Statistical mechanics with quantum potentials: Application to protein-ligand binding affinities

During the drug development process, lead optimization requires intensive chemical synthesis and testing efforts. The process can be highly iterative in nature with multiple rounds of synthesis required, because changes made to improve, for example, pharmacokinetic factors such as solubility can also decrease potency, requiring further changes to recover potency, and so on. Consistently accurate computational predictions of protein-ligand binding affinities would significantly reduce this expensive and time consuming burden, by providing medicinal chemists the ability to more aggressively prioritize ligands for synthesis and testing based on computational results. However, currently, achievement of consistent accuracy in protein-ligand binding affinity prediction is an unmet goal in the field of computational chemistry. Conventional docking and scoring methods have been shown to provide enrichment of active vs. inactive ligands in chemical libraries, but still are very limited in their ability to rank candidate ligands by their binding affinities. Even advances like free energy perturbation (FEP) and VeraChemandapos;s own mining minima free energy method VM2, remain limited in their ability to consistently provide the accuracy levels needed. Importantly, all of these methods have in common a dependency on classical molecular mechanics (MM) force fields, and even the best force fields for proteins and drug-like molecules are not guaranteed to have optimal parameters nor to provide adequate descriptions of chemical interactions involving, for example, π-stacking, polarization, charge transfer, or metal cations. In fact, the approximations inherent in typical force fields are thought to be a key factor limiting accuracy. In this fast- track SBIR proposal, we aim to address this key limitation by integrating VeraChemandapos;s free energy method VM2 with quantum mechanical (QM) potentials, producing a new software package for QM based protein-ligand free energy calculations called PLQM-VM2. This package will be distinct from other free energy methods, such as FEP, which is not readily implemented with QM potentials. Similarly, although QM has been applied to protein-ligand systems, existing methods are limited to focusing on a single conformation, whereas PLQM- VM2 will integrate existing force field-based conformational searching with QM energy and free energy refinement. Phase I will provide a first level of QM protein-ligand free energy capability, integrating VM2 with a fast semi-empirical QM treatment of the ligand and protein active site. In Phase II, a capability to allow fast and accurate inclusion of protein atoms beyond the active site will be added through a SEQM/polarizable force field method, and a very efficient QM fragmentation scheme will enable energy corrections at higher-level QM. Parallelization on CPUs and GPUs will provide fast enough turnaround to support industry Randamp;D, and submission of calculations to both local computer clusters and cloud resources will be supported. The package will be tested and best practices defined through application to multiple protein targets each with high quality measured affinities for a large series of non-covalent inhibitors.Finding new drug molecules to treat disease is very difficult and if scientists were able to use computer software programs to reliably predict how strongly drug-candidate molecules bind with a particular protein, this would speed up, and make less expensive, the drug discovery process, as they would have to make and test a lot fewer molecules in the laboratory. Currently available software packages are of some help, but even the best ones do not provide the reliable accuracy scientists really need, largely because they model the interactions between molecules using classical physics, whereas these interactions require quantum mechanics to describe them properly. This project aims to introduce quantum mechanics based descriptions of molecular interactions into a current state of the art software package for predictions of binding strengths, thereby improving its reliability.