Magnetic Ionic Liquids: Current Achievements and Future Perspectives with a Focus on Computational Approaches.

Magnetic ionic liquids (MILs) stand out as a remarkable subclass of ionic liquids (ILs), combining the desirable features of traditional ILs with the unique ability to respond to external magnetic fields. The incorporation of paramagnetic species into their structures endows them with additional attractive features, including thermochromic behavior and luminescence. These exceptional properties position MILs as highly promising materials for diverse applications, such as gas capture, DNA extractions, and sensing technologies. The present Review synthesizes key experimental findings, offering insights into the structural, thermal, magnetic, and optical properties across various MIL families. Special emphasis is placed on unraveling the influence of different paramagnetic species on MILs' behavior and functionality. Additionally, the Review highlights recent advancements in computational approaches applied to MIL research. By leveraging molecular dynamics (MD) simulations and density functional theory (DFT) calculations, these computational techniques have provided invaluable insights into the underlying mechanisms governing MILs' behavior, facilitating accurate property predictions. In conclusion, this Review provides a comprehensive overview of the current state of research on MILs, showcasing their special properties and potential applications while highlighting the indispensable role of computational methods in unraveling the complexities of these intriguing materials. The Review concludes with a forward-looking perspective on the future directions of research in the field of magnetic ionic liquids.

Explainable Supervised Machine Learning Model To Predict Solvation Gibbs Energy.

Many challenges persist in developing accurate computational models for predicting solvation free energy (ΔGsol). Despite recent developments in Machine Learning (ML) methodologies that outperformed traditional quantum mechanical models, several issues remain concerning explanatory insights for broad chemical predictions with an acceptable speed-accuracy trade-off. To overcome this, we present a novel supervised ML model to predict the ΔGsol for an array of solvent-solute pairs. Using two different ensemble regressor algorithms, we made fast and accurate property predictions using open-source chemical features, encoding complex electronic, structural, and surface area descriptors for every solvent and solute. By integrating molecular properties and chemical interaction features, we have analyzed individual descriptor importance and optimized our model though explanatory information form feature groups. On aqueous and organic solvent databases, ML models revealed the predictive relevance of solutes with increasing polar surface area and decreasing polarizability, yielding better results than state-of-the-art benchmark Neural Network methods (without complex quantum mechanical or molecular dynamic simulations). Both algorithms successfully outperformed previous ΔGsol predictions methods, with a maximum absolute error of 0.22 ± 0.02 kcal mol-1, further validated in an external benchmark database and with solvent hold-out tests. With these explanatory and statistical insights, they allow a thoughtful application of this method for predicting other thermodynamic properties, stressing the relevance of ML modeling for further complex computational chemistry problems.

An integrated protocol to study hydrogen abstraction reactions by atomic hydrogen in flexible molecules: application to butanol isomers.

This work presents a protocol designed to study hydrogen abstraction reactions by atomic hydrogen in molecules with multiple conformations. The protocol starts with the search and location of the conformers of the equilibrium structures using the TorsiFlex program. By a simple modification of the starting geometry of reactants, a Python script generates the input for the hydrogen abstraction transition states. Initially, the search of the stationary points (reactants and transition states) is carried out at a low-level employing firstly a preconditioned search and secondly a random search. The low-level conformers were reoptimized using a higher level electronic structure method. This information allows the evaluation of the multistructural harmonic-oscillator partition functions, which are corrected for zero-point energy anharmonicity by the hybrid degeneracy-corrected second-order vibrational perturbation theory and for torsional anharmonicity by the multistructural torsional method, as implemented in the MsTor program. The structural information of the stationary points is used by Pilgrim to evaluate the multipath canonical variational transition state theory thermal rate constants with multidimensional small-curvature corrections for tunneling. Therefore, the thermal rate constants include variational (recrossing) and tunneling effects in addition to the effect of multiple conformations on the thermal rate constants. These features grant the applicability of the method to a wide range of temperatures. The method was applied to each of the hydrogen abstraction sites of the four isomers of butanol. The methodology employed allowed us to calculate the thermal rate constants in the temperature range of 250-2500 K and to accurately fit them to analytical expressions. The variety of abstraction sites shows that the protocol is robust and that it can be employed to study hydrogen abstraction reactions in molecules containing carbon and oxygen as heavy atoms.

Correction: An integrated protocol to study hydrogen abstraction reactions by atomic hydrogen in flexible molecules: application to butanol isomers.

Correction for 'An integrated protocol to study hydrogen abstraction reactions by atomic hydrogen in flexible molecules: application to butanol isomers' by David Ferro-Costas et al., Phys. Chem. Chem. Phys., 2022, DOI: 10.1039/d1cp03928h.

First-principles-based kinetic Monte Carlo simulations of CO oxidation on catalytic Au(110) and Ag(110) surfaces.

At the core of the development of more efficient and reliable fuel cells (FCs), there are several essential chemical reactions, namely carbon monoxide (CO) oxidation. This reaction is a keystone in the cleaning of hydrogen fuel used in fuel cells due to strong poisoning by this species of the platinum catalyst used in these devices. The present work aims to provide insight regarding the activation of CO oxidation by gold or silver microfacets possessing low coordinated atoms. To achieve this, density functional theory (DFT) quantum calculations, which determined two competing reaction pathways for CO oxidation, i.e., by molecularly adsorbed oxygen, and by dissociated oxygen, are combined with first-principles kinetic Monte Carlo (1p-kMC) simulations, which employed the resulting DFT parameters in order to address the effect of temperature and partial pressures and the interplay of the elementary reaction events. The use of 1p-kMC is a step further from available works regarding the CO oxidation on gold- and silver-based catalysts for cleansing of hydrogen that is used as a fuel in FCs. Indeed, this research contributes to the conclusion that CO oxidation should preferentially occur on silver microfacets, while the obtained turnover frequencies (TOFs) reinforced such a conclusion.

Molecular dynamic study of alcohol-based deep eutectic solvents.

The applicability of deep eutectic solvents is determined by their physicochemical properties. In turn, the properties of eutectic mixtures are the result of the components' molar ratio and chemical composition. Owing to the relatively low viscosities displayed by alcohol-based deep eutectic solvents (DESs), their application in industry is more appealing. Modeling the composition-property relationships established in polyalcohol-based mixtures is crucial for both understanding and predicting their behavior. In this work, a physicochemical property-structure comparison study is made between four choline chloride polyalcohol-based DESs, namely, ethaline, propeline, propaneline, and glyceline. Physicochemical properties obtained from molecular dynamic simulations are compared to experimental data, whenever possible. The simulations cover the temperature range from 298.15 to 348.15 K. The simulated and literature experimental data are generally in good agreement for all the studied DESs. Structural properties, such as radial and spatial distribution functions, coordination numbers, hydrogen bond donor (HBD)-HBD aggregate formation, and hydrogen bonding are analyzed in detail. The higher prevalence of HBD:HBD and HBD:anion hydrogen bonds is likely to be the major reason for the relatively high density and viscosity of glyceline as well as for lower DES self-diffusions.

AKT Inhibitors: The Road Ahead to Computational Modeling-Guided Discovery.

AKT, is a serine/threonine protein kinase comprising three isoforms-namely: AKT1, AKT2 and AKT3, whose inhibitors have been recognized as promising therapeutic targets for various human disorders, especially cancer. In this work, we report a systematic evaluation of multi-target Quantitative Structure-Activity Relationship (mt-QSAR) models to probe AKT' inhibitory activity, based on different feature selection algorithms and machine learning tools. The best predictive linear and non-linear mt-QSAR models were found by the genetic algorithm-based linear discriminant analysis (GA-LDA) and gradient boosting (Xgboost) techniques, respectively, using a dataset containing 5523 inhibitors of the AKT isoforms assayed under various experimental conditions. The linear model highlighted the key structural attributes responsible for higher inhibitory activity whereas the non-linear model displayed an overall accuracy higher than 90%. Both these predictive models, generated through internal and external validation methods, were then used for screening the Asinex kinase inhibitor library to identify the most potential virtual hits as pan-AKT inhibitors. The virtual hits identified were then filtered by stepwise analyses based on reverse pharmacophore-mapping based prediction. Finally, results of molecular dynamics simulations were used to estimate the theoretical binding affinity of the selected virtual hits towards the three isoforms of enzyme AKT. Our computational findings thus provide important guidelines to facilitate the discovery of novel AKT inhibitors.

Unravelling the Interactions of Magnetic Ionic Liquids by Energy Decomposition Schemes: Towards a Transferable Polarizable Force Field.

This work aims at unravelling the interactions in magnetic ionic liquids (MILs) by applying Symmetry-Adapted Perturbation Theory (SAPT) calculations, as well as based on those to set-up a polarisable force field model for these liquids. The targeted MILs comprise two different cations, namely: 1-butyl-3-methylimidazolium ([Bmim]+) and 1-ethyl-3-methylimidazolium ([Emim]+), along with several metal halides anions such as [FeCl4]-, [FeBr4]-, [ZnCl3]- and [SnCl4]2- To begin with, DFT geometry optimisations of such MILs were performed, which in turn revealed that the metallic anions prefer to stay close to the region of the carbon atom between the nitrogen atoms in the imidazolium fragment. Then, a SAPT study was carried out to find the optimal separation of the monomers and the different contributions for their interaction energy. It was found that the main contribution to the interaction energy is the electrostatic interaction component, followed by the dispersion one in most of the cases. The SAPT results were compared with those obtained by employing the local energy decomposition scheme based on the DLPNO-CCSD(T) method, the latter showing slightly lower values for the interaction energy as well as an increase of the distance between the minima centres of mass. Finally, the calculated SAPT interaction energies were found to correlate well with the melting points experimentally measured for these MILs.

Density of Deep Eutectic Solvents: The Path Forward Cheminformatics-Driven Reliable Predictions for Mixtures.

Deep eutectic solvents (DES) are often regarded as greener sustainable alternative solvents and are currently employed in many industrial applications on a large scale. Bearing in mind the industrial importance of DES-and because the vast majority of DES has yet to be synthesized-the development of cheminformatic models and tools efficiently profiling their density becomes essential. In this work, after rigorous validation, quantitative structure-property relationship (QSPR) models were proposed for use in estimating the density of a wide variety of DES. These models were based on a modelling dataset previously employed for constructing thermodynamic models for the same endpoint. The best QSPR models were robust and sound, performing well on an external validation set (set up with recently reported experimental density data of DES). Furthermore, the results revealed structural features that could play crucial roles in ruling DES density. Then, intelligent consensus prediction was employed to develop a consensus model with improved predictive accuracy. All models were derived using publicly available tools to facilitate easy reproducibility of the proposed methodology. Future work may involve setting up reliable, interpretable cheminformatic models for other thermodynamic properties of DES and guiding the design of these solvents for applications.

Understanding the Binding Specificity of G-Protein Coupled Receptors toward G-Proteins and Arrestins: Application to the Dopamine Receptor Family.

G-Protein coupled receptors (GPCRs) are involved in a myriad of pathways key for human physiology through the formation of complexes with intracellular partners such as G-proteins and arrestins (Arrs). However, the structural and dynamical determinants of these complexes are still largely unknown. Herein, we developed a computational big-data pipeline that enables the structural characterization of GPCR complexes with no available structure. This pipeline was used to study a well-known group of catecholamine receptors, the human dopamine receptor (DXR) family and its complexes, producing novel insights into the physiological properties of these important drug targets. A detailed description of the protein interfaces of all members of the DXR family (D1R, D2R, D3R, D4R, and D5R) and the corresponding protein interfaces of their binding partners (Arrs: Arr2 and Arr3; G-proteins: Gi1, Gi2, Gi3, Go, Gob, Gq, Gslo, Gssh, Gt2, and Gz) was generated. To produce reliable structures of the DXR family in complex with either G-proteins or Arrs, we performed homology modeling using as templates the structures of the ß2-adrenergic receptor (ß2AR) bound to Gs, the rhodopsin bound to Gi, and the recently acquired neurotensin receptor-1 (NTSR1) and muscarinic 2 receptor (M2R) bound to arrestin (Arr). Among others, the work demonstrated that the three partner groups, Arrs and Gs- and Gi-proteins, are all structurally and dynamically distinct. Additionally, it was revealed the involvement of different structural motifs in G-protein selective coupling between D1- and D2-like receptors. Having constructed and analyzed 50 models involving DXR, this work represents an unprecedented large-scale analysis of GPCR-intracellular partner interface determinants. All data is available at www.moreiralab.com/resources/dxr.

Interaction of Coumarin Phytoestrogens with ERα and ERß: A Molecular Dynamics Simulation Study.

Coumarin phytoestrogens, as one of the important classes of phytoestrogens, have been proved to play an important role in various fields of human life. In this study, molecular simulation method including molecular docking and molecular dynamics methods were performed to explore the various effects between four classical coumarin phytoestrogens (coumestrol, 4-methoxycoumestrol, psoralen and isopsoralen), and estrogen receptors (ERα, ERß), respectively. The calculated results not only proved that the four coumarin phytoestrogens have weaker affinity than 17ß-estradiol to both ERα, and ERß, but also pointed out that the selective affinity for ERß is greater than ERα. In addition, the binding mode indicated that the formation of hydrogen bond and hydrophobic interaction have an important effect on the stability of the complexes. Further, the calculation and decomposition of binding free energy explored the main contribution interactions to the total free energy.

Targeting Beta-Blocker Drug-Drug Interactions with Fibrinogen Blood Plasma Protein: A Computational and Experimental Study.

In this work, one of the most prevalent polypharmacology drug-drug interaction events that occurs between two widely used beta-blocker drugs-i.e., acebutolol and propranolol-with the most abundant blood plasma fibrinogen protein was evaluated. Towards that end, molecular docking and Density Functional Theory (DFT) calculations were used as complementary tools. A fibrinogen crystallographic validation for the three best ranked binding-sites shows 100% of conformationally favored residues with total absence of restricted flexibility. From those three sites, results on both the binding-site druggability and ligand transport analysis-based free energy trajectories pointed out the most preferred biophysical environment site for drug-drug interactions. Furthermore, the total affinity for the stabilization of the drug-drug complexes was mostly influenced by steric energy contributions, based mainly on multiple hydrophobic contacts with critical residues (THR22: P and SER50: Q) in such best-ranked site. Additionally, the DFT calculations revealed that the beta-blocker drug-drug complexes have a spontaneous thermodynamic stabilization following the same affinity order obtained in the docking simulations, without covalent-bond formation between both interacting beta-blockers in the best-ranked site. Lastly, experimental ultrasound density and velocity measurements were performed and allowed us to validate and corroborate the computational obtained results.

New Workflow for QSAR Model Development from Small Data Sets: Small Dataset Curator and Small Dataset Modeler. Integration of Data Curation, Exhaustive Double Cross-Validation, and a Set of Optimal Model Selection Techniques.

Quantitative structure-activity relationship (QSAR) modeling is a well-known in silico technique with extensive applications in several major fields such as drug design, predictive toxicology, materials science, food science, etc. Handling small-sized datasets due to the lack of experimental data for specialized end points is a crucial task for the QSAR researcher. In the present study, we propose an integrated workflow/scheme capable of dealing with small dataset modeling that integrates dataset curation, "exhaustive" double cross-validation and a set of optimal model selection techniques including consensus predictions. We have developed two software tools, namely, Small Dataset Curator, version 1.0.0, and Small Dataset Modeler, version 1.0.0, to effortlessly execute the proposed workflow. These tools are freely available for download from https://dtclab.webs.com/software-tools . We have performed case studies employing seven diverse datasets to demonstrate the performance of the proposed scheme (including data curation) for small dataset QSAR modeling. The case studies also confirm the usability and stability of the developed software tools.

QSAR-Co: An Open Source Software for Developing Robust Multitasking or Multitarget Classification-Based QSAR Models.

Quantitative structure-activity relationships (QSAR) modeling is a well-known computational technique with wide applications in fields such as drug design, toxicity predictions, nanomaterials, etc. However, QSAR researchers still face certain problems to develop robust classification-based QSAR models, especially while handling response data pertaining to diverse experimental and/or theoretical conditions. In the present work, we have developed an open source standalone software "QSAR-Co" (available to download at https://sites.google.com/view/qsar-co ) to setup classification-based QSAR models that allow mining the response data coming from multiple conditions. The software comprises two modules: (1) the Model development module and (2) the Screen/Predict module. This user-friendly software provides several functionalities required for developing a robust multitasking or multitarget classification-based QSAR model using linear discriminant analysis or random forest techniques, with appropriate validation, following the principles set by the Organisation for Economic Co-operation and Development (OECD) for applying QSAR models in regulatory assessments.

CompScore: Boosting Structure-Based Virtual Screening Performance by Incorporating Docking Scoring Function Components into Consensus Scoring.

Consensus scoring has become a commonly used strategy within structure-based virtual screening (VS) workflows with improved performance compared to those based in a single scoring function. However, no research has been devoted to analyze the worth of docking scoring functions components in consensus scoring. We implemented and tested a method that incorporates docking scoring functions components into the setting of high performance VS workflows. This method uses genetic algorithms for finding the combination of scoring components that maximizes the VS enrichment for any target. Our methodology was validated using a data set including ligands and decoys for 102 targets that have been widely used in VS validation studies. Results show that our approach outperforms other methods for all targets. It also boosts the initial enrichment performance of the traditional use of whole scoring functions in consensus scoring by an average of 45%. Our methodology showed to be outstandingly predictive when challenged to rescore external (previously unseen) data. Remarkably, CompScore was able not only to retain its performance after redocking with a different software, but also proved that the enrichment obtained was not artificial. CompScore is freely available at: http://bioquimio.udla.edu.ec/compscore/ .

MitoTarget Modeling Using ANN-Classification Models Based on Fractal SEM Nano-Descriptors: Carbon Nanotubes as Mitochondrial F0F1-ATPase Inhibitors.

Recently, it has been suggested that the mitochondrial oligomycin A-sensitive F0-ATPase subunit is an uncoupling channel linked to apoptotic cell death, and as such, the toxicological inhibition of mitochondrial F0-ATP hydrolase can be an interesting mitotoxicity-based therapy under pathological conditions. In addition, carbon nanotubes (CNTs) have been shown to offer higher selectivity like mitotoxic-targeting nanoparticles. In this work, linear and nonlinear classification algorithms on structure-toxicity relationships with artificial neural network (ANN) models were set up using the fractal dimensions calculated from CNTs as a source of supramolecular chemical information. The potential ability of CNT-family members to induce mitochondrial toxicity-based inhibition of the mitochondrial H+-F0F1-ATPase from in vitro assays was predicted. The attained experimental data suggest that CNTs have a strong ability to inhibit the F0-ATPase active-binding site following the order oxidized-CNT (CNT-COOH > CNT-OH) > pristine-CNT and mimicking the oligomycin A mitotoxicity behavior. Meanwhile, the performance of the ANN models was found to be improved by including different nonlinear combinations of the calculated fractal scanning electron microscopy (SEM) nanodescriptors, leading to models with excellent internal accuracy and predictivity on external data to classify correctly CNT-mitotoxic and nonmitotoxic with specificity (Sp > 98.9%) and sensitivity (Sn > 99.0%) from ANN models compared with linear approaches (LNN) with Sp ≈ Sn > 95.5%. Finally, the present study can contribute toward the rational design of carbon nanomaterials and opens new opportunities toward mitochondrial nanotoxicology-based in silico models.

Alignment-Free Method to Predict Enzyme Classes and Subclasses.

The Enzyme Classification (EC) number is a numerical classification scheme for enzymes, established using the chemical reactions they catalyze. This classification is based on the recommendation of the Nomenclature Committee of the International Union of Biochemistry and Molecular Biology. Six enzyme classes were recognised in the first Enzyme Classification and Nomenclature List, reported by the International Union of Biochemistry in 1961. However, a new enzyme group was recently added as the six existing EC classes could not describe enzymes involved in the movement of ions or molecules across membranes. Such enzymes are now classified in the new EC class of translocases (EC 7). Several computational methods have been developed in order to predict the EC number. However, due to this new change, all such methods are now outdated and need updating. In this work, we developed a new multi-task quantitative structure-activity relationship (QSAR) method aimed at predicting all 7 EC classes and subclasses. In so doing, we developed an alignment-free model based on artificial neural networks that proved to be very successful.

Detection of simple inorganic and organic molecules over Cu-decorated circumcoronene: a combined DFT and QTAIM study.

Nowadays graphene materials have attracted a considerable attention because of their potential utilization as gas sensors, biosensors, or adsorbents. Doping or decorating the graphene surface with transition metals can significantly tune its electronic properties and chemical reactivity. Circumcoronene, being a polyaromatic hydrocarbon composed of 19 benzene rings, can be used as a model system of a tiny graphene quantum dot. The adsorption of a set of small molecules (water, hydrogen peroxide, methanol, ethanol, and oxygen) over the copper-decorated circumcoronene was theoretically investigated using density functional theory (DFT) and Bader's quantum theory of atoms in molecules (QTAIM). Following the obtained B3LYP energies, the adsorption of O2 and the chemisorption of H2O2 were found to be energetically the most favorable, with energetic outcomes of -3.6 eV and -3.7 eV, respectively. Moreover, an H2O2 molecule was decomposed during the chemisorption on the Cu atom to form a neutral Cu(OH)2 molecule. Changes in the electronic structure of the studied systems, in particular the oxidation of copper, after the adsorption were investigated within the framework of QTAIM (e.g., charges, bond critical points, and delocalization indices) and partial density of states (PDOS) analysis. The results of this study suggest the suitability of the Cu-decorated graphene materials as adsorbents and/or gas sensors in practical applications.

Influence of the anion on the properties of ionic liquid mixtures: a molecular dynamics study.

Mixing of ionic liquids provides new opportunities for their tuning, enabling the applications of ionic liquid mixtures to expand. At the same time, the genesis of the fundamental properties of ionic liquid mixtures is still poorly understood. In this study we carried out a molecular dynamics simulation of binary mixtures of 1-buthyl-3-methylimidazolium hexafluorophosphate, 1-buthyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide, and 1-buthyl-3-methylimidazolium tris(perfluoroethyl)trifluorophosphate ([C4mim][PF6] + [C4mim][NTf2], [C4mim][PF6] + [C4mim][FAP], [C4mim][FAP] + [C4mim][NTf2]) in a wide concentration range at 303.15 K and complemented it with quantum mechanical calculations. Three pure ionic liquids underwent the same kind of analysis for comparison purposes. We found that the addition of the [FAP]--anion to a mixture enhances the segregation of non-polar domains and weakens the hydrogen-bond network. The H-bonds in the studied mixtures are rather weak, as follows from QTAIM analysis, with the rarest occurrence for the [FAP]--anion. The competition of two anions in the mixtures for the most acidic hydrogen of the 1-butyl-3-methylimidazolium cation is reported. In most of the cases, the smaller anion ([PF6]- or [NTf2]-) with stronger charge concentration displaces the bigger one ([NTf2]- or [FAP]-) from the preferred coordination site. The existing nano-segregation in some mixtures notably slows down ion diffusion. Our results show that the differences in anion size, shape and nature are the main reasons for nano-segregation and the non-ideal behavior of ionic liquid mixtures.

On the thickness of the double layer in ionic liquids.

In this study, we examined the thickness of the electrical double layer (EDL) in ionic liquids using density functional theory (DFT) calculations and molecular dynamics (MD) simulations. We focused on BF4- anion adsorption from the 1-ethyl-3-methylimidazolium tetrafluoroborate (EMImBF4) ionic liquid on the Au(111) surface. At both DFT and MD levels, we evaluated the capacitance-potential dependence for the Helmholtz model of the interface. Using MD simulations, we also explored a more realistic, multilayer EDL model accounting for the ion layering. Concurrent analysis of the DFT and MD results provides a ground for thinking whether the electrical double layer in ionic liquids is one- or multi-ionic-layer thick.