Solvation entropy, enthalpy and free energy prediction using a multi-task deep learning functional in 1D-RISM

Simultaneous calculation of entropies, enthalpies and free energies has been a long-standing challenge in computational chemistry, partly because of the difficulty in obtaining estimates of all three properties from a single consistent simulation methodology. This has been particularly true for methods from the Integral Equation Theory of Molecular Liquids such as the Reference Interaction Site Model which have traditionally given large errors in solvation thermodynamics. Recently, we presented pyRISM-CNN, a combination of the 1 Dimensional Reference Interaction Site Model (1D-RISM) solver, pyRISM, with a deep learning based free energy functional, as a method of predicting solvation free energy (SFE). With this approach, a 40-fold improvement in prediction accuracy was delivered for a multi-solvent, multi-temperature dataset when compared to the standard 1D-RISM theory [Fowles et al., Digital Discovery, 2023, 2, 177–188]. Here, we report three further developments to the pyRISM-CNN methodology. Firstly, solvation free energies have been introduced for organic molecular ions in methanol or water solvent systems at 298 K, with errors below 4 kcal mol−1 obtained without the need for corrections or additional descriptors. Secondly, the number of solvents in the training data has been expanded from carbon tetrachloride, water and chloroform to now also include methanol. For neutral solutes, prediction errors nearing or below 1 kcal mol−1 are obtained for each organic solvent system at 298 K and water solvent systems at 273–373 K. Lastly, pyRISM-CNN was successfully applied to the simultaneous prediction of solvation enthalpy, entropy and free energy through a multi-task learning approach, with errors of 1.04, 0.98 and 0.47 kcal mol−1, respectively, for water solvent systems at 298 K

Accurately predicting solvation free energy in aqueous and organic solvents beyond 298 K by combining deep learning and the 1D reference interaction site model

We report a method to predict the absolute solvation free energy (SFE) of small organic and druglike molecules in water, carbon tetrachloride and chloroform solvents beyond 298 K by combining the 1 Dimensional Reference Interaction Site Model (1D-RISM) and deep learning. RISM is a statistical mechanics based method for modelling molecular solutions that is computationally inexpensive but is too inaccurate for routine SFE calculations in its common form. By replacing the 1D-RISM SFE functional with a 1D convolutional neural network (CNN) trained on RISM correlation functions, we show that predictions approaching chemical accuracy can be obtained for aqueous and non-aqueous solvents at a wide-range of temperatures. This method builds upon the previously reported RISM-MOL-INF procedure which applied RISM to accurately characterise solvation and desolvation processes through solute–solvent correlation functions [Palmer et al., Mol. Pharm., 2015, 12, 3420–3432]. Unlike RISM-MOL-INF however, the newly developed pyRISM-CNN model applied here is capable of rapidly modelling these processes in several different solvents and at a wide-range of temperatures. The pyRISM-CNN functional reduces the predictive error by up to 40-fold as compared to the standard 1D-RISM theory. Prediction errors below 1 kcal mol−1 are obtained for organic solutes in carbon tetrachloride or chloroform solvent systems at 298 K and water solvent systems at 273–373 K. pyRISM-CNN has been implemented in our in-house 1D-RISM solver (pyRISM), which is made freely available as open-source software

Living with diabetes: rationale, study design and baseline characteristics for an Australian prospective cohort study

Background: Diabetes mellitus is a major global public health threat. In Australia, as elsewhere, it is responsible for a sizeable portion of the overall burden of disease, and significant costs. The psychological and social impact of diabetes on individuals with the disease can be severe, and if not adequately addressed, can lead to the worsening of the overall disease picture. The Living With Diabetes Study aims to contribute to a holistic understanding of the psychological and social aspects of diabetes mellitus

Erratum to: 36th International Symposium on Intensive Care and Emergency Medicine

[This corrects the article DOI: 10.1186/s13054-016-1208-6.]

The seeds of divergence: the economy of French North America, 1688 to 1760

Generally, Canada has been ignored in the literature on the colonial origins of divergence with most of the attention going to the United States. Late nineteenth century estimates of income per capita show that Canada was relatively poorer than the United States and that within Canada, the French and Catholic population of Quebec was considerably poorer. Was this gap long standing? Some evidence has been advanced for earlier periods, but it is quite limited and not well-suited for comparison with other societies. This thesis aims to contribute both to Canadian economic history and to comparative work on inequality across nations during the early modern period. With the use of novel prices and wages from Quebec—which was then the largest settlement in Canada and under French rule—a price index, a series of real wages and a measurement of Gross Domestic Product (GDP) are constructed. They are used to shed light both on the course of economic development until the French were defeated by the British in 1760 and on standards of living in that colony relative to the mother country, France, as well as the American colonies. The work is divided into three components. The first component relates to the construction of a price index. The absence of such an index has been a thorn in the side of Canadian historians as it has limited the ability of historians to obtain real values of wages, output and living standards. This index shows that prices did not follow any trend and remained at a stable level. However, there were episodes of wide swings—mostly due to wars and the monetary experiment of playing card money. The creation of this index lays the foundation of the next component. The second component constructs a standardized real wage series in the form of welfare ratios (a consumption basket divided by nominal wage rate multiplied by length of work year) to compare Canada with France, England and Colonial America. Two measures are derived. The first relies on a “bare bones” definition of consumption with a large share of land-intensive goods. This measure indicates that Canada was poorer than England and Colonial America and not appreciably richer than France. However, this measure overestimates the relative position of Canada to the Old World because of the strong presence of land-intensive goods. A second measure is created using a “respectable” definition of consumption in which the basket includes a larger share of manufactured goods and capital-intensive goods. This second basket better reflects differences in living standards since the abundance of land in Canada (and Colonial America) made it easy to achieve bare subsistence, but the scarcity of capital and skilled labor made the consumption of luxuries and manufactured goods (clothing, lighting, imported goods) highly expensive. With this measure, the advantage of New France over France evaporates and turns slightly negative. In comparison with Britain and Colonial America, the gap widens appreciably. This element is the most important for future research. By showing a reversal because of a shift to a different type of basket, it shows that Old World and New World comparisons are very sensitive to how we measure the cost of living. Furthermore, there are no sustained improvements in living standards over the period regardless of the measure used. Gaps in living standards observed later in the nineteenth century existed as far back as the seventeenth century. In a wider American perspective that includes the Spanish colonies, Canada fares better. The third component computes a new series for Gross Domestic Product (GDP). This is to avoid problems associated with using real wages in the form of welfare ratios which assume a constant labor supply. This assumption is hard to defend in the case of Colonial Canada as there were many signs of increasing industriousness during the eighteenth and nineteenth centuries. The GDP series suggest no long-run trend in living standards (from 1688 to circa 1765). The long peace era of 1713 to 1740 was marked by modest economic growth which offset a steady decline that had started in 1688, but by 1760 (as a result of constant warfare) living standards had sunk below their 1688 levels. These developments are accompanied by observations that suggest that other indicators of living standard declined. The flat-lining of incomes is accompanied by substantial increases in the amount of time worked, rising mortality and rising infant mortality. In addition, comparisons of incomes with the American colonies confirm the results obtained with wages— Canada was considerably poorer. At the end, a long conclusion is provides an exploratory discussion of why Canada would have diverged early on. In structural terms, it is argued that the French colony was plagued by the problem of a small population which prohibited the existence of scale effects. In combination with the fact that it was dispersed throughout the territory, the small population of New France limited the scope for specialization and economies of scale. However, this problem was in part created, and in part aggravated, by institutional factors like seigneurial tenure. The colonial origins of French America’s divergence from the rest of North America are thus partly institutional

Physics-based and deep learning approaches for the determination of solution thermodynamic parameters

Thermodynamic parameters associated with dissolution are frequently obtained via cheminformatics models, however, such an approach requires a significant quantity of training data, and does not guarantee that the model will perform competently for molecules beyond a threshold of dissimilarity to those used during training. Furthermore, physics-based approaches often include a range of approximations that limit their accuracy. There is thus much scope to develop new models which avoid these pitfalls. In Chapter 5, a physics-based approach for the prediction of intrinsic aqueous solubility is proposed. This proof-of-concept was developed for use with the sublimation thermodynamic cycle, and expands upon previous work by replacing several thermodynamic approximations with theoretically rigorous quantum mechanical calculations of the crystalline phase. Combining these with hydration free energies obtained from MD/FEP simulations or density functional theory leads to calculated solubilites that are comparable to both experiment and cheminformatics-based machine learning predictions. This approach also highlights how methods must be adapted to model different conformations in different phases, and the influence those conformations can have on any final solubility prediction. In Chapter 6, the accurate prediction of solvation free energy using 1D-RISM, in a method referred to as pyRISM-CNN, is reported. With this approach, a 1D CNN trained on RISM correlation functions is combined with the 1D-RISM solver, pyRISM, to predict the solvation free energy of small organic and drug-like molecules across several organic or aqueous solvent systems at temperatures beyond 298K. The pyRISMCNN functional reduces the predictive error by up to 40-fold as compared to the standard 1D-RISM theory, with errors below 1 kcal/mol obtained for solutes in organic solvents at 298K and water solvent systems at 273-373K. In Chapter 7, an extended version of the pyRISM-CNN methodology has also been developed to allow for the prediction of additional thermodynamic parameters in a wider range of solvents and environmental conditions. Firstly, the number of solvents in the training data has been expanded from carbon tetrachloride, water and chloroform to now also include methanol. Secondly, solvation free energies have been introduced for organic molecular ions in methanol and water solvent systems at 298K. For neutral solutes, prediction errors nearing or below 1 kcal/mol are obtained for each organic solvent system at 298K and water solvent systems at 273-373K. Errors below 4 kcal/mol are obtained for organic molecular ions without the need for corrections or additional descriptors. Lastly, pyRISM-CNN was successfully applied to the simultaneous prediction of solvation enthalpy, entropy and free energy through a multi-task learning approach, with errors of 1.04, 0.98 and 0.47 kcal/mol, respectively, for water solvent systems at 298K. There has been limited development of organic solvent models for use with RISM, with any development typically done on a solvent-by-solvent basis without a standardised procedure. The challenges faced when building organic solvent models within RISM are often the result of common convergence problems observed during model development, or with the choice of Lennard-Jones parameters used to represent intermolecular interactions. In Chapter 8, a new method of parameterising coarse-grained organic solvent models for the accurate prediction of solvation free energy is proposed. This method models solvent molecules as LJ spheres, with representative solvent parameters determined through an extensive grid search of the most favourable energy surfaces calculated from MP2 based QM calculations. This approach has been tested with the standard 3D-RISM theory and pyRISM-CNN, from which promising results were obtained for a chloroform solvent model that can rival the accuracy of current atomistic solvent models.Thermodynamic parameters associated with dissolution are frequently obtained via cheminformatics models, however, such an approach requires a significant quantity of training data, and does not guarantee that the model will perform competently for molecules beyond a threshold of dissimilarity to those used during training. Furthermore, physics-based approaches often include a range of approximations that limit their accuracy. There is thus much scope to develop new models which avoid these pitfalls. In Chapter 5, a physics-based approach for the prediction of intrinsic aqueous solubility is proposed. This proof-of-concept was developed for use with the sublimation thermodynamic cycle, and expands upon previous work by replacing several thermodynamic approximations with theoretically rigorous quantum mechanical calculations of the crystalline phase. Combining these with hydration free energies obtained from MD/FEP simulations or density functional theory leads to calculated solubilites that are comparable to both experiment and cheminformatics-based machine learning predictions. This approach also highlights how methods must be adapted to model different conformations in different phases, and the influence those conformations can have on any final solubility prediction. In Chapter 6, the accurate prediction of solvation free energy using 1D-RISM, in a method referred to as pyRISM-CNN, is reported. With this approach, a 1D CNN trained on RISM correlation functions is combined with the 1D-RISM solver, pyRISM, to predict the solvation free energy of small organic and drug-like molecules across several organic or aqueous solvent systems at temperatures beyond 298K. The pyRISMCNN functional reduces the predictive error by up to 40-fold as compared to the standard 1D-RISM theory, with errors below 1 kcal/mol obtained for solutes in organic solvents at 298K and water solvent systems at 273-373K. In Chapter 7, an extended version of the pyRISM-CNN methodology has also been developed to allow for the prediction of additional thermodynamic parameters in a wider range of solvents and environmental conditions. Firstly, the number of solvents in the training data has been expanded from carbon tetrachloride, water and chloroform to now also include methanol. Secondly, solvation free energies have been introduced for organic molecular ions in methanol and water solvent systems at 298K. For neutral solutes, prediction errors nearing or below 1 kcal/mol are obtained for each organic solvent system at 298K and water solvent systems at 273-373K. Errors below 4 kcal/mol are obtained for organic molecular ions without the need for corrections or additional descriptors. Lastly, pyRISM-CNN was successfully applied to the simultaneous prediction of solvation enthalpy, entropy and free energy through a multi-task learning approach, with errors of 1.04, 0.98 and 0.47 kcal/mol, respectively, for water solvent systems at 298K. There has been limited development of organic solvent models for use with RISM, with any development typically done on a solvent-by-solvent basis without a standardised procedure. The challenges faced when building organic solvent models within RISM are often the result of common convergence problems observed during model development, or with the choice of Lennard-Jones parameters used to represent intermolecular interactions. In Chapter 8, a new method of parameterising coarse-grained organic solvent models for the accurate prediction of solvation free energy is proposed. This method models solvent molecules as LJ spheres, with representative solvent parameters determined through an extensive grid search of the most favourable energy surfaces calculated from MP2 based QM calculations. This approach has been tested with the standard 3D-RISM theory and pyRISM-CNN, from which promising results were obtained for a chloroform solvent model that can rival the accuracy of current atomistic solvent models