Broad chemical transferability in structure-based coarse-graining.

Compared to top-down coarse-grained (CG) models, bottom-up approaches are capable of offering higher structural fidelity. This fidelity results from the tight link to a higher resolution reference, making the CG model chemically specific. Unfortunately, chemical specificity can be at odds with compound-screening strategies, which call for transferable parameterizations.

Resolution limit of data-driven coarse-grained models spanning chemical space.

Increasing the efficiency of materials design remains a significant challenge given the large size of chemical compound space (CCS). The use of a chemically transferable coarse-grained model enables different molecular fragments to map to the same bead type, significantly increasing screening efficiency. Here, we propose new criteria for the design of coarse-grained models allowing for the optimization of their chemical transferability and evaluate the Martini model within this framework.

Controlled exploration of chemical space by machine learning of coarse-grained representations.

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.

Assessment of molecular dynamics simulations for amorphous poly(3-hexylthiophene) using neutron and X-ray scattering experiments.

The molecular morphology and dynamics of conjugated polymers in the bulk solid state play a significant role in determining macroscopic charge transport properties. To understand this relationship, molecular dynamics (MD) simulations and quantum mechanical calculations are used to evaluate local electronic properties. In this work, we investigate the importance of system and simulation parameters, such as force fields and equilibration methods, when simulating amorphous poly(3-hexylthiophene) (P3HT), a model semiconducting polymer.

Drug-Membrane Permeability across Chemical Space.

Unraveling the relation between the chemical structure of small druglike compounds and their rate of passive permeation across lipid membranes is of fundamental importance for pharmaceutical applications. The elucidation of a comprehensive structure-permeability relationship expressed in terms of a few molecular descriptors is unfortunately hampered by the overwhelming number of possible compounds. In this work, we reduce a priori the size and diversity of chemical space to solve an analogous-but smoothed out-structure-property relationship problem.

In silico screening of drug-membrane thermodynamics reveals linear relations between bulk partitioning and the potential of mean force.

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.