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 VeraChem'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 VeraChem'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 R&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.
