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Recent advances in cancer research have identified critical angiogenic signaling pathways and the influence of the extracellular matrix on endothelial cell migration. These findings provide us with insight into the process of angiogenesis that can facilitate the development of effective computational models of sprouting angiogenesis. In this work, we present the first three-dimensional model of sprouting angiogenesis that considers explicitly the effect of the extracellular matrix and of the soluble as well as matrix-bound growth factors on capillary growth. The computational model relies on a hybrid particle-mesh representation of the blood vessels and it introduces an implicit representation of the vasculature that can accommodate detailed descriptions of nutrient transport. Extensive parametric studies reveal the role of the extracellular matrix structure and the distribution of the different vascular endothelial growth factors isoforms on the dynamics and the morphology of the generated vascular networks.

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A new algorithm relying on finite integration is presented that solves the equations of continuum electrostatics for truncated ͑and possibly reaction-field corrected͒ solute-solvent and solventsolvent interactions under either nonperiodic or periodic boundary conditions. After testing and validation by comparison with existing methods, the algorithm is applied to investigate the effect of cutoff truncation and artificial periodicity in explicit-solvent simulations of ionic solvation and ion-ion interactions. Both cutoff truncation and artificial periodicity significantly alter the polarization around a spherical ion and thus, its solvation free energy. The nature and magnitude of the two perturbations are analyzed in details, and correction terms are proposed for both effects. Cutoff truncation is also shown to induce strong alterations in the potential of mean force for ion-ion interaction. These observations help to rationalize artifacts previously observed in explicitsolvent simulations, namely spurious features in the radial distribution functions close to the cutoff distance and alterations in the relative stabilities of contact, solvent-separated and free ion pairs.

Advances in computational chemistry create an ongoing need for larger and higher-quality datasets that characterize noncovalent molecular interactions. We present three benchmark collections of quantum mechanical data, covering approximately 3,700 distinct types of interacting molecule pairs. The first collection, which we refer to as DES370K, contains interaction energies for more than 370,000 dimer geometries. These were computed using the coupled-cluster method with single, double, and perturbative triple excitations [CCSD(T)], which is widely regarded as the gold-standard method in electronic structure theory. Our second benchmark collection, a core representative subset of DES370K called DES15K, is intended for more computationally demanding applications of the data. Finally, DES5M, our third collection, comprises interaction energies for nearly 5,000,000 dimer geometries; these were calculated using SNS-MP2, a machine learning approach that provides results with accuracy comparable to that of our coupled-cluster training data. These datasets may prove useful in the development of density functionals, empirically corrected wavefunction-based approaches, semi-empirical methods, force fields, and models trained using machine learning methods.