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The partitioning of small molecules in cell membranes—a key parameter for pharmaceutical applications—typically relies on experimentally available bulk partitioning coefficients. Computer simulations provide a structural resolution of the insertion thermodynamics via the potential of mean force but require significant sampling at the atomistic level. Here, we introduce high-throughput coarse-grained molecular dynamics simulations to screen thermodynamic properties. This application of physics-based models in a large-scale study of small molecules establishes linear relationships between partitioning coefficients and key features of the potential of mean force. This allows us to predict the structure of the insertion from bulk experimental measurements for more than 400 000 compounds. The potential of mean force hereby becomes an easily accessible quantity—already recognized for its high predictability of certain properties, e.g., passive permeation. Further, we demonstrate how coarse graining helps reduce the size of chemical space, enabling a hierarchical approach to screening small molecules. [ABSTRACT FROM AUTHOR]

A mapping of a macromolecule is a prescription to construct a simplified representation of the system in which only a subset of its constituent atoms is retained. As the specific choice of the mapping affects the analysis of all-atom simulations as well as the construction of coarse-grained models, the characterisation of the mapping space has recently attracted increasing attention. We here introduce a notion of scalar product and distance between reduced representations, which allows the study of the metric and topological properties of their space in a quantitative manner. Making use of a Wang–Landau enhanced sampling algorithm, we exhaustively explore such space, and examine the qualitative features of mappings in terms of their squared norm. A one-to-one correspondence with an interacting lattice gas on a finite volume leads to the emergence of discontinuous phase transitions in mapping space, which mark the boundaries between qualitatively different reduced representations of the same molecule. [ABSTRACT FROM AUTHOR]

We compare different coarse-grained single-blob models for star polymers. We find that phenomenological models inspired by the Daoud-Cotton theory reproduce quite poorly the thermodynamics of these systems, even if the potential is assumed to be density dependent, as done in the analysis of experimental results. Using the numerically determined coarse-grained potential, we also determine the minimum value fc of the functionality of the star polymer for which a fluid-solid transition occurs. By applying the Hansen-Verlet criterion we find 35 < fc <= 40. This result is confirmed by an analysis that uses the modified (reference) hypernetted chain method and is qualitatively consistent with previous work. [ABSTRACT FROM AUTHOR]

Assessing the permeation rate of drug-like molecules across a lipid membrane is of paramount importance for pharmaceutical applications. While the Meyer-Overton rule relates the permeability coefficient to a few thermodynamic properties, its accuracy is limited by the homogeneity assumption. In this work, we extract an analogous relation for the permeation of a small solute through a lipid bilayer. This is obtained by systematically screening a subset of chemical space by means of high-throughput coarse-grained simulations, and relying on the accurate inhomogeneous solubility-diffusion model (ISDM). We connect the permeability coefficient of a compound to two molecular descriptors: partitioning free-energy and acid dissociation constant. In the ISDM the permeability is a functional of the potential of mean force. We discuss how – and when – combining together these profiles for a large variety of compounds can result in a smooth dependence of the permeability on the molecular descriptors. We focus on acidic molecules: for weak acids, the permeability largely depends on the main free-energy differences of the problem, which directly link to our molecular descriptors. For strong acids, the multivalued nature of the permeability requires to introduce a third variable, characterising the amphiphilicity of the small molecule. Abbreviations: CG: coarse-grained; HTCG: high-throughput coarse-grained; PMF: potential of mean force; ISDM: inhomogeneous solubility-diffusion model; DOPC: 1; 2-Dioleoyl-sn-glycero-3-phosphocholine [ABSTRACT FROM AUTHOR]

The determination of potentials of mean force for solute insertion in a lipid membrane by means of all-atom molecular dynamics simulations is often hampered by sampling issues. Recently, a multiscale method has been proposed to leverage the conformational ensemble of a lower-resolution model as starting point for higher resolution simulations. In this work, we analyze the efficiency of this method by comparing its predictions for propanol insertion into a lipid membrane against conventional atomistic umbrella sampling simulation results. The multiscale approach is confirmed to provide accurate results with a gain of one order of magnitude in computational time. We then investigate the role of the coarse-grained representation. We find that the accuracy of the results is tightly connected to the presence of a good configurational overlap between the coarse-grained and atomistic models—a general requirement when developing multiscale simulation methods. [ABSTRACT FROM AUTHOR]

We discuss the reliability of integral equation methods based on several commonly used closure relations in determining the phase diagram of coarse-grained models of soft-matter systems characterised by mutually interacting soft- and hard-core particles. Specifically, we consider a set of potentials appropriate to describe a system of hard-sphere colloids and linear homopolymers in good solvent, and investigate the behaviour when the soft particles are smaller than the colloids, which is the regime of validity of the coarse-grained models. Using computer-simulation results as a benchmark, we find that the hypernetted-chain approximation provides accurate estimates of thermodynamics and structure in the colloid–gas phase in which the density of colloids is small. On the other hand, all closures considered appear to be unable to describe the behaviour of the mixture in the colloid–liquid phase, as they cease to converge at polymer densities significantly smaller than those at the binodal. As a consequence, integral equations appear to be unable to predict a quantitatively correct phase diagram. [ABSTRACT FROM PUBLISHER]

The size of chemical compound space is too large to be probed exhaustively. This leads high-throughput protocols to drastically subsample and results in sparse and nonuniform datasets. Rather than arbitrarily selecting compounds, we systematically explore chemical space according to the target property of interest. We first perform importance sampling by introducing a Markov chain Monte Carlo scheme across compounds. We then train a machine learning (ML) model on the sampled data to expand the region of chemical space probed. Our boosting procedure enhances the number of compounds by a factor 2 to 10, enabled by the ML model's coarse-grained representation, which both simplifies the structure-property relationship and reduces the size of chemical space. The ML model correctly recovers linear relationships between transfer free energies. These linear relationships correspond to features that are global to the dataset, marking the region of chemical space up to which predictions are reliable; this is a more robust alternative to the predictive variance. Bridging coarse-grained simulations with ML gives rise to an unprecedented database of drug-membrane insertion free energies for 1.3 million compounds. [ABSTRACT FROM AUTHOR]