Developing semi-empirical water model for efficiently simulating temperature-dependent chemisorption of CO in amine solvents.

Classical molecular dynamics (MD) simulations without bond forming/breaking cannot be used to model chemical reactions (CRs) among small molecules. Although the first-principle MD simulation can adequately describe CRs with explicit water molecules, such simulation is normally too costly for most researchers to afford. Generally, water molecules in a solvent can exert hydrophobic forces on reacting molecules, which yields a so-called caging effect that cannot be ignored when constructing a free energy landscape for reacting molecules.

Application of machine-learning algorithms to predict the transport properties of Mie fluids.

The ability to predict transport properties of fluids, such as the self-diffusion coefficient and viscosity, has been an ongoing effort in the field of molecular modeling. While there are theoretical approaches to predict the transport properties of simple systems, they are typically applied in the dilute gas regime and are not directly applicable to more complex systems. Other attempts to predict transport properties are performed by fitting available experimental or molecular simulation data to empirical or semi-empirical correlations.

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.

Utilizing Machine Learning for Efficient Parameterization of Coarse Grained Molecular Force Fields.

We present a machine learning approach to automated force field development in dissipative particle dynamics (DPD). The approach employs Bayesian optimization to parametrize a DPD force field against experimentally determined partition coefficients. The optimization process covers a discrete space of over 40 000 000 points, where each point represents the set of potentials that jointly forms a force field.

Challenge to Reconcile Experimental Micellar Properties of the CnEm Nonionic Surfactant Family.

We wished to compile a data set of results from the experimental literature to support the development and validation of accurate computational models (force fields) for an important class of micelle-forming nonionic surfactant compounds, the poly(ethylene oxide) alkyl ethers, usually denoted C E . However, careful examination of the experimental literature exposed a striking degree of variation in values reported for critical micelle concentrations (cmc) and mean aggregation numbers ( N). This variation was so large that it masked important trends known to exist within this family of molecules, thereby rendering most of the literature data to be of limited utility for force field development.

Cultural Change: Implementation of a Recovery Program in a Veterans Health Administration Medical Center Inpatient Unit.

Introduction: The Freedom Commission's recommendations, Substance Abuse and Mental Health Services Administration's framework, and policy directives on recovery-oriented services have fueled the recovery transformation. Mental health recovery services have been implemented in a broad range of outpatient settings. However, psychiatric inpatient units remained embedded in the traditional model of care.

Machine Learning of Dynamic Electron Correlation Energies from Topological Atoms.

We present an innovative method for predicting the dynamic electron correlation energy of an atom or a bond in a molecule utilizing topological atoms. Our approach uses the machine learning method Kriging (Gaussian Process Regression with a non-zero mean function) to predict these dynamic electron correlation energy contributions. The true energy values are calculated by partitioning the MP2 two-particle density-matrix via the Interacting Quantum Atoms (IQA) procedure.

The Transferability of Topologically Partitioned Electron Correlation Energies in Water Clusters.

The electronic effects that govern the cohesion of water clusters are complex, demanding the inclusion of N-body, Coulomb, exchange and correlation effects. Here we present a much needed quantitative study of the effect of correlation (and hence dispersion) energy on the stabilization of water clusters. For this purpose we used a topological energy partitioning method called Interacting Quantum Atoms (IQA) to partition water clusters into topological atoms, based on a MP2/6-31G(d,p) wave function, and modified versions of GAUSSIAN09 and the Quantum Chemical Topology (QCT) program MORFI.

Quantifying Electron Correlation of the Chemical Bond.

The Interacting Quantum Atoms (IQA) method is used to analyze the correlated part of the Møller-Plesset (MP) perturbation theory two-particle density matrix. Such an analysis determines the effects of electron correlation within atoms and between atoms, which covers both bonds and nonbonded through-space atom-atom interactions within a molecule or molecular complex. Electron correlation lowers the energy of the atoms at either end of a bond, but for the bond itself, it can be stabilizing or destabilizing.

Are the Sublimation Thermodynamics of Organic Molecules Predictable?

We compare a range of computational methods for the prediction of sublimation thermodynamics (enthalpy, entropy, and free energy of sublimation). These include a model from theoretical chemistry that utilizes crystal lattice energy minimization (with the DMACRYS program) and quantitative structure property relationship (QSPR) models generated by both machine learning (random forest and support vector machines) and regression (partial least squares) methods. Using these methods we investigate the predictability of the enthalpy, entropy and free energy of sublimation, with consideration of whether such a method may be able to improve solubility prediction schemes.

Implementation of Recovery Programming on an Inpatient Acute Psychiatric Unit and Its Impact on Readmission.

Background: Recovery-oriented models of care are evidence based and have been shown to improve patient satisfaction and outcomes as well as decrease the percentage of readmissions to inpatient psychiatric units.

Methods: This quality improvement project was implemented on a 16-bed inpatient adult mental health unit in a Veterans Affairs Medical Center. Percentages of readmissions were compared throughout the course of implementation of the recovery model.

Bringing computational science to the public.

Background: The increasing use of computers in science allows for the scientific analyses of large datasets at an increasing pace. We provided examples and interactive demonstrations at Dundee Science Centre as part of the 2015 Women in Science festival, to present aspects of computational science to the general public. We used low-cost Raspberry Pi computers to provide hands on experience in computer programming and demonstrated the application of computers to biology.

Uniting cheminformatics and chemical theory to predict the intrinsic aqueous solubility of crystalline druglike molecules.

We present four models of solution free-energy prediction for druglike molecules utilizing cheminformatics descriptors and theoretically calculated thermodynamic values. We make predictions of solution free energy using physics-based theory alone and using machine learning/quantitative structure-property relationship (QSPR) models. We also develop machine learning models where the theoretical energies and cheminformatics descriptors are used as combined input.

Enzyme informatics.

Over the last 50 years, sequencing, structural biology and bioinformatics have completely revolutionised biomolecular science, with millions of sequences and tens of thousands of three dimensional structures becoming available. The bioinformatics of enzymes is well served by, mostly free, online databases. BRENDA describes the chemistry, substrate specificity, kinetics, preparation and biological sources of enzymes, while KEGG is valuable for understanding enzymes and metabolic pathways.

Minimizing side reactions in chemoenzymatic dynamic kinetic resolution: organometallic and material strategies.

Chemoenzymatic dynamic kinetic resolution (DKR) of rac-1-phenyl ethanol into R-1-phenylethanol acetate was investigated with emphasis on the minimization of side reactions. The organometallic hydrogen transfer (racemization) catalyst was varied, and this was observed to alter the rate and extent of oxidation of the alcohol to form ketone side products. The performance of highly active catalyst [(pentamethylcyclopentadienyl)IrCl(2)(1-benzyl,3-methyl-imidazol-2-ylidene)] was found to depend on the batch of lipase B used.

First-Principles Calculation of the Intrinsic Aqueous Solubility of Crystalline Druglike Molecules.

We demonstrate that the intrinsic aqueous solubility of crystalline druglike molecules can be estimated with reasonable accuracy from sublimation free energies calculated using crystal lattice simulations and hydration free energies calculated using the 3D Reference Interaction Site Model (3D-RISM) of the Integral Equation Theory of Molecular Liquids (IET). The solubilities of 25 crystalline druglike molecules taken from different chemical classes are predicted by the model with a correlation coefficient of R = 0.85 and a root mean square error (RMSE) equal to 1.