Molecular Dynamics Free Energy Simulation study to Investigate Binding Pattern of Isoliquiritigenin as PPAR𝛾 Agonist

Phytochemicals are rich source of bioactive constituents and can be used as another alternative to currently used drugs for diseases like Diabetes mellitus. The potential of Isoliquiritigenin (a constituent of Pterocarpus marsupium) as PPAR𝛾 agonist was evaluated by in silico technique. Autodock results showed that Tyr327, and Tyr473 of the PPARγ forms H-bonds with Isoliquiritigenin (binding energy of -7.46 kcal/mol) and Troglitazone (known drug) showed H bond with Tyr327, Ser289, with binding energy of -11.01 kcal/mol. Isoliquiritigenin, binding energy in Extra precision (XP) was -6.74 kcal/mol while Troglitazone docking, gave binding energy in XP mode as -9.59 kcal/mol. The best Induced fit docking (IFD) score of the optimised PPARγ- Isoliquiritigenin complexes was -9.39 Kcal/mol. The important residues in IFD forming H bond were Cys 285, Arg 288, Tyr 327 and Leu 340. The post docking MM/GBSA free energy for PPARγ with Isoliquiritigenin and Troglitazone was -49.29 and -71.48 Kcal/mol respectively. Binding interaction in MD simulation and Principal Component Analysis studies revealed stable binding throughout 100 ns simulation. Post Simulation MM/PBSA free energy was calculated. The results indicated that compound possessed a negative binding free energy with -114.37KJ/mol. It was observed that van der Waals, electrostatic interactions and non-polar solvation energy negatively contributed to the total interaction energy while only polar solvation energy positively contributed to total free binding energy. The Isoliquiritigenin fulfils the criteria of drug-likeness property. Thus, study presents a systematic analysis on molecular mechanism of action of Isoliquiritigenin as PPARγ agonist in controlling Diabetes mellitus.

Group Contribution and Machine Learning Approaches to Predict Abraham Solute Parameters, Solvation Free Energy, and Solvation Enthalpy

We present a group contribution method (SoluteGC) and a machine learning model (SoluteML) to predict the Abraham solute parameters, as well as a machine learning model (DirectML) to predict solvation free energy and enthalpy at 298 K. The proposed group contribution method uses atom-centered functional groups with corrections for ring and polycyclic strain whilst the machine learning models adopt a directed message passing neural network. The solute parameters predicted from SoluteGC and SoluteML are used to calculate solvation energy and enthalpy via linear free energy relationships. Extensive data sets containing 8366 solute parameters, 20253 solvation free energies, and 6322 solvation enthalpies are compiled in this work to train the models. The three models are each evaluated on the same test sets using both random and substructure-based solute splits for solvation energy and enthalpy predictions. The results show that the DirectML model is superior to the SoluteML and SoluteGC models for both predictions and can provide accuracy comparable to that of advanced quantum chemistry methods. Yet, even though the DirectML model performs better in general, all three models are useful for various purposes. Uncertain predicted values can be identified by comparing the 3 models, and when the 3 models are combined together, they can provide even more accurate predictions than any one of them individually. Finally, we present our compiled solute parameter, solvation energy, and solvation enthalpy databases (SoluteDB, dGsolvDBx, dHsolvDB) and provide public access to our final prediction models through a simple web-based tool, software package, and source code.

Review—Reference Electrodes in Li-Ion and Next Generation Batteries: Correct Potential Assessment, Applications and Practices

This review provides an accessible analysis of the processes on reference electrodes and their applications in Li-ion and next generation batteries research. It covers fundamentals and definitions as well as specific practical applications and is intended to be comprehensible for researchers in the battery field with diverse backgrounds. It covers fundamental concepts, such as two- and three-electrodes configurations, as well as more complex quasi- or pseudo- reference electrodes. The electrode potential and its dependance on the concentration of species and nature of solvents are explained in detail and supported by relevant examples. The solvent, in particular the cation solvation energy, contribution to the electrode potential is important and a largely unknown issue in most the battery research. This effect can be as high as half a volt for the Li/Li+ couple and we provide concrete examples of the battery systems where this effect must be taken into account. With this review, we aim to provide guidelines for the use and assessment of reference electrodes in the Li-ion and next generation batteries research that are comprehensive and accessible to an audience with a diverse scientific background.

Modeling the Liquid Phase Exfoliation of Graphene in Polar and Nonpolar Solvents

Exfoliation is a promising technique to obtain graphene from graphite. The search for suitable exfoliation solvents is currently underway. The quality of the solvents used for spontaneous exfoliation is determined by a simple thermodynamic model. The model shows that the solvation energy of the organic solvents is higher for NMP (-177.37 mJ m-2) than other nonpolar solvents. It also shows that the solvation energy is correlated with sheet deformation and surface excess. Four groups of effective solvents are identified, including amine-, sulfoxide-, halogen-benzene-based solvents, in addition to cyclic structures with the oxygen atom. One can predict and screen potential solvents for spontaneous graphene exfoliation based on the reported mechanism.

Group Contribution and Machine Learning Approaches to Predict Abraham Solute Parameters, Solvation Free Energy, and Solvation Enthalpy

We present a group contribution method (SoluteGC) and a machine learning model (SoluteML) to predict the Abraham solute parameters, as well as a machine learning model (DirectML) to predict solvation free energy and enthalpy at 298 K. The proposed group contribution method uses atom-centered functional groups with corrections for ring and polycyclic strain whilst the machine learning models adopt a directed message passing neural network. The solute parameters predicted from SoluteGC and SoluteML are used to calculate solvation energy and enthalpy via linear free energy relationships. Extensive data sets containing 8366 solute parameters, 20253 solvation free energies, and 6322 solvation enthalpies are compiled in this work to train the models. The three models are each evaluated on the same test sets using both random and substructure-based solute splits for solvation energy and enthalpy predictions. The results show that the DirectML model is superior to the SoluteML and SoluteGC models for both predictions and can provide accuracy comparable to that of advanced quantum chemistry methods. Yet, even though the DirectML model performs better in general, all three models are useful for various purposes. Uncertain predicted values can be identified by comparing the 3 models, and when the 3 models are combined together, they can provide even more accurate predictions than any one of them individually. Finally, we present our compiled solute parameter, solvation energy, and solvation enthalpy databases (SoluteDB, dGsolvDBx, dHsolvDB) and provide public access to our final prediction models through a simple web-based tool, software package, and source code.

Modeling adsorption of organic compounds (OCs) by carbon nanotubes (CNTs): role of OCs and CNTs properties on the linear solvation energy relationships (LSER)

This study evaluated a comprehensive database for the adsorption of polar and nonpolar organic compounds (OCs) by carbon nanotubes (CNTs) and to use the linear solvation energy relationship (LSER) technique for developing predictive adsorption models of OCs by multi-walled carbon nanotubes (MWCNTs) and single-walled carbon nanotubes (SWCNTs). The results showed that coefficient of determinations (R2) values for all compounds are higher variability in the 200 g/mol molecular weight cutoff (74–99%). When the molecular weight cutoff of all OCs is higher than 200 g/mol, the trend of their R2 values is decreased (less than 70%). Among all adsorbate descriptor coefficients, V and B terms are the most significant descriptors (p values ≤ 0.05) in LSER equations for adsorption of low molecular weight polar and nonpolar OCs by both CNTs. Besides, KOW normalization of all Kd values did not have significant impact on the regression of the LSER model, indicating that hydrophobic interactions are not sole mechanism for the adsorption of OCs on CNTs. Lastly, SWCNTs exhibited higher polar OCs uptake than MWCNTs, which was attributed to more polar surface of SWCNTs as suggested by its high oxygen content (%10).