Cassandra is an open source atomistic Monte Carlo software package that is effective in simulating the thermodynamic properties of fluids and solids. The different features and algorithms used in Cassandra are described, along with implementation details and theoretical underpinnings to various methods used. Benchmark and example calculations are shown, and information on how users can obtain the package and contribute to it are provided. © 2017 Wiley Periodicals, Inc.

Herein, molecular modeling techniques were used with the main goal to obtain candidates from a drug database as potential targets to be used against SARS-CoV-2. This novel coronavirus, responsible by the COVID-19 outbreak since the end of 2019, became a challenge since there is not vaccine for this disease. The first step in this investigation was to solvate the isolated S-protein in water for molecular dynamics (MD) simulation, being observed a transition from "up" to "down" conformation of receptor-binding domain (RBD) of the S-protein with angle of 54.3 and 43.0 degrees, respectively. The RBD region was more exposed to the solvent and to the possible drugs due to its enhanced surface area. From the equilibrated MD structure, virtual screening by docking calculations were performed using a library contained 9091 FDA approved drugs. Among them, 24 best-scored ligands (14 traditional herbal isolate and 10 approved drugs) with the binding energy below -8.1 kcal/mol were selected as potential candidates to inhibit the SARS-CoV-2 S-protein, preventing the human cell infection and their replication. For instance, the ivermectin drug (present in our list of promise candidates) was recently used successful to control viral replication in vitro. MD simulations were performed for the three best ligands@S-protein complexes and the binding energies were calculated using the MM/ PBSA approach. Overall, it is highlighted an important strategy, some key residues, and chemical groups which may be considered on clinical trials for COVID-19 outbreak.

We present an adaptable method to compute the solubility limit of solids by molecular simulation, which avoids the difficulty of reference state calculations. In this way, the method is highly adaptable to molecules of complex topology. Results are shown for solubility calculations of sodium chloride in water and light alcohols at atmospheric conditions. The pseudosupercritical path integration method is used to calculate the free energy of the solid and gives results that are in good agreement with previous studies that reference the Einstein crystal. For the solution phase calculations, the self-adaptive Wang-Landau transition-matrix Monte Carlo method is used within the context of an expanded isothermal-isobaric ensemble. The method shows rapid convergence properties and the uncertainty in the calculated chemical potential was 1% or less for all cases. The present study underpredicts the solubility limit of sodium chloride in water, suggesting a shortcoming of the molecular models. Importantly, the proper trend for the chemical potential in various solvents was captured, suggesting that relative solubilities can be computed by the method.

We present an efficient, automated expanded ensemble method to calculate the residual chemical potential or solvation free energy by molecular dynamics simulation. The methodology is validated by computing the residual chemical potential of 13 amino acid analogs in water at 300 K and 1 bar and comparing to reference simulation data. Overall agreement is good, with the methodology of the present study reaching limiting precisions of less than 0.1 kBT in half of the total simulation time of the reference simulation study which utilized Bennett's acceptance ratio method. The apparent difference in the efficiencies is a result of the inherent advantages of the expanded ensemble method, which creates an improved decorrelation of simulation data and improves the sampling of the important regions of the configurational phase space of each subensemble. The present adaptation utilizes histograms of proposed transition energies collected throughout the entire simulation, to make extremely precise calculations of the relative free energy between neighboring subensembles.

We present results from a computational study investigating the use of Gibbs ensemble and grand-canonical transition-matrix Monte Carlo (GC-TMMC) methods to determine the liquid-vapor phase coexistence properties of pure molecular fluids of varying degrees of complexity. The molecules used in this study were ethane, n-octane, cyclohexane, 2,5-dimethylhexane, 1-propanol, and water. We first show that the GC-TMMC method can reproduce Gibbs ensemble results found in the literature. Given the excellent agreement for each molecule, we then compare directly the performance of Gibbs ensemble and GC-TMMC simulations at both low and high reduced temperatures by monitoring the relative uncertainties in the saturation properties as a function of computational time. In general, we found that the GC-TMMC method yielded limiting uncertainties in the saturated vapor density and pressure that were significantly smaller, by an order of magnitude in some instances, than those of the Gibbs ensemble method. Limiting Gibbs ensemble uncertainties for these properties were generally in the 0.8-5.0% range. However, both methods yielded comparable limiting uncertainties in the saturated liquid density, which fell within the range of 0.1-1.0%. In the case of water at 300 K, we found that the Gibbs ensemble outperformed GC-TMMC. The relatively poor performance of the GC-TMMC method in this situation was tied to the slow convergence of the density probability distribution at this low temperature. We also discuss strategies for improving the convergence rate under these conditions.