Comparison of thermodynamic properties of coarse-grained and atomic-level simulation models.

Thermodynamic data are often used to calibrate or test amomic-level (AL) force fields for molecular dynamics (MD) simulations. In contrast, the majority of coarse-grained (CG) force fields do not rely extensively on thermodynamic quantities. Recently, a CG force field for lipids, hydrocarbons, ions, and water, in which approximately four non-hydrogen atoms are mapped onto one interaction site, has been proposed and applied to study various aspects of lipid systems. To date, no extensive investigation of its capability to describe salvation thermodynamics has been undertaken. In the present study, a detailed picture of vaporization, solvation, and phase-partitioning thermodynamics for liquid hydrocarbons and water was obtained at CG and AL resolutions, in order to compare the two types or models and evaluate their ability to describe thermodynamic properties in the temperature range between 263 and 343 K. Both CG and AL models capture the experimental dependence of the thermodynamic properties on the temperature, albeit a systematically weaker dependence is found for the CG model. Moreover, deviations are found for solvation thermodynamics and for the corresponding enthalpy-entropy compensation for the CG model. Particularly water/oil repulsion seems to be overestimated. However, the results suggest that the thermodynamic properties considered should be reproducible by a CG model provided it is reparametrized on the basis of these liquid-phase properties.     

Value at Ruin and Tail Value at Ruin of the Compound Poisson Process with Diffusion and Efficient Computational Methods

We analyze the insurer risk under the compound Poisson risk process perturbed by a Wiener process with infinite time horizon. In the first part of this article, we consider the capital required to have fixed probability of ruin as a measure of risk and then a coherent extension of it, analogous to the tail value at risk. We show how both measures of risk can be efficiently computed by the saddlepoint approximation. We also show how to compute the stabilities of these measures of risk with respect to variations of probability of ruin. In the second part of this article, we are interested in the computation of the probability of ruin due to claim and the probability of ruin due to oscillation. We suggest a computational method based on upper and lower bounds of the probability of ruin and we compare it to the saddlepoint and to the Fast Fourier transform methods. This alternative method can be used to evaluate the proposed measures of risk and their stabilities with heavy-tailed individual losses, where the saddlepoint approximation cannot be used. The numerical accuracy of all proposed methods is very high and therefore these measures of risk can be reliably used in actuarial risk analysis.     

Electrolytes for quasi solid‐state dye‐sensitized solar cells based on block copolymers

The first use of PS_n‐b‐PEO_m‐b‐PS_n block copolymers (PS = polystyrene, PEO = poly(ethylene oxide)) as solid hosts for iodine/iodide electrolytes in dye‐sensitized solar cells (DSSCs) is described. Using the benchmark photosensitizer N719, DSSC based on the quasi solid‐state electrolytes afforded efficiencies up to 6.7%, to be compared with an efficiency of 7.3% obtained in similar conditions with a conventional iodine/iodide liquid electrolyte. By varying the PS:PEO relative volume ratio in the block copolymers different properties and morphologies were obtained. © 2013 Wiley Periodicals, Inc. J. Polym. Sci., Part A: Polym. Chem. 2014 , 52, 719–727     

A Maltol-Containing Ruthenium Polypyridyl Complex as a Potential Anticancer Agent

Cancer is one of the main causes of death worldwide. Chemotherapy, despite its severe side effects, is to date one of the leading strategies against cancer. Metal-based drugs present several potential advantages when compared to organic compounds and they have gained trust from the scientific community after the approval on the market of the drug cisplatin. Recently, we reported the ruthenium complex ([Ru(DIP)₂(sq)](PF₆) (where DIP is 4,7-diphenyl-1,10-phenantroline and sq is semiquinonate) with a remarkable potential as chemotherapeutic agent against cancer, both in vitro and in vivo. In this work, we analyse a structurally similar compound, namely [Ru(DIP)₂(mal)](PF₆), carrying the flavour-enhancing agent approved by the FDA, maltol (mal). To possess an FDA approved ligand is crucial for a complex, whose mechanism of action might include ligand exchange. Herein, we describe the synthesis and characterisation of [Ru(DIP)₂(mal)](PF₆), its stability in solutions and under conditions that resemble the physiological ones, and its in-depth biological investigation. Cytotoxicity tests on different cell lines in 2D model and on HeLa MultiCellular Tumour Spheroids (MCTS) demonstrated that our compound has higher activity than cisplatin, inspiring further tests. [Ru(DIP)₂(mal)](PF₆) was efficiently internalised by HeLa cells through a passive transport mechanism and severely affected the mitochondrial metabolism.     

Predicting retrosynthetic pathways using transformer-based models and a hyper-graph exploration strategy

We present an extension of our Molecular Transformer model combined with a hyper-graph exploration strategy for automatic retrosynthesis route planning without human intervention. The single-step retrosynthetic model sets a new state of the art for predicting reactants as well as reagents, solvents and catalysts for each retrosynthetic step. We introduce four metrics (coverage, class diversity, round-trip accuracy and Jensen–Shannon divergence) to evaluate the single-step retrosynthetic models, using the forward prediction and a reaction classification model always based on the transformer architecture. The hypergraph is constructed on the fly, and the nodes are filtered and further expanded based on a Bayesian-like probability. We critically assessed the end-to-end framework with several retrosynthesis examples from literature and academic exams. Overall, the frameworks have an excellent performance with few weaknesses related to the training data. The use of the introduced metrics opens up the possibility to optimize entire retrosynthetic frameworks by focusing on the performance of the single-step model only.

We present an extension of our Molecular Transformer model combined with a hyper-graph exploration strategy for automatic retrosynthesis route planning without human intervention.