The Trotter factorization of the Liouville propagator is used to generate new reversible molecular dynamics integrators. This strategy is applied to derive reversible reference system propagator algorithms (RESPA) that greatly accelerate simulations of systems with a separation of time scales or with long range forces. The new algorithms have all of the advantages of previous RESPA integrators but are reversible, and more stable than those methods. These methods are applied to a set of paradigmatic systems and are shown to be superior to earlier methods. It is shown how the new RESPA methods are related to predictorcorrector integrators. Finally, we show how these methods can be used to accelerate the integration of the equations of motion of systems with Nose thermostats.

Designing tight binding ligands is a primary objective of small molecule drug discovery.Over the past few decades, free energy calculations have benefited from improved force fields and sampling algorithms, as well as the advent of low cost parallel computing.However, it has proven to be challenging to reliably achieve the level of accuracy that would be needed to guide lead optimization (~5X in binding affinity) for a wide range of ligands and protein targets. Not surprisingly, widespread commercial application of free energy simulations has been limited due to the lack of large-scale validation coupled with the technical challenges traditionally associated with running these types of calculations.Here, we report an approach that achieves an unprecedented level of accuracy across a broad range of target classes and ligands, with retrospective results encompassing 200 ligands and a wide variety of chemical perturbations, many of which involve significant changes in ligand chemical structures. In addition, we have applied the method in prospective drug discovery projects and found a significant improvement in the quality of the compounds synthesized that have been predicted to be potent. Compounds predicted to be potent by this approach have a substantial reduction in false positives relative to compounds synthesized based on other computational or medicinal chemistry approaches. Furthermore, the results are consistent with those obtained from our retrospective studies, demonstrating the robustness and broad range of applicability of this approach, which can be used to drive decisions in lead optimization.3

A new molecular dynamics model in which the point charges on atomic sites are allowed to fluctuate in response to the environment is developed and applied to water. The idea for treating charges as variables is based on the concept of electronegativity equalization according to which: (a) The electronegativity of an atomic site is dependent on the atom's type and charge and is perturbed by the electrostatic potential it experiences from its neighbors and (b) Charge is transferred between atomic sites in such a way that electronegativities are equalized. The charges are treated as dynamical variables using an extended Lagrangian method in which the charges are given a fictitious mass, velocities and kinetic energy and then propagated according to Newtonian mechanics along with the atomic degrees of freedom. Models for water with fluctuating charges are developed using the geometries of two common fixed-charge water potentials: the simple point charge (SPC) and the 4-point transferable intermolecular potential (TIP4P). Both fluctuating charge models give accurate predictions for gasphase and liquid state properties, including radial distribution functions, the dielectric constant, and the diffusion constant. The method does not introduce any new intermolecular interactions beyond those already present in the fixed charge models and increases the computer time by only a factor of 1.1, making this method tractable for large systems.

We provide an overview of the IMPACT molecular mechanics program with an emphasis on recent developments and a description of its current functionality. With respect to core molecular mechanics technologies we include a status report for the fixed charge and polarizable force fields that can be used with the program and illustrate how the force fields, when used together with new atom typing and parameter assignment modules, have greatly expanded the coverage of organic compounds and medicinally relevant ligands. As we discuss in this review, explicit solvent simulations have been used to guide our design of implicit solvent models based on the generalized Born framework and a novel nonpolar estimator that have recently been incorporated into the program. With IMPACT it is possible to use several different advanced conformational sampling algorithms based on combining features of molecular dynamics and Monte Carlo simulations. The program includes two specialized molecular mechanics modules: Glide, a high-throughput docking program, and QSite, a mixed quantum mechanics/molecular mechanics module. These modules employ the IMPACT infrastructure as a starting point for the construction of the protein model and assignment of molecular mechanics parameters, but have then been developed to meet specialized objectives with respect to sampling and the energy function.

A small change in the Hamiltonian scaling in replica exchange with solute Tempering (REST) is found to improve its sampling efficiency greatly especially for the sampling of aqueous protein solutions in which there are large scale solute conformation changes. Like the original REST (REST1), the new version (which we call REST2) also bypasses the poor scaling with system size of the standard temperature replica exchange method (TREM), reducing the number of replicas (parallel processes) from what must be used in TREM. This reduction is accomplished by deforming the Hamiltonian function for each replica in such a way that the acceptance probability for the exchange of replica configurations does not depend on the number of explicit water molecules in the system. For proof of concept, REST2 is compared with TREM and with REST1 for the folding of the trpcage and β-hairpin in water. The comparisons confirm that REST2 greatly reduces the number of CPUs required by regular replica exchange and greatly increases the sampling efficiency over REST1. This method reduces the CPU time required for calculating thermodynamic averages and for the ab initio folding of proteins in explicit water.