Predicting solvation free energies using parameter-free solvent models

We present a new approach for predicting solvation free energies in non-aqueous solvents. Utilizing the corresponding states principle, we estimate solvent Lennard-Jones parameters directly from their critical points. Combined with atomic solutes and pressure corrected three-dimensional reference interaction site model (3D-RISM/PC+), the model gives accurate predictions for a wide range of non-polar solvents, including olive oil. The results, obtained without electrostatic interactions and with a very coarse-grained solvent provide an interesting alternative to widely used and heavily parametrized models

Hydration free energies of molecular ions from theory and simulation

We present a theoretical/computational framework for accurate calculation of hydration free energies of ionized molecular species. The method is based on a molecular theory, 3D-RISM, combined with a recently developed pressure correction (PC+). The 3D-RISM/PC+ model can provide ∼3 kcal/mol hydration free energy accuracy for a large variety of ionic compounds, provided that the Galvani potential of water is taken into account. The results are compared with direct atomistic simulations. Several methodological aspects of hydration free energy calculations for charged species are discussed

Fast and general method to predict the physicochemical properties of druglike molecules using the integral equation theory of molecular liquids

We report a method to predict physico-chemical properties of druglike molecules using a classical statistical mechanics based solvent model combined with machine learning. The RISM-MOL-INF method introduced here provides an accurate technique to characterize solvation and desolvation processes based on solute-solvent correlation functions computed by the 1D Reference Interaction Site Model of the Integral Equation Theory of Molecular Liquids. These functions can be obtained in a matter of minutes for most small organic and druglike molecules using existing software (RISM-MOL) (Sergiievskyi, V. P.; Hackbusch, W.; Fedorov, M. V. J. Comput. Chem. 2011, 32, 1982-1992.). Predictions of caco-2 cell permeability and hydration free energy obtained using the RISM-MOL-INF method are shown to be more accurate than the state-of-the-art tools for benchmark datasets. Due to the importance of solvation and desolvation effects in biological systems, it is anticipated that the RISM-MOL-INF approach will find many applications in biophysical and biomedical property prediction

Salting-out effects by pressure-corrected 3D-RISM

In this paper, we demonstrate that using a pressure corrected three-dimensional reference interaction site model (3D-RISM/PC+) one can accurately predict salting-out (Setschenow's) constants for a wide range of organic compounds in aqueous solutions of NaCl. The approach, based on classical molecular force fields, offers an alternative to more heavily parametrized methods

Measles: An overview of a re-emerging disease in children and immunocompromised patients

Despite the availability of a safe and effective vaccine, in 2018, around 350,000 measles cases were reported worldwide, which resulted in an estimate of 142,300 deaths from measles. Additionally, in 2017, global measles cases spiked, causing the death of 110,000 people, mostly children under the age of 5 years and immunocompromised adults. The increase in measles incidence is caused by the ongoing reduction of vaccination coverage. This event has triggered public and scientific interest. For this reason, we reviewed the pathophysiology of measles infection, focusing on mechanisms by which the virus spreads systemically through the host organism. By reaching the lymphocytes from the airways through a \u201ctrojan horse\u201d strategy, measles induces an immunosuppression status. H and F glycoproteins, both expressed in the envelope, ensure attachment of the virus to host cells and spreading from one cell to another by binding to several receptors, as described in detail. The severity of the disease depends both on the age and underlying conditions of patients as well as the social and health context in which epidemics spread, and is often burdened by sequelae and complications that may occur several years after infection. Particular attention was paid to special groups that are more susceptible to severe or atypical measles. An overview of microbiology, symptoms, diagnosis, prevention, and treatment completes and enriches the review

3D matters! 3D-RISM and 3D convolutional neural network for accurate bioaccumulation prediction

In this work, we present a new method for predicting complex physicalchemical properties of organic molecules. The approach utilizes 3D convolutional neural network (ActivNet4) that uses solvent spatial distributions around solutes as input. These spatial distributions are obtained by a molecular theory called threedimensional reference interaction site model (3D-RISM). We have shown that the method allows one to achieve a good accuracy of prediction of bioconcentration factor (BCF) which is difficult to predict by direct application of methods of molecular theory or simulations. Our research demonstrates that combination of molecular theories with modern machine learning approaches can be effectively used for predicting properties that are otherwise inaccessible to purely theory-based models

Launch of the Space experiment PAMELA

PAMELA is a satellite borne experiment designed to study with great accuracy cosmic rays of galactic, solar, and trapped nature in a wide energy range protons: 80 MeV-700 GeV, electrons 50 MeV-400 GeV). Main objective is the study of the antimatter component: antiprotons (80 MeV-190 GeV), positrons (50 MeV-270 GeV) and search for antimatter with a precision of the order of 10^-8). The experiment, housed on board the Russian Resurs-DK1 satellite, was launched on June, 15, 2006 in a 350*600 km orbit with an inclination of 70 degrees. The detector is composed of a series of scintillator counters arranged at the extremities of a permanent magnet spectrometer to provide charge, Time-of-Flight and rigidity information. Lepton/hadron identification is performed by a Silicon-Tungsten calorimeter and a Neutron detector placed at the bottom of the device. An Anticounter system is used offline to reject false triggers coming from the satellite. In self-trigger mode the Calorimeter, the neutron detector and a shower tail catcher are capable of an independent measure of the lepton component up to 2 TeV. In this work we describe the experiment, its scientific objectives and the performance in the first months after launch.Comment: Accepted for publication on Advances in Space Researc

PAMELA - A Payload for Antimatter Matter Exploration and Light-nuclei Astrophysics

The PAMELA experiment is a satellite-borne apparatus designed to study charged particles in the cosmic radiation with a particular focus on antiparticles. PAMELA is mounted on the Resurs DK1 satellite that was launched from the Baikonur cosmodrome on June 15th 2006. The PAMELA apparatus comprises a time-of-flight system, a magnetic spectrometer, a silicon-tungsten electromagnetic calorimeter, an anticoincidence system, a shower tail catcher scintillator and a neutron detector. The combination of these devices allows antiparticles to be reliably identified from a large background of other charged particles. This paper reviews the design, space qualification and on-ground performance of PAMELA. The in-orbit performance will be discussed in future publications.The PAMELA experiment is a satellite-borne apparatus designed to study charged particles in the cosmic radiation with a particular focus on antiparticles. PAMELA is mounted on the Resurs DK1 satellite that was launched from the Baikonur cosmodrome on June 15th 2006. The PAMELA apparatus comprises a time-of-flight system, a magnetic spectrometer, a silicon-tungsten electromagnetic calorimeter, an anticoincidence system, a shower tail catcher scintillator and a neutron detector. The combination of these devices allows antiparticles to be reliably identified from a large background of other charged particles. This paper reviews the design, space qualification and on-ground performance of PAMELA. The in-orbit performance will be discussed in future publications

The influence of solid state information and descriptor selection on statistical models of temperature dependent aqueous solubility.

Predicting the equilibrium solubility of organic, crystalline materials at all relevant temperatures is crucial to the digital design of manufacturing unit operations in the chemical industries. The work reported in our current publication builds upon the limited number of recently published quantitative structure-property relationship studies which modelled the temperature dependence of aqueous solubility. One set of models was built to directly predict temperature dependent solubility, including for materials with no solubility data at any temperature. We propose that a modified cross-validation protocol is required to evaluate these models. Another set of models was built to predict the related enthalpy of solution term, which can be used to estimate solubility at one temperature based upon solubility data for the same material at another temperature. We investigated whether various kinds of solid state descriptors improved the models obtained with a variety of molecular descriptor combinations: lattice energies or 3D descriptors calculated from crystal structures or melting point data. We found that none of these greatly improved the best direct predictions of temperature dependent solubility or the related enthalpy of solution endpoint. This finding is surprising because the importance of the solid state contribution to both endpoints is clear. We suggest our findings may, in part, reflect limitations in the descriptors calculated from crystal structures and, more generally, the limited availability of polymorph specific data. We present curated temperature dependent solubility and enthalpy of solution datasets, integrated with molecular and crystal structures, for future investigations

Accurate hydration free energies at a wide range of temperatures from 3D-RISM

We present a new model for computing hydration free energies by 3D reference interaction site model (3D-RISM) that uses an appropriate initial state of the system (as suggested by Sergiievskyi et al.). The new adjustment to 3D-RISM theory significantly improves hydration free energy predictions for various classes of organic molecules at both ambient and non-ambient temperatures. An extensive benchmarking against experimental data shows that the accuracy of the model is comparable to (much more computationally expensive) molecular dynamics simulations. The calculations can be readily performed with a standard 3D-RISM algorithm. In our work, we used an open source package AmberTools; a script to automate the whole procedure is available on the web (https://github.com/ MTS-Strathclyde/ISc