GPU‐specific algorithms for improved solute sampling in grand canonical Monte Carlo simulations

Abstract

The Grand Canonical Monte Carlo (GCMC) ensemble defined by the excess chemical potential, μ ex , volume, and temperature, in the context of molecular simulations allows for variations in the number of particles in the system. In practice, GCMC simulations have been widely applied for the sampling of rare gasses and water, but limited in the context of larger molecules. To overcome this limitation, the oscillating μ ex GCMC method was introduced and shown to be of utility for sampling small solutes, such as formamide, propane, and benzene, as well as for ionic species such as monocations, acetate, and methylammonium. However, the acceptance of GCMC insertions is low, and the method is computationally demanding. In the present study, we improved the sampling efficiency of the GCMC method using known cavity-bias and configurational-bias algorithms in the context of GPU architecture. Specifically, for GCMC simulations of aqueous solution systems, the configurational-bias algorithm was extended by applying system partitioning in conjunction with a random interval extraction algorithm, thereby improving the efficiency in a highly parallel computing environment. The

Integrated Covalent Drug Design Workflow Using Site Identification by Ligand Competitive Saturation

“…When applying the workflow, the most time-consuming part is running the SILCS simulations. This typically requires 1–3 days for most proteins using 10 GPUs . Once the FragMaps, Cys probability map, and exclusion map are available, they can be used repeatedly, allowing for rapid calculation of all the SILCS metrics described above for the warhead fragments as well as putative covalent ligands.…”

“…This typically requires 1−3 days for most proteins using 10 GPUs. 76 Once the FragMaps, Cys probability map, and exclusion map are available, they can be used repeatedly, allowing for rapid calculation of all the SILCS metrics described above for the warhead fragments as well as putative covalent ligands. When lead compounds are available, optimization of the noncovalent scaffold may be undertaken using the same FragMaps.…”

Efficient Sampling of Cavity Hydration in Proteins with Nonequilibrium Grand Canonical Monte Carlo and Polarizable Force Fields

“…Since water exchange with the virtual reservoir takes place through Monte Carlo insertion/deletion moves, the kinetic barrier associated with the physical water exchange between protein cavity and bulk solvent is avoided, making GCMC potentially much more efficient than MD-based methods. 42,43 With additional algorithms for improving acceptance probabilities 44−49 and implementation on modern computational hardware such as GPUs, 48,50,51 GCMC simulations have become widely used in the biomolecular simulation community in recent years. 25,52−54 Sampling of the hydration level may still be locally trapped due to the generally rough energy landscape of condensed phase systems.…”

“…An alternative computational approach for predicting the internal hydration level is grand canonical Monte Carlo (GCMC), − which allows the exchange of water between the region of interest and a virtual reservoir under the constant chemical potential, volume and temperature condition (i.e., the μVT ensemble). Since water exchange with the virtual reservoir takes place through Monte Carlo insertion/deletion moves, the kinetic barrier associated with the physical water exchange between protein cavity and bulk solvent is avoided, making GCMC potentially much more efficient than MD-based methods. , With additional algorithms for improving acceptance probabilities − and implementation on modern computational hardware such as GPUs, ,, GCMC simulations have become widely used in the biomolecular simulation community in recent years. ,− Sampling of the hydration level may still be locally trapped due to the generally rough energy landscape of condensed phase systems. Therefore, it is worthwhile to consider additional strategies that further enhance the overall efficiency for exploring different hydration levels.…”

Enhancing SILCS-MC via GPU Acceleration and Ligand Conformational Optimization with Genetic and Parallel Tempering Algorithms

“…Beyond the implementation and testing of additional algorithms in the present study the capabilities and efficiency of SILCS-MC has been improved through GPU acceleration. This development has increased the computational speed to surpass previous CPU-based implementations of SILCS-MC by 2 orders of magnitude. , Consequently, GPU-optimized SILCS-MC facilitates the processing of larger molecules and the increasingly large virtual libraries without compromising on the precision necessary for effective CADD efforts. ,,,,− ,− …”