Our OPLS-2009IL force field parameters (J. Chem. Theory Comput. 2009, 5, 1038-1050) were originally developed and tested on 68 unique ionic liquids featuring the 1-alkyl-3-methylimidazolium [RMIM], N-alkylpyridinium [RPyr], and choline cations. Experimental validation was limited to densities and a few, largely conflicting, heat of vaporization (ΔH) values reported in the literature at the time. Owing to the use of Monte Carlo as our sampling technique, it was also not possible to investigate the reproduction of dynamics. The [RMIM] OPLS-2009IL parameters have been revisited in this work and adapted for use in molecular dynamics (MD) simulations. In addition, new OPLS-AA parameters have been developed for multiple anions, i.e., AlCl, BF, Br, Cl, NO, PF, acetate, benzoate bis(pentafluoroethylsulfonyl)amide, bis(trifluoroethylsulfonyl)amide, dicyanamide, formate, methylsulfate, perchlorate, propanoate, thiocyanate, tricyanomethanide, and trifluoromethanesulfonate. The computed solvent densities, heats of vaporization, viscosities, diffusion coefficients, heat capacities, surface tensions, and other relevant solvent data compared favorably with experiment. A charge scaling of ±0.8 e was also investigated as a means to mimic polarization and charge transfer effects. The 0.8-scaling led to significant improvements for ΔH, surface tension, and self-diffusivity; however, a concern when scaling charges is the potential degradation of local intermolecular interactions at short ranges. Radial distribution functions (RDFs) were used to examine cation-anion interactions when employing 0.8*OPLS-2009IL and the scaled force field accurately reproduced RDFs from ab initio MD simulations.

The Drug Design Data Resource (D3R) ran Grand Challenge 2015 between September 2015 and February 2016. Two targets served as the framework to test community docking and scoring methods: (i) HSP90, donated by AbbVie and the Community Structure Activity Resource (CSAR), and (ii) MAP4K4, donated by Genentech. The challenges for both target datasets were conducted in two stages, with the first stage testing pose predictions and the capacity to rank compounds by affinity with minimal structural data; and the second stage testing methods for ranking compounds with knowledge of at least a subset of the ligand-protein poses. An additional sub-challenge provided small groups of chemically similar HSP90 compounds amenable to alchemical calculations of relative binding free energy. Unlike previous blinded Challenges, we did not provide cognate receptors or receptors prepared with hydrogens and likewise did not require a specified crystal structure to be used for pose or affinity prediction in Stage 1. Given the freedom to select from over 200 crystal structures of HSP90 in the PDB, participants employed workflows that tested not only core docking and scoring technologies, but also methods for addressing water-mediated ligand-protein interactions, binding pocket flexibility, and the optimal selection of protein structures for use in docking calculations. Nearly 40 participating groups submitted over 350 prediction sets for Grand Challenge 2015. This overview describes the datasets and the organization of the challenge components, summarizes the results across all submitted predictions, and considers broad conclusions that may be drawn from this collaborative community endeavor.

The Drug Design Data Resource (D3R) ran Grand Challenge 2 (GC2) from September 2016 through February 2017. This challenge was based on a dataset of structures and affinities for the nuclear receptor farnesoid X receptor (FXR), contributed by F. Hoffmann-La Roche. The dataset contained 102 IC50 values, spanning six orders of magnitude, and 36 high-resolution co-crystal structures with representatives of four major ligand classes. Strong global participation was evident, with 49 participants submitting 262 prediction submission packages in total. Procedurally, GC2 mimicked Grand Challenge 2015 (GC2015), with a Stage 1 subchallenge testing ligand pose prediction methods and ranking and scoring methods, and a Stage 2 subchallenge testing only ligand ranking and scoring methods after the release of all blinded co-crystal structures. Two smaller curated sets of 18 and 15 ligands were developed to test alchemical free energy methods. This overview summarizes all aspects of GC2, including the dataset details, challenge procedures, and participant results. We also consider implications for progress in the field, while highlighting methodological areas that merit continued development. Similar to GC2015, the outcome of GC2 underscores the pressing need for methods development in pose prediction, particularly for ligand scaffolds not currently represented in the Protein Data Bank ( http://www.pdb.org ), and in affinity ranking and scoring of bound ligands.

This study investigates the effect of roughness on interfacial properties of an n-alkanethiolate self-assembled monolayer (SAM) and uses hydrophobicity to demonstrate the existence of upper and lower limits. This article also sheds light on the origin of the previously unexplained gradual increase in contact angles with increases in the size of the molecule making the SAM. We prepared Au surfaces with a root-mean-square (RMS) roughness of ∼0.2-0.5 nm and compared the wetting properties of n-alkanethiolate (C10-C16) SAMs fabricated on these surfaces. Static contact angles, θ(s), formed between the SAM and water, diethylene glycol, and hexadecane showed an odd-even effect irrespective of the solvent properties. The average differences in subsequent SAM(E) and SAM(O) are Δθ(s|n  – (n+1)|) ≈ 1.7° (n = even) and Δθ(s|n – (n+1)|) ≈ 3.1° (n = odd). A gradual increase in θ(s) with increasing length of the molecule was observed, with values ranging from water 104.7-110.7° (overall Δθ(s) = 6.0° while for the evens Δθ(s)(E) = 4.4° and odds Δθ(s)(O) = 3.5°) to diethylene glycol 72.9-80.4° (overall Δθ(s) = 7.5° while for the evens Δθ(s)(E) = 2.9° and odds Δθ(s)(O) = 2.4°) and hexadecane 40.4–49.4° (overall Δθ(s) = 9.0° while for the evens Δθ(s)(E) = 3.7° and odds Δθ(s)(O) = 2.1°). This article establishes that the gradual increase in θ(s) with increasing molecular size in SAMs is due to asymmetry in the zigzag oscillation in the odd-even effect. Comparison of the magnitude and proportion differences in this asymmetry allows us to establish the reduction in interfacial dispersive forces, due to increasing SAM crystallinity with increasing molecular size, as the origin of this asymmetry. By comparing the dependence of θ(s) on surface roughness we infer that (i) RMS roughness ≈ 1 nm is a theoretical limit beyond which the odd-even effect cannot be observed and (ii) on a hypothetically flat surface the maximum difference in hydrophobicity, as expressed in θ(s), is ∼3°.

The origin of the odd-even effect in properties of self-assembled monolayers (SAMs) and/or technologies derived from them is poorly understood. We report that hydrophobicity and, hence, surface wetting of SAMs are dominated by the nature of the substrate (surface roughness and identity) and SAM tilt angle, which influences surface dipoles/orientation of the terminal moiety. We measured static contact angles (θs) made by water droplets on n-alkanethiolate SAMs with an odd (SAM(O)) or even (SAM(E)) number of carbons (average θs range of 105.8-112.1°). When SAMs were fabricated on smooth "template-stripped" metal (M(TS)) surfaces [root-mean-square (rms) roughness = 0.36 ± 0.01 nm for Au(TS) and 0.60 ± 0.04 nm for Ag(TS)], the odd-even effect, characterized by a zigzag oscillation in values of θs, was observed. We, however, did not observe the same effect with rougher "as-deposited" (M(AD)) surfaces (rms roughness = 2.27 ± 0.16 nm for Au(AD) and 5.13 ± 0.22 nm for Ag(AD)). The odd-even effect in hydrophobicity inverts when the substrate changes from Au(TS) (higher θs for SAM(E) than SAM(O), with average Δθs |n - (n + 1)| ≈ 3°) to Ag(TS) (higher θs for SAM(O) than SAM(E), with average Δθs |n - (n + 1)| ≈ 2°). A comparison of hydrophobicity across Ag(TS) and Au(TS) showed a statistically significant difference (Student's t test) between SAM(E) (Δθs |Ag evens - Au evens| ≈ 5°; p < 0.01) but failed to show statistically significant differences on SAM(O) (Δθs |Ag odds - Au odds| ≈ 1°; p > 0.1). From these results, we deduce that the roughness of the metal substrate (from comparison of M(AD) versus M(TS)) and orientation of the terminal -CH2CH3 (by comparing SAM(E) and SAM(O) on Au(TS) versus Ag(TS)) play major roles in the hydrophobicity and, by extension, general wetting properties of n-alkanethiolate SAMs.