Crystal structure prediction (CSP) calculations can reduce risk and improve efficiency during drug development. Traditionally, CSP calculations use lattice energies computed through density functional theory. While this approach is often successful in predicting the low energy structures, it neglects the crucial role of thermal effects on polymorph stabilities. In the present study, we develop a robust and efficient protocol for predicting the relative stability of polymorphs at different temperatures. The protocol is executed on a highly parallel cloud computing infrastructure to produce results at time scales useful for drug development timelines. We demonstrate this protocol on molecule XXIII from the sixth crystal structure prediction blind test. Our results predict that Form D is the most stable experimentally observed polymorph at ambient temperature and Form C is the most stable at low temperature consistent with experiments also conducted in the present study.

We have used molecular dynamics simulations to examine the surface adsorption of a model anti-agglomerant inhibitor (quaternary ammonium salt) binding to a hydrate surface in both aqueous and liquid hydrocarbon phases. From our molecular dynamics simulation data, we were able to identify the preferred binding sites on the (111) crystal face of a methane−propane sII hydrate as well as characterize the equilibrium binding configurations of the inhibitor and their associated binding energies. In the aqueous phase, we observed that the inhibitor proceeds through a two-step surface adsorption mechanism, whereas in the liquid hydrocarbon phase surface adsorption occurs through a single-step mechanism. To characterize the extent of surface adsorption in each liquid phase, we calculated the standard binding free energy using the free energy perturbation method following a double decoupling thermodynamic cycle. We found that the surface adsorption in the liquid hydrocarbon phase is an exergonic process, whereas the surface adsorption in the aqueous phase is an endergonic process. Our results demonstrate that the extent of surface adsorption is much larger in the liquid hydrocarbon phase relative to the aqueous phase and suggest that the inhibitor is less effective in the aqueous phase because the surface adsorption is less favorable. Finally, we examine the effect of the inhibitor on the water structure in the liquid phase and in the hydrate phase, with the results highlighting the difference between the nature of anti-agglomerant/hydrate interactions as compared to kinetic inhibitor/hydrate interactions.

The ability of coronaviruses to infect humans is invariably associated with their binding strengths to human receptor proteins. Both SARS-CoV-2, initially named 2019-nCoV, and SARS-CoV were reported to utilize angiotensin-converting enzyme 2 (ACE2) as an entry receptor in human cells. To better understand the interplay between SARS-CoV-2 and ACE2, we performed computational alanine scanning mutagenesis on the “hotspot” residues at protein–protein interfaces using relative free energy calculations. Our data suggest that the mutations in SARS-CoV-2 lead to a greater binding affinity relative to SARS-CoV. In addition, our free energy calculations provide insight into the infectious ability of viruses on a physical basis and also provide useful information for the design of antiviral drugs.

We used molecular dynamics simulations to examine the surface adsorption of a model antiagglomerant (AA) molecule binding to an sII methane–propane hydrate in environments of different salinities. From our simulation data, we identified the preferred binding sites on the hydrate surface and characterized the equilibrium binding configurations. In addition, for a subset of these binding configurations, we calculated the standard binding free energy in different concentrations of brine using potential of mean force free-energy calculations. We demonstrate that in higher salinity environments, the surface adsorption of the AAs is enhanced through two distinct mechanisms. First, the salt decreases the solubility of the AA in the solution, which increases the thermodynamic driving force for surface adsorption. Second, the salt ions create a negatively charged interfacial layer close to the hydrate surface that effectively solvates the cationic head of the AA molecule. Quantitatively, we found that the presence of 3.5 and 10 wt % NaCl decreases the standard binding free energy of the long hydrocarbon tail binding configuration by 0.8 and 1.4 kcal/mol, decreases the standard binding free energy of the cationic head binding by 1.5 and 3.3 kcal/mol, and decreases the standard binding free energy of simultaneous head and tail binding by 1.9 and 4.3 kcal/mol, respectively.

Simulating nucleation of molecular crystals is extremely challenging for all but the simplest cases. The challenge lies in formulating effective order parameters that are capable of driving the transition process. In recent years, order parameters based on molecular pair-functions have been successfully used in combination with enhanced sampling techniques to simulate nucleation of simple molecular crystals. However, despite the success of these approaches, we demonstrate that they can fail when applied to more complex cases. In fact, we show that order parameters based on molecular pair-functions, while successful at nucleating benzene, fail for paracetamol. Hence, we introduce a novel approach to formulate order parameters. In our approach, we construct reduced dimensional distributions of relevant quantities on the fly and then quantify the difference between these distributions and selected reference distributions. By computing the distribution of different quantities and by choosing different reference distributions, it is possible to systematically construct an effective set of order parameters. We then show that our new order parameters are capable of driving the nucleation of ordered states and, in particular, the form I crystal of paracetamol.