Symmetry effects and equivalences in lattice models of hydrophobic interaction

We establish the equivalence of a recently introduced discrete model of the hydrophobic interaction, as well as its extension to continuous state variables, with the Ising model in a magnetic field with temperature-dependent strength. In order to capture the effect of symmetries of the solvent particles we introduce a generalized multi-state model. We solve this model - which is not of the Ising type - exactly in one dimension. Our findings suggest that a small increase in symmetry decreases the amplitude of the solvent-mediated part of the potential of mean force between solute particles and enhances the solubility in a very simple fashion. High symmetry decreases also the range of the attractive potential. This weakening of the hydrophobic effect observed in the model is in agreement with the notion that the effect is entropic in origin.Comment: 19 pages, 2 figure

Blinded Predictions and Post Hoc Analysis of the Second Solubility Challenge Data: Exploring Training Data and Feature Set Selection for Machine and Deep Learning Models

Accurate methods to predict solubility from molecular structure are highly sought after in the chemical sciences. To assess the state of the art, the American Chemical Society organized a "Second Solubility Challenge"in 2019, in which competitors were invited to submit blinded predictions of the solubilities of 132 drug-like molecules. In the first part of this article, we describe the development of two models that were submitted to the Blind Challenge in 2019 but which have not previously been reported. These models were based on computationally inexpensive molecular descriptors and traditional machine learning algorithms and were trained on a relatively small data set of 300 molecules. In the second part of the article, to test the hypothesis that predictions would improve with more advanced algorithms and higher volumes of training data, we compare these original predictions with those made after the deadline using deep learning models trained on larger solubility data sets consisting of 2999 and 5697 molecules. The results show that there are several algorithms that are able to obtain near state-of-the-art performance on the solubility challenge data sets, with the best model, a graph convolutional neural network, resulting in an RMSE of 0.86 log units. Critical analysis of the models reveals systematic differences between the performance of models using certain feature sets and training data sets. The results suggest that careful selection of high quality training data from relevant regions of chemical space is critical for prediction accuracy but that other methodological issues remain problematic for machine learning solubility models, such as the difficulty in modeling complex chemical spaces from sparse training data sets

Constructions of free commutative integro-differential algebras

In this survey, we outline two recent constructions of free commutative integro-differential algebras. They are based on the construction of free commutative Rota-Baxter algebras by mixable shuffles. The first is by evaluations. The second is by the method of Gr\"obner-Shirshov bases.Comment: arXiv admin note: substantial text overlap with arXiv:1302.004

Non-monotonic variation with salt concentration of the second virial coefficient in protein solutions

The osmotic virial coefficient $B_2$ of globular protein solutions is calculated as a function of added salt concentration at fixed pH by computer simulations of the ``primitive model''. The salt and counter-ions as well as a discrete charge pattern on the protein surface are explicitly incorporated. For parameters roughly corresponding to lysozyme, we find that $B_2$ first decreases with added salt concentration up to a threshold concentration, then increases to a maximum, and then decreases again upon further raising the ionic strength. Our studies demonstrate that the existence of a discrete charge pattern on the protein surface profoundly influences the effective interactions and that non-linear Poisson Boltzmann and Derjaguin-Landau-Verwey-Overbeek (DLVO) theory fail for large ionic strength. The observed non-monotonicity of $B_2$ is compared to experiments. Implications for protein crystallization are discussed.Comment: 43 pages, including 17 figure

Variation among 532 genomes unveils the origin and evolutionary history of a global insect herbivore

The diamondback moth, Plutella xylostella is a cosmopolitan pest that has evolved resistance to all classes of insecticide, and costs the world economy an estimated US $4-5 billion annually. We analyse patterns of variation among 532 P. xylostella genomes, representing a worldwide sample of 114 populations. We find evidence that suggests South America is the geographical area of origin of this species, challenging earlier hypotheses of an Old-World origin. Our analysis indicates that Plutella xylostella has experienced three major expansions across the world, mainly facilitated by European colonization and global trade. We identify genomic signatures of selection in genes related to metabolic and signaling pathways that could be evidence of environmental adaptation. This evolutionary history of P. xylostella provides insights into transoceanic movements that have enabled it to become a worldwide pest.Minsheng You … Simon W. Baxter … Christopher M. Ward … et al

Phase transition in spin systems with various types of fluctuations

Various types ordering processes in systems with large fluctuation are overviewed. Generally, the so-called order–disorder phase transition takes place in competition between the interaction causing the system be ordered and the entropy causing a random disturbance. Nature of the phase transition strongly depends on the type of fluctuation which is determined by the structure of the order parameter of the system. As to the critical property of phase transitions, the concept “universality of the critical phenomena” is well established. However, we still find variety of features of ordering processes. In this article, we study effects of various mechanisms which bring large fluctuation in the system, e.g., continuous symmetry of the spin in low dimensions, contradictions among interactions (frustration), randomness of the lattice, quantum fluctuations, and a long range interaction in off-lattice systems