfully steered slow/expensive bench efforts--there are other systems where the predictions are not very useful. Even where retrospective analysis is possible, it is often not very clear why FEP calculations are so good for some target systems, and so bad for others. Broadly, the limitations of FEP can be distilled down to three problems: A poor description of the energetics (force field); insufficient sampling; or a misunderstanding of the fundamental science (e.g., incorrect protein model, wrong binding site, wrong protonation state, etc.). It is generally believed that many issues arise from the first of these—and improving the evaluation of energetics using quantum mechanics (QM) will be the focus here. There is a huge interest in methods that can help obviate the existing problems with FEP. Herein, we propose a new platform for FEP, which incorporates a quantum mechanical description of the molecular interaction of central interest. The traditional force field used with FEP is a simplified analytic expression with fit coefficients termed Molecular Mechanics (MM). MM is a simple approximation of the true molecular interactions that can be described using quantum mechanics. But QM has been, until recently, far too expensive to use in the context of the molecular dynamics (MD) sampling required for FEP. At long last, we have determined how to integrate QM into the FEP paradigm, using a carefully programmed distributed processing platform that lends itself to use on commodity cloud computers, and by integrating a semiempirical QM implementation that provides predictions that are much better than those from MM, but at a cost far less than for a full DFT QM prediction. Our implementation allows FEP calculations with a realistic QM core region of hundreds of atoms to be carried out with the scale of sampling associated with accurate FEP calculations and with turnaround commensurate with modern drug discovery. Here, we propose to validate this platform against traditional MM-based FEP, to show it addresses many of the issues of that approach. We will also identify additional implementation ideas to further improve effective throughput and/or accuracy.