Hepatic glucagon action: beyond glucose mobilization.

Glucagon's ability to promote hepatic glucose production has been known for over a century, with initial observations touting this hormone as a diabetogenic agent. However, glucagon receptor agonism [when balanced with an incretin, including glucagon-like peptide 1 (GLP-1) to dampen glucose excursions] is now being developed as a promising therapeutic target in the treatment of metabolic diseases, like metabolic dysfunction-associated steatotic disease/metabolic dysfunction-associated steatohepatitis (MASLD/MASH), and may also have benefit for obesity and chronic kidney disease. Conventionally regarded as the opposing tag-team partner of the anabolic mediator insulin, glucagon is gradually emerging as more than just a "catabolic hormone." Glucagon action on glucose homeostasis within the liver has been well characterized. However, growing evidence, in part thanks to new and sensitive "omics" technologies, has implicated glucagon as more than just a "glucose liberator." Elucidation of glucagon's capacity to increase fatty acid oxidation while attenuating endogenous lipid synthesis speaks to the dichotomous nature of the hormone. Furthermore, glucagon action is not limited to just glucose homeostasis and lipid metabolism, as traditionally reported. Glucagon plays key regulatory roles in hepatic amino acid and ketone body metabolism, as well as mitochondrial turnover and function, indicating broader glucagon signaling consequences for metabolic homeostasis mediated by the liver. Here we examine the broadening role of glucagon signaling within the hepatocyte and question the current dogma, to appreciate glucagon as more than just that "catabolic hormone."

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.

Steering New Drug Discovery Campaigns: Permeability, Solubility, and Physicochemical Properties in the bRo5 Chemical Space.

An increasing number of drug discovery programs concern compounds in the beyond rule of 5 (bRo5) chemical space, such as cyclic peptides, macrocycles, and degraders. Recent results show that common paradigms of property-based drug design need revision to be applied to larger and more flexible compounds. A virtual event entitled "Solubility, permeability and physico-chemical properties in the bRo5 chemical space" was organized to provide preliminary guidance on how to make the discovery of oral drugs in the bRo5 space more effective. The four speakers emphasized the importance of the bRo5 space as a source of new oral drugs and provided examples of experimental and computational methods specifically tailored for design and optimization in this chemical space.

Membrane Permeability in Cyclic Peptides is Modulated by Core Conformations.

Cyclic peptides have the potential to bind to challenging targets, which are undruggable with small molecules, but their application is limited by low membrane permeability. Here, using a series of cyclic pentapeptides, we showed that established physicochemical criteria of permeable peptides are heavily violated. We revealed that a dominant core conformation, stabilized by amides' shielding pattern, could guide the design of novel compounds. As a result, counter-intuitive strategies, such as incorporation of polar residues, can be beneficial for permeability. We further find that core globularity is a promising descriptor, which can extend the capability of standard predictive models.

Multisolvent Models for Solvation Free Energy Predictions Using 3D-RISM Hydration Thermodynamic Descriptors.

The potential to predict solvation free energies (SFEs) in any solvent using a machine learning (ML) model based on thermodynamic output, extracted exclusively from 3D-RISM simulations in water is investigated. The models on multiple solvents take into account both the solute and solvent description and offer the possibility to predict SFEs of any solute in any solvent with root mean squared errors less than 1 kcal/mol. Validations that involve exclusion of fractions or clusters of the solutes or solvents exemplify the model's capability to predict SFEs of novel solutes and solvents with diverse chemical profiles. In addition to being predictive, our models can identify the solute and solvent features that influence SFE predictions. Furthermore, using 3D-RISM hydration thermodynamic output to predict SFEs in any organic solvent reduces the need to run 3D-RISM simulations in all these solvents. Altogether, our multisolvent models for SFE predictions that take advantage of the solvation effects are expected to have an impact in the property prediction space.

Water envelope has a critical impact on the design of protein-protein interaction inhibitors.

We show that a water envelope network plays a critical role in protein-protein interactions (PPI). The potency of a PPI inhibitor is modulated by orders of magnitude on manipulation of the solvent envelope alone. The structure-activity relationship of PEX14 inhibitors was analyzed as an example using in silico and X-ray data.

Toward a universal model to calculate the solvation thermodynamics of druglike molecules: the importance of new experimental databases.

We demonstrate that a new free energy functional in the integral equation theory of molecular liquids gives accurate calculations of hydration thermodynamics for druglike molecules. The functional provides an improved description of excluded volume effects by incorporating two free coefficients. When the values of these coefficients are obtained from experimental data for simple organic molecules, the hydration free energies of an external test set of druglike molecules can be calculated with an accuracy of about 1 kcal/mol. The 3D RISM/UC method proposed here is easily implemented using existing computational software and allows in silico screening of the solvation thermodynamics of potential pharmaceutical molecules at significantly lower computational expense than explicit solvent simulations.

In silico screening of bioactive and biomimetic solutes using Integral Equation Theory.

The Integral Equation Theory (IET) of Molecular Liquids is a theoretical framework for modelling solution phase behaviour that has recently found new applications in computational drug design. IET allows calculation of solvation thermodynamic parameters at significantly lower computational expense than explicit solvent simulations, but also provides information about the microscopic solvent structure that is not accessible by implicit continuum models. In this review we focus on recent advances in two fields of research using these methods: (i) calculation of the hydration free energies of bioactive molecules; (ii) modelling the aggregation of biomimetic molecules. In addition, we discuss sources of experimental solvation data for druglike molecules.

Hydration thermodynamics using the reference interaction site model: speed or accuracy?

We report a method to dramatically improve the accuracy of hydration free energies (HFE) calculated by the 1D and 3D reference interaction site models (RISM) of molecular integral equation theory. It is shown that the errors in HFEs calculated by RISM approaches using the Gaussian fluctuations (GF) free energy functional are not random, but can be decomposed into linear combination of contributions from different structural elements of molecules (number of double bonds, number of OH groups, etc.). Therefore, by combining RISM/GF with cheminformatics, one can develop an accurate method for HFE prediction. We call this approach the structural description correction model (SDC) ( Ratkova et al. J. Phys. Chem. B 2010 , 114 , 12068 ). In this work, we investigated the prediction quality of the SDC model combined with 1D and 3D RISM approaches. In parallel, we analyzed the computational performance of these two methods. The SDC model parameters were obtained by fitting against a training set of 53 simple organic molecules. To demonstrate that the values of these parameters were transferable between different classes of molecules, the models were tested against 98 more complex molecules (including 38 polyfragment compounds). The results show that the 3D RISM/SDC model predicts the HFEs with very good accuracy (RMSE of 0.47 kcal/mol), while the 1D RISM approach provides only moderate accuracy (RMSE of 1.96 kcal/mol). However, a single 1D RISM/SDC calculation takes only a few seconds on a PC, whereas a single 3D RISM/SDC HFE calculation is approximately 100 times more computationally expensive. Therefore, we suggest that one should use the 1D RISM/SDC model for large-scale high-throughput screening of molecular hydration properties, while further refinement of these properties for selected compounds should be carried out with the more computationally expensive but more accurate 3D RISM/SDC model.

Combination of RISM and Cheminformatics for Efficient Predictions of Hydration Free Energy of Polyfragment Molecules: Application to a Set of Organic Pollutants.

Here, we discuss a new method for predicting the hydration free energy (HFE) of organic pollutants and illustrate the efficiency of the method on a set of 220 chlorinated aromatic hydrocarbons. The new model is computationally inexpensive, with one HFE calculation taking less than a minute on a PC. The method is based on a combination of a molecular integral equations theory, one-dimensional reference interaction site model (1D RISM), with the cheminformatics approach. We correct HFEs obtained by the 1D RISM with a set of empirical corrections. The corrections are associated with the partial molar volume and structural descriptors of the molecules. We show that the introduced corrections can significantly improve the quality of the 1D RISM HFE predictions obtained by the partial wave free energy expression [ Ten-no , S. J. Chem. Phys. 2001 , 115 , 3724 ] and the Kovalenko-Hirata closure [ Kovalenko , A. ; Hirata , F. J. Chem. Phys. 1999 , 110 , 10095 ]. We also show that the quality of the model can be further improved by the reparametrization using QM-derived partial charges instead of the originally used OPLS-AA partial charges. The final model gives good results for polychlorinated benzenes (the mean and standard deviation of the error are 0.02 and 0.36 kcal/mol, correspondingly). At the same time, the model gives somewhat worse results for polychlorobiphenyls (PCBs) with a systematic bias of -0.72 kcal/mol but a small standard deviation equal to 0.55 kcal/mol. We note that the error remains the same for the whole set of PCBs, whereas errors of HFEs predicted with continuum solvation models (data were taken from Phillips , K. L. et al. Environ. Sci. Technol. 2008 , 42 , 8412 ) increase significantly for higher chlorinated PCB congeners. In conclusion, we discuss potential future applications of the model and several avenues for its further improvement.

On a relationship between molecular polarizability and partial molar volume in water.

We reveal a universal relationship between molecular polarizability (a single-molecule property) and partial molar volume in water that is an ensemble property characterizing solute-solvent systems. Since both of these quantities are of the key importance to describe solvation behavior of dissolved molecular species in aqueous solutions, the obtained relationship should have a high impact in chemistry, pharmaceutical, and life sciences as well as in environments. We demonstrated that the obtained relationship between the partial molar volume in water and the molecular polarizability has in general a non-homogeneous character. We performed a detailed analysis of this relationship on a set of ~200 organic molecules from various chemical classes and revealed its fine well-organized structure. We found that this structure strongly depends on the chemical nature of the solutes and can be rationalized in terms of specific solute-solvent interactions. Efficiency and universality of the proposed approach was demonstrated on an external test set containing several dozens of polyfunctional and druglike molecules.

An accurate prediction of hydration free energies by combination of molecular integral equations theory with structural descriptors.

In this work, we report a novel method for the estimation of the hydration free energy of organic molecules, the structural descriptors correction (SDC) model. The method is based on a combination of the reference interaction site model (RISM) with several empirical corrections. The model requires only a small number of chemical descriptors associated with the main features of the chemical structure of solutes: excluded volume, branch, double bond, benzene ring, hydroxyl group, halogen atom, aldehyde group, ketone group, ether group, and phenol fragment. The optimum model was selected after testing of different RISM free energy expressions on a training set of 65 molecules. We show that the correction parameters of the SDC model are transferable between different chemical classes, which allows one to cover a wide range of organic solutes. The new model substantially increases the accuracy of calculated HFEs by RISM giving the standard deviation of the error for a test set of 120 organic molecules around 1.2 kcal/mol.

Towards a universal method for calculating hydration free energies: a 3D reference interaction site model with partial molar volume correction.

We report a simple universal method to systematically improve the accuracy of hydration free energies calculated using an integral equation theory of molecular liquids, the 3D reference interaction site model. A strong linear correlation is observed between the difference of the experimental and (uncorrected) calculated hydration free energies and the calculated partial molar volume for a data set of 185 neutral organic molecules from different chemical classes. By using the partial molar volume as a linear empirical correction to the calculated hydration free energy, we obtain predictions of hydration free energies in excellent agreement with experiment (R = 0.94, σ = 0.99 kcal mol (- 1) for a test set of 120 organic molecules).