The COSMO and COSMO‐RS solvation models

Molecular and supramolecular adaptation by coupled stimuli

Adaptation transcends scale in both natural and artificial systems, but delineating the causative factors of this phenomenon requires urgent clarification. Herein, we unravel the molecular requirements for adaptation and establish a link to rationalize adaptive behavior on a self-assembled level. These concepts are established by analyzing a model compound exhibiting both light- and pH-responsive units, which enable the combined or independent application of different stimuli. On a molecular level, adaptation arises from coupled stimuli, as the final outcome of the system depends on their sequence of application. However, in a self-assembled state, a single stimulus suffices to induce adaptation as a result of collective molecular behavior and the reversibility of non-covalent interactions. Our findings go beyond state-of-the-art (multi)stimuli-responsive systems and allow us to draw up design guidelines for adaptive behavior both at the molecular and supramolecular levels, which are fundamental criteria for the realization of intelligent matter.

Mechanistic Principles of Hydrogen Evolution in the Membrane-Bound Hydrogenase

The membrane-bound hydrogenase (Mbh) from Pyrococcus furiosus is an archaeal member of the Complex I superfamily. It catalyzes the reduction of protons to H2 gas powered by a [NiFe] active site and transduces the free energy into proton pumping and Na+/H+ exchange across the membrane. Despite recent structural advances, the mechanistic principles of H2 catalysis and ion transport in Mbh remain elusive. Here, we probe how the redox chemistry drives the reduction of the proton to H2 and how the catalysis couples to conformational dynamics in the membrane domain of Mbh. By combining large-scale quantum chemical density functional theory (DFT) and correlated ab initio wave function methods with atomistic molecular dynamics simulations, we show that the proton transfer reactions required for the catalysis are gated by electric field effects that direct the protons by water-mediated reactions from Glu21L toward the [NiFe] site, or alternatively along the nearby His75L pathway that also becomes energetically feasible in certain reaction steps. These local proton-coupled electron transfer (PCET) reactions induce conformational changes around the active site that provide a key coupling element via conserved loop structures to the ion transport activity. We find that H2 forms in a heterolytic proton reduction step, with spin crossovers tuning the energetics along key reaction steps. On a general level, our work showcases the role of electric fields in enzyme catalysis and how these effects are employed by the [NiFe] active site of Mbh to drive PCET reactions and ion transport.

Cocrystal Prediction of Nifedipine Based on the Graph Neural Network and Molecular Electrostatic Potential Surface

Nifedipine (NIF) is a dihydropyridine calcium channel blocker primarily used to treat conditions such as hypertension and angina. However, its low solubility and low bioavailability limit its effectiveness in clinical practice. Here, we developed a cocrystal prediction model based on Graph Neural Networks (CocrystalGNN) for the screening of cocrystals with NIF. And scoring 50 coformers using CocrystalGNN. To validate the reliability of the model, we used another prediction method, Molecular Electrostatic Potential Surface (MEPS), to verify the prediction results. Subsequently, we performed a second validation using experiments. The results indicate that our model achieved high performance. Ultimately, cocrystals of NIF were successfully obtained and all cocrystals exhibited better solubility and dissolution characteristics compared to the parent drug. This study lays a solid foundation for combining virtual prediction with experimental screening to discover novel water-insoluble drug cocrystals.

pH-sensitive spontaneous decay of functionalized carbon dots in solutions

Carbon quantum dots have become attractive in various applications, such as drug delivery, biological sensing, photocatalysis, and solar cells. Among these, pH sensing via luminescence lifetime measurements of surface-functionalized carbon dots is one application currently investigated for their long lifetime and autonomous operation. In this article, we explore the theoretical connection between excitation lifetimes and the pH value of the surrounding liquid via the protonation and deprotonation of functional groups. Example calculations applied to m-phenylenediamine, phloroglucinol, and tethered disperse blue 1 are shown by applying a separation approach treating the electronic wave function of functional groups separately from the internal electronic structure of the (large) carbon dot. The bulk of the carbon dot is treated as an environment characterized by its optical spectrum that shifts the transition rates of the functional group. A simple relationship between pH, pKa, and mixed fluorescence lifetime is derived from the transition rates of the protonated and deprotonated states. pH sensitivity improves when the difference in the transition rates is greatest between protonated and deprotonated species, with the greatest sensitivity found where the pKa is close to the pH region of interest. The introduced model can directly be extended to consider multicomponent liquids and multiple protonation states.

Proposal for structure revision of pinofuranoxin A through total syntheses of stereoisomers

The relative configuration of the epoxide functionality in pinofuranoxin A (1), α-alkylidene-β-hydroxy-γ-methyl-γ-butyrolactone with trans-epoxy side chain isolated by Evidente et al. in 2021, was revised by DFT-based spectral reinvestigations and stereo-controlled synthesis. The present investigation demonstrates the difficulty of the configurational elucidation of the stereogenic centers on the conformationally flexible acyclic side-chains. Sharpless's enantioselective epoxidations and dihydroxylations were quite effective in the reinvestigations of the configurations. As our syntheses made all diastereomers available, these would be quite effective in the next structure-biological activity relationship studies.

Evaluation of chemical reactivity and polarity of imidazolium-based ionic liquids using quantum chemical calculations

CONTEXT

By using the DFT theory, the orbital energies of twenty-five ionic liquids were estimated. To comprehend their molecular stability and chemical reactivity, the orbital energy values, orbital energy gap (HOMO-LUMO energy gap), chemical hardness (η), chemical softness (S), electronegativity (χ), and electrophilicity index (ω) were computed. The interaction energies between all of the cations and anions under study were then computed and examined. Finally, using quantum chemical calculations, the molecular polarity of all 25 ionic liquids was examined using the sigma profile and sigma potential.

METHODS

Using the lowest orbital energy of the molecules, HF/6-31G* level of theory quantum chemical calculation were carried out using the MOLDEN and GAUSSIAN03 software. The B3LYP DFT approach produced a COSMO file that contained the species optimal SCD.

Lewis Acid Decorated Hexacyanodiborane(6) Dianion

First examples of diborane(6) dianions decorated with weakly coordination B(C6F5)3 (BCF) groups and SiEt3 + moieties have been synthesized demonstrating the synthetic potential of the [B2(CN)6]2- dianion. [B2{CNB(C6F5)3}6]2- (1) was isolated as potassium and tetrabutylammonium salt. 1 is a rare example for a weakly coordinating dianion and it was used for the stabilization of the carbocation [Ph3C]+ and the oxonium acid [H(OEt2)2]+. Reaction of [Ph3C]21 with HSiEt3 resulted in the silylated neutral diborane(6) [B2{CNB(C6F5)3}4(CNSiEt3)2] (2) in which two BCF groups have been selectively replaced by SiEt3 + substituents, underscoring the stability and chemical versatility of the [B2(CN)6]2- dianion. The chemical properties and physicochemical data of 1 and 2 provide insight into electronic, coordinating, and steric properties of theses novel diborane(6) compounds.

NBN- and BNB-Phenalenyls: the Yin and Yang of Heteroatom-doped π Systems

NBN- and BNB-doped phenalenyls are isoelectronic to phenalenyl anions and cations, respectively. They represent a pair of complementary molecules that have essentially identical structures but opposite properties as electron donors and acceptors. The NBN-phenalenyls 1-4 considered here were prepared from N,N'-dimethyl-1,8-diaminonaphthalene and readily available boron-containing building blocks (i. e., BH3⋅SMe2 (1), p-CF3-C6H4B(OH)2 (2), C6H5B(OH)2 (3), or MesBCl2/iPr2NEt (4)). Treatment of 1 with 4-Me2N-2,6-Me2-C6H2Li gave the corresponding NBN derivative 5. The BNB-phenalenyl 6 was synthesized from 1,8-naphthalenediyl-bridged diborane(6), PhNH2, and MesMgBr. A computational study reveals that the photoemission of 1, 4, and 5 originates from locally excited (LE) states at the NBN-phenalenyl fragments, while that of 2 is dominated by charge transfer (CT) from the NBN-phenalenyl to the p-CF3-C6H4 fragment. Depending on the dihedral angle θ between its Ph and NBN planes, compound 3 emits mainly from a less polar LE (θ >55°) or more polar CT state (θ <55°). In turn, the energetic preference for either state is governed by the polarity of the solvent used. An equimolar aggregate of the NBN- and BNB-phenalenyls 3 and 6 (in THF/H2O) shows a distinct red-shifted emission compared to that of the individual components, which originates from an intermolecular CT state.

Formation of Trideuteromethylated Artifacts of Pyrrole-Containing Natural Products

Pyrrole-containing natural products form a large group of structurally diverse compounds that occur in both terrestrial and marine organisms. In the present study the formation of trideuteromethylated artifacts of pyrrole-containing natural products was investigated, focusing on the discorhabdins. Three deuterated discorhabdins, 1, 3, and 5, were identified to be isolation procedure artifacts caused by the presence of DMSO-d6 during NMR sample preparation and handling. Three additional semisynthetic derivatives, 7-9, were made during the investigation of the mechanism of formation, which was shown to be driven by trideuteromethyl radicals in the presence of water, methanol, TFA, and traces of iron in the deuterated solvent. Generation of trideuteromethylated artifacts was also confirmed for other classes of pyrrole-containing metabolites, namely, makaluvamines, tambjamines, and dibromotryptamines, which had also been dissolved in DMSO-d6 during the structure elucidation process. Semisynthetic discorhabdins were assessed for antiproliferative activity against a panel of human tumor cell lines, and 14-trideuteromethyldiscorhabdin L (3) averaged low micromolar potency.

Stability of the protactinium(V) mono-oxo cation probed by first-principle calculations

This study explores the distinctive behavior of protactinium (Z=91) within the actinide series. In contrast to neighboring elements like uranium or plutonium, protactinium in the pentavalent state diverges by not forming the typical dioxo protactinyl moiety PaO2 + in aqueous phase. Instead, it manifests as a monooxo PaO3+ cation or a Pa5+ . Employing first-principle calculations with implicit and explicit solvation, we investigate two stoichiometrically equivalent neutral complexes: PaO(OH)2 (X)(H2 O) and Pa(OH)4 (X), where X represents various monodentate and bidentate ligands. Calculating the Gibbs free energy for the reaction PaO(OH)2 (X)(H2 O)→Pa(OH)4 (X), we find that the PaO(OH)2 (X)(H2 O) complex is stabilized with Cl- , Br- , I- , NCS- , NO3 - , and SO4 2- ligands, while it is not favored with OH- , F- , and C2 O4 2- ligands. Quantum Theory of Atoms in Molecules (QTAIM) and Natural Bond Orbital (NBO) methods reveal the Pa mono-oxo bond as a triple bond, with significant contributions from the 5f and 6d shells. Covalency of the Pa mono-oxo bond increases with certain ligands, such as Cl- , Br- , I- , NCS- , and NO3 - . These findings elucidate protactinium's unique chemical attributes and provide insights into the conditions supporting the stability of relevant complexes.

Binding Free Energy Calculation Based on the Fragment Molecular Orbital Method and Its Application in Designing Novel SHP-2 Allosteric Inhibitors

The accurate prediction of binding free energy is a major challenge in structure-based drug design. Quantum mechanics (QM)-based approaches show promising potential in predicting ligand-protein binding affinity by accurately describing the behavior and structure of electrons. However, traditional QM calculations face computational limitations, hindering their practical application in drug design. Nevertheless, the fragment molecular orbital (FMO) method has gained widespread application in drug design due to its ability to reduce computational costs and achieve efficient ab initio QM calculations. Although the FMO method has demonstrated its reliability in calculating the gas phase potential energy, the binding of proteins and ligands also involves other contributing energy terms, such as solvent effects, the 'deformation energy' of a ligand's bioactive conformations, and entropy. Particularly in cases involving ionized fragments, the calculation of solvation free energy becomes particularly crucial. We conducted an evaluation of some previously reported implicit solvent methods on the same data set to assess their potential for improving the performance of the FMO method. Herein, we develop a new QM-based binding free energy calculation method called FMOScore, which enhances the performance of the FMO method. The FMOScore method incorporates linear fitting of various terms, including gas-phase potential energy, deformation energy, and solvation free energy. Compared to other widely used traditional prediction methods such as FEP+, MM/PBSA, MM/GBSA, and Autodock vina, FMOScore showed good performance in prediction accuracies. By constructing a retrospective case study, it was observed that incorporating calculations for solvation free energy and deformation energy can further enhance the precision of FMO predictions for binding affinity. Furthermore, using FMOScore-guided lead optimization against Src homology-2-containing protein tyrosine phosphatase 2 (SHP-2), we discovered a novel and potent allosteric SHP-2 inhibitor (compound 8).

Natural deep eutectic solvents for the extraction of lentinan from shiitake mushroom: COSMO-RS screening and ANN-GA optimizing conditions

Using natural deep eutectic solvents (NDES) for green extraction of lentinan from shiitake mushroom is a high-efficiency method. However, empirical and trial-and-error methods commonly used to select suitable NDES are unconvincing and time-consuming. Conductor-like screening model for realistic solvation (COSMO-RS) is helpful for the priori design of NDES by predicting the solubility of biomolecules. In this study, 372 NDES were used to evaluate lentinan dissolution capability via COSMO-RS. The results showed that the solvent formed by carnitine (15 wt%), urea (40.8 wt%), and water (44.2 wt%) exhibited the best performance for the extraction of lentinan. In the extraction stage, an artificial neural network coupled with genetic algorithm (ANN-GA) was developed to optimize the extraction conditions and to analyze their interaction effects on lentinan content. Therefore, COSMO-RS and ANN-GA can be used as powerful tools for solvent screening and extraction process optimization, which can be extended to various bioactive substance extraction.

Building Chemical Intuition about Physicochemical Properties of C8-Per-/Polyfluoroalkyl Carboxylic Acids through Computational Means

We have predicted acid dissociation constants (pK a), octanol-water partition coefficients (K OW), and DMPC lipid membrane-water partition coefficients (K lipid-w) of 150 different eight-carbon-containing poly-/perfluoroalkyl carboxylic acids (C8-PFCAs) utilizing the COnductor-like Screening MOdel for Realistic Solvents (COSMO-RS) theory. Different trends associated with functionalization, degree of fluorination, degree of saturation, degree of chlorination, and branching are discussed on the basis of the predicted values for the partition coefficients. In general, functionalization closest to the carboxylic headgroup had the greatest impact on the value of the predicted physicochemical properties.

Predicting absolute aqueous solubility by applying a machine learning model for an artificially liquid-state as proxy for the solid-state

In this study, we use machine learning algorithms with QM-derived COSMO-RS descriptors, along with Morgan fingerprints, to predict the absolute solubility of drug-like compounds. The QM-derived descriptors account for the molecular properties of the solute, i.e., the solute-solute interactions in an artificial-liquid-state (super-cooled liquid), and the solute-solvent interactions in solution. We employ two main approaches to predict solubility: (i) a hypothetical pathway that involves melting the solute at room temperature T = T¯ ([Formula: see text]) and mixing the artificially liquid solute into the solvent ([Formula: see text]). In this approach [Formula: see text] is predicted using machine learning models, and the [Formula: see text] is obtained from COSMO-RS calculations; (ii) direct solubility prediction using machine learning algorithms. The models were trained on a large number of Bayer in-house compounds for which water solubility data is available at physiological pH of 6.5 and ambient temperature. We also evaluated our models using external datasets from a solubility challenge. Our models present great improvements compared to the absolute solubility prediction with the QSAR model for the artificial liquid state as implemented in the COSMOtherm software, for both in-house and external datasets. We are furthermore able to demonstrate the superiority of QM-derived descriptors compared to cheminformatics descriptors. We finally present low-cost alternative models using fragment-based COSMOquick calculations with only marginal reduction in the quality of predicted solubility.

Photocatalyzed [2σ + 2σ] and [2σ + 2π] Cycloadditions for the Synthesis of Bicyclo[3.1.1]heptanes and 5- or 6-Membered Carbocycles

We report the use of photocatalysis for the homolytic ring-opening of carbonyl cyclopropanes. In contrast to previous studies, our approach does not require a metal cocatalyst or a strong reductant. The carbonyl cyclopropanes can be employed for both [2σ + 2σ] and [2σ + 2π] annulation with either alkenes/alkynes or bicyclo[1.1.0]butanes, yielding cyclopent-anes/-enes and bicyclo[3.1.1]heptanes (BCHs), respectively. BCHs are promising bioisosteres for 1,2,4,5 tetra-substituted aromatic rings. Mechanistic studies, including density functional theory computation and a trapping experiment with DMPO, support a 1,3-biradical generated from cyclopropane as a key intermediate for these transformations.

Modelling solute-solvent interactions in VCD spectra analysis with the micro-solvation approach

Vibrational circular dichroism (VCD) spectroscopy has become an important part of the (stereo-)chemists' toolbox as a reliable method for the determination of absolute configurations. Being the chiroptical version of infrared spectroscopy, it has also been recognized as being very sensitive to conformational changes and intermolecular interactions. This sensitivity originates from the fact that the VCD spectra of individual conformers are often more different than their IR spectra, so that changes in conformational distributions or band positions and intensities become more pronounced. What is an advantage for studies focussing on intermolecular interactions can, however, quickly turn into a major obstacle during AC determinations: solute-solvent interactions can have a strong influence on spectral signatures and they must be accurately treated when simulating VCD and IR spectra. In this perspective, we showcase selected examples which exhibit particularly pronounced solvent effects. It is demonstrated that it is typically sufficient to model solute-solvent interactions by placing single solvent molecules near hydrogen bonding sites of the solute and subsequently use the optimized structures for spectra simulations. This micro-solvation approach works reasonably well for medium-sized, not too conformationally flexible molecules. We thus also discuss its limitations and outline the next steps that method development needs to take in order to further improve the workflows for VCD spectra predictions.

First hyperpolarizability of the di-8-ANEPPS and DR1 nonlinear optical chromophores in solution. An experimental and multi-scale theoretical chemistry study

The solvent effects on the linear and second-order nonlinear optical properties of an aminonaphtylethenylpyridinium (ANEP) dye are investigated by combining experimental and theoretical chemistry methods. On the one hand, deep near infrared (NIR) hyper-Rayleigh scattering (HRS) measurements (1840-1950 nm) are performed on solutions of di-8-ANEPPS in deuterated chloroform, dimethylformamide, and dimethylsulfoxide to determine their first hyperpolarizablity (βHRS). For the first time, these HRS experiments are carried out in the picosecond regime in the deep NIR with very moderate (≤3 mW) average input power, providing a good signal-to-noise ratio and avoiding solvent thermal effects. Moreover, the frequency dispersion of βHRS is investigated for Disperse Red 1 (DR1), a dye commonly used as HRS external reference. On the other hand, these are compared with computational chemistry results obtained by using a sequential molecular dynamics (MD) then quantum mechanics (QM) approach. The MD method allows accounting for the dynamical nature of the molecular structures. Then, the QM part is based on TDDFT/M06-2X/6-311+G* calculations using solvation models ranging from continuum to discrete ones. Measurements report a decrease of the βHRS of di-8-ANEPPS in more polar solvents and these effects are reproduced by the different solvation models. For di-8-ANEPPS and DR1, comparisons show that the use of a hybrid solvation model, combining the description of the solvent molecules around the probe by point charges with a continuum model, already achieves quasi quantitative agreement with experiment. These results are further improved by using a polarizable embedding that includes the atomic polarizabilities in the solvent description.

Discovery of a Druggable, Cryptic Pocket in SARS-CoV-2 nsp16 Using Allosteric Inhibitors

A collaborative, open-science team undertook discovery of novel small molecule inhibitors of the SARS-CoV-2 nsp16-nsp10 2'-O-methyltransferase using a high throughput screening approach with the potential to reveal new inhibition strategies. This screen yielded compound 5a, a ligand possessing an electron-deficient double bond, as an inhibitor of SARS-CoV-2 nsp16 activity. Surprisingly, X-ray crystal structures revealed that 5a covalently binds within a previously unrecognized cryptic pocket near the S-adenosylmethionine binding cleft in a manner that prevents occupation by S-adenosylmethionine. Using a multidisciplinary approach, we examined the mechanism of binding of compound 5a to the nsp16 cryptic pocket and developed 5a derivatives that inhibited nsp16 activity and murine hepatitis virus replication in rat lung epithelial cells but proved cytotoxic to cell lines canonically used to examine SARS-CoV-2 infection. Our study reveals the druggability of this newly discovered SARS-CoV-2 nsp16 cryptic pocket, provides novel tool compounds to explore the site, and suggests a new approach for discovery of nsp16 inhibition-based pan-coronavirus therapeutics through structure-guided drug design.

Multiscale Conceptual Design of a Scalable and Sustainable Process to Dissolve and Regenerate Keratin from Chicken Feathers

A multiscale strategy was used to conceptually design and economically analyze a scalable and sustainable process for dissolving and regenerating keratin from chicken feathers by using a sodium acetate-urea deep eutectic solvent as the reacting media. In this study, the recovery and recycling of the solvent were also considered. Moreover, molecular modeling of the solvent, keratin and its derivatives, property estimation of the corresponding mixtures, and simulation of the different process alternatives proposed, including the equipment sizing, estimation of energy needs, and economic analysis were presented. A quasi-planar cluster governed by H-bond interactions resulted in the most stable configuration of the deep eutectic solvent. Molecular models having molecular weights higher than 1.400 g/mol were created to represent the keratin species, where the most abundant amino acids in the feathers were included and conveniently ordered in the chain. Property estimations performed with the conductor-like screening model-real solvent succeeded in describing the main features of the interactions between the keratin derivatives and the solvents used. The process analysis performed on several alternatives showed that the process is technically and economically viable at the industrial scale, the costs being strongly dependent on the excess of both the solvent used to dissolve keratin and the water added for its regeneration. Several options to improve the process and reduce the costs are discussed.

Recent Advances in Co-Former Screening and Formation Prediction of Multicomponent Solid Forms of Low Molecular Weight Drugs

Multicomponent solid forms of low molecular weight drugs, such as co-crystals, salts, and co-amorphous systems, are a result of the combination of an active pharmaceutical ingredient (API) with a pharmaceutically acceptable co-former. These solid forms can enhance the physicochemical and pharmacokinetic properties of APIs, making them increasingly interesting and important in recent decades. Nevertheless, predicting the formation of API multicomponent solid forms in the early stages of formulation development can be challenging, as it often requires significant time and resources. To address this, empirical and computational methods have been developed to help screen for potential co-formers more efficiently and accurately, thus reducing the number of laboratory experiments needed. This review provides a comprehensive overview of current screening and prediction methods for the formation of API multicomponent solid forms, covering both crystalline states (co-crystals and salts) and amorphous forms (co-amorphous). Furthermore, it discusses recent advances and emerging trends in prediction methods, with a particular focus on artificial intelligence.

Organosolv pretreatment: an in-depth purview of mechanics of the system

The concept of biorefinery has been advancing globally and organosolv pretreatment strategy has seen an upsurge in research due to its efficiency in removing the recalcitrant lignin and dissolution of cellulose. The high-performance organosolv system uses green solvents and its reusability contributes concurrently to the biorefinery sector and sustainability. The major advantage of the current system involves the continuous removal of lignin to enhance cellulose accessibility, thereby easing the later biorefinery steps, which were immensely restricted due to the recalcitrant lignin. The current system process can be further explored and enhanced via the amalgamation of new technologies, which is still a work in progress. Thus, the current review summarizes organosolv pretreatment and the range of solvents used, along with a detailed mechanistic approach that results in efficient pretreatment of LCB. The latest developments for designing high-performance pretreatment systems, their pitfalls, and advanced assessments such as Life Cycle Assessment along with Techno-Economic Assessment have also been deliberated to allow an insight into its diverse potential applicability towards a sustainable future.

Exploring Cuneanes as Potential Benzene Isosteres and Energetic Materials: Scope and Mechanistic Investigations into Regioselective Rearrangements from Cubanes

Cuneane is a strained hydrocarbon that can be accessed via metal-catalyzed isomerization of cubane. The carbon atoms of cuneane define a polyhedron of the C2v point group with six faces─two triangular, two quadrilateral, and two pentagonal. The rigidity, strain, and unique exit vectors of the cuneane skeleton make it a potential scaffold of interest for the synthesis of functional small molecules and materials. However, the limited previous synthetic efforts toward cuneanes have focused on monosubstituted or redundantly substituted systems such as permethylated, perfluorinated, and bis(hydroxymethylated) cuneanes. Such compounds, particularly rotationally symmetric redundantly substituted cuneanes, have limited potential as building blocks for the synthesis of complex molecules. Reliable, predictable, and selective syntheses of polysubstituted cuneanes bearing more complex substitution patterns would facilitate the study of this ring system in myriad applications. Herein, we report the regioselective, AgI-catalyzed isomerization of asymmetrically 1,4-disubstituted cubanes to cuneanes. In-depth DFT calculations provide a charge-controlled regioselectivity model, and direct dynamics simulations indicate that the nonclassical carbocation invoked is short-lived and dynamic effects augment the charge model.

Pseudo-Enantiomeric Paraphaeolactones and Their Biosynthetic Derivatives from Paraphaeosphaeria sp. KT4192: A Proposition of a Favorskii Rearrangement for Their Biosynthesis

Paraphaeolactones A1, A2, B1, and B2 (1-4, respectively), known arthropsadiol D (5), massariphenone (6) and its positional isomer 7, and massarilactones E (8) and G (9) were isolated from the culture broth of Paraphaeosphaeria sp. KT4192. Although the structural resemblance between 1 and 2 implies that these comprised a diastereomeric pair at the C-2 stereogenic center, electronic circular dichroism (ECD) spectral analyses revealed that they were pseudo-enantiomers possessing the common (2R)-configuration. Paraphaeolactones B1 and B2 (3 and 4) were the derivatives of 2, which equipped the 3-(1-hydroxy-2-oxopropyl)-4-methylcatechol moiety via an acetal bond at C-10. The relative configurations of their acetal carbons were elucidated by NOE experiments, and those of C-8' were deduced independently by ECD spectral analysis. The present study disclosed that 1-5, 8, and 9 contain a methylcyclohexene substructure with the same absolute configuration. This prompted us to reinvestigate the absolute configurations of known structurally related fungal metabolites, allowing us to conclude that the methylcyclohexene moieties of these natural products have the same absolute configuration despite the variety of configurations of other stereogenic centers. The plausible biosynthetic routes for 1-9 are discussed on the basis of the above conclusion. We propose a Favorskii rearrangement as the key transformation for biosyntheses of 1-4.

Constrained Nuclear-Electronic Orbital Density Functional Theory with a Dielectric Continuum Solvent Model

Solvent effects are crucial for simulating chemical and biological processes in solutions. The continuum solvation model is widely used for incorporating solvent effects with different levels of theoretical descriptions of solutes. For solutes and solutions containing hydrogen atoms, nuclear quantum effects can also be nonnegligible for reliable simulations. In this work, we couple our recently developed constrained nuclear-electronic orbital density functional theory with a dielectric continuum solvation model to cover nuclear quantum effects and solvent effects simultaneously. This approach is applied to the formate ion, where an anomalous solvatochromic shift in C-H stretch frequency was reported in experiments. By using this new approach to account for nuclear quantum effects and solvent effects, we show that the vibrational frequency of the C-H stretch and the solvatochromic shift are accurately described.

Solv: An Alternative Continuum Model Implementation Based on Fixed Atomic Charges, Scaled Particle Theory, and the Atom-Atom Potential Method

An alternative continuum model implementation is reported. The electrostatic contribution to the solvation Gibbs free energy utilizes the noniterative conductor-like screening model of Vyboishchikov and Voityuk (DOI: 10.1002/jcc.26531) based on the fixed partial atomic charges. The nonelectrostatic solute-solvent dispersion-repulsion energy is calculated through the Caillet-Claverie atom-atom potential method employing the grid-based approach. The nonelectrostatic cavitation energy is computed within the scaled particle theory (SPT) formalism with the solute hard-sphere radius obtained via the Pierotti-Claverie (PC) scheme, from the solute molecular surface (SPT-S) or volume (SPT-V). The solvent hard-sphere radius is derived through the fitting to the experimental total solvation free energies of 2530 neutral species in 92 solvents. Application of the model to reproduce both the absolute and relative (reaction net) solvation free energies indicates that the SPT-V approach based on the CM5 charges is the best performer. The method is suggested for the solvation free energy calculation in the nonaqueous solvents.

COSMO-RS blind prediction of distribution coefficients and aqueous pKa values from the SAMPL8 challenge

The SAMPL8 blind prediction challenge, which addresses the acid/base dissociation constants (pKa) and the distribution coefficients (logD), was addressed by the Conductor like Screening Model for Realistic Solvation (COSMO-RS). Using the COSMOtherm implementation of COSMO-RS together with a rigorous conformational sampling, yielded logD predictions with a root mean square deviation (RMSD) of 1.36 log units over all 11 compounds and seven bi-phasic systems of the data set, which was the most accurate of all contest submissions (logD).For the SAMPL8 pKa competition, participants were asked to report the standard state free energies of all microstates, which were then used to calculate the macroscopic pKa. We have used COSMO-RS based linear free energy fit models to calculate the requested energies. The assignment of the calculated and experimental pKa values was made on the basis of the popular transitions, i.e. the transition hat was predicted by the majority of the submissions. With this assignment and a model that covers both, pKa and base pKa, we achieved an RMSD of 3.44 log units (18 pKa values of 14 molecules), which is the second place of the six ranked submissions. By changing to an assignment that is based on the experimental transition curves, the RMSD reduces to 1.65. In addition to the ranked contribution, we submitted two more data sets, one for the standard pKa model and one or the standard base pKa model of COSMOtherm. Using the experiment based assignment with the predictions of the two sets we received a RMSD of 1.42 log units (25 pKa values of 20 molecules). The deviation mainly arises from a single outlier compound, the omission of which leads to an RMSD of 0.89 log units.

Everything You Wanted to Know about Deep Eutectic Solvents but Were Afraid to Be Told

Are deep eutectic solvents (DESs) a promising alternative to conventional solvents? Perhaps, but their development is hindered by a plethora of misconceptions. These are carefully analyzed here, beginning with the very meaning of DESs, which has strayed far beyond its original scope of eutectic mixtures of Lewis or Brønsted acids and bases. Instead, a definition that is grounded on thermodynamic principles and distinguishes between eutectic and deep eutectic is encouraged, and the types of precursors that can be used to prepare DESs are reviewed. Landmark works surrounding the sustainability, stability, toxicity, and biodegradability of these solvents are also discussed, revealing piling evidence that numerous DESs reported thus far, particularly those that are choline based, lack sufficient sustainability-related traits to be considered green solvents. Finally, emerging DES applications are reviewed, emphasizing their most remarkable feature: the ability to liquefy a solid compound with a target property, allowing its use as a liquid solvent.

Chemical Transformations of 2D Kaolinic Clay Mineral Surfaces from Sulfuric Acid Exposure

A combined experimental and computational approach is used to investigate the chemical transformations of kaolinite and metakaolin surfaces when exposed to sulfuric acid. These clay minerals are hydrated ternary metal oxides and are shown to be susceptible to degradation by loss of Al as the water-soluble salt Al₂(SO₄)₃, due to interactions between H₂SO₄ and aluminum cations. This degradation process results in a silica-rich interfacial layer on the surfaces of the aluminosilicates, most prominently observed in metakaolin exposed to pH environments of less than 4. Our observations are supported by XPS, ATR-FTIR, and XRD experiments. Concurrently, DFT methodologies are used to probe the interactions between the clay mineral surfaces and H₂SO₄ as well as other sulfur-containing adsorbates. An analysis performed using a DFT + thermodynamics model shows that the surface transformation processes that lead to the loss of Al and SO₄ from metakaolin are favorable at pH below 4; however, such transformations are not favorable for kaolinite, a result that agrees with our experimental efforts. The data obtained from both experimental techniques and computational studies support that the dehydrated surface of metakaolin interacts more strongly with sulfuric acid and provide atomistic insight into the acid-induced transformations of these mineral surfaces.

Machine Learning for Fast, Quantum Mechanics-Based Approximation of Drug Lipophilicity

Lipophilicity, as measured by the partition coefficient between octanol and water (log P), is a key parameter in early drug discovery research. However, measuring log P experimentally is difficult for specific compounds and log P ranges. The resulting lack of reliable experimental data impedes development of accurate in silico models for such compounds. In certain discovery projects at Novartis focused on such compounds, a quantum mechanics (QM)-based tool for log P estimation has emerged as a valuable supplement to experimental measurements and as a preferred alternative to existing empirical models. However, this QM-based approach incurs a substantial computational cost, limiting its applicability to small series and prohibiting quick, interactive ideation. This work explores a set of machine learning models (Random Forest, Lasso, XGBoost, Chemprop, and Chemprop3D) to learn calculated log P values on both a public data set and an in-house data set to obtain a computationally affordable, QM-based estimation of drug lipophilicity. The message-passing neural network model Chemprop emerged as the best performing model with mean absolute errors of 0.44 and 0.34 log units for scaffold split test sets of the public and in-house data sets, respectively. Analysis of learning curves suggests that a further decrease in the test set error can be achieved by increasing the training set size. While models directly trained on experimental data perform better at approximating experimentally determined log P values than models trained on calculated values, we discuss the potential advantages of using calculated log P values going beyond the limits of experimental quantitation. We analyze the impact of the data set splitting strategy and gain insights into model failure modes. Potential use cases for the presented models include pre-screening of large compound collections and prioritization of compounds for full QM calculations.

Predicting Solvent Effects on SN2 Reaction Rates: Comparison of QM/MM, Implicit, and MM Explicit Solvent Models

Solvents are one of the key variables in the optimization of a synthesis yield or properties of a synthesis product. In this paper, contemporary solvent models are applied to predict the rates of S_N2 reactions in a range of aqueous and non-aqueous solvents. High-level CCSD(T)/CBS//M06-2X/6-31+G(d) gas phase energies were combined with solvation free energies from SMD, SM12, and ADF-COSMO-RS continuum solvent models, as well as molecular mechanics (MM) explicit solvent models with different atomic charge schemes to predict the rate constants of three S_N2 reactions in eight protic and aprotic solvents. It is revealed that the prediction of rate constants in organic solvents is not necessarily less challenging than in water and popular solvent models struggle to predict their rate constants to within 3 log units of experimental values. Among the continuum solvent models, the ADF-COSMO-RS model performed the best in predicting absolute rate contants while the SM12 model was best at predicting relative rate constants with an average accuracy of about 1.5 and 0.8 log units, respectively. The use of computationally more demanding MM explicit solvent models did not translate to improvements in absolute rate constants but was quite effective at predicting relative rate constants due to systematic error cancellation. Free energy barriers obtained from umbrella sampling with explicit solvent QM/MM simulations led to excellent agreement with experimental values, provided that a validated level of theory is used to treat the QM region.

Quantitative assessment of the contribution of soil organic matter functional groups and heteroatoms to PAHs adsorption based on the COSMO-RS model

Soil organic matter (SOM) is considered as a pivotal factor influencing the adsorption of pollutants. However, few prior quantitative investigations of the SOM functional group distribution to the contaminants' fate have been conducted. In this paper, the SOM cluster method based on COSMO-RS theory has been conducted to illustrate the chemical composition variables of SOM that affect the polycyclic aromatic hydrocarbons (PAHs) fate in quantitative terms. In the theoretical simulations, the contributions of carbonyl, carboxyl, aromatic, oxyalkyl and aliphatic groups in SOM to phenanthrene (Phe) and pyrene (Pyr) adsorption are evaluated by calculating the partition coefficients (LogP). The results show that the increase in oxyalkyl content leads to a decrease in LogP. Inversely, carbonyl and carboxyl groups of SOMs positively associated with Phe adsorption. The changes in aromatic and alkyl components have a similar magnitude of influence on LogP. Moreover, the effect of non-carbon-based functional groups in SOM on the Phe partitioning has been examined for the first time. The increase of sulfur and nitrogen content in SOM hinder Phe adsorption, while the rise of phosphorus content promotes the adsorption. In soil adsorption experiments, four natural soils, characterized by X-ray photoelectron spectroscopy (XPS) and Diffuse reflectance infrared Fourier transform (DRIFT), are selected to verify the influence of SOM functional group distribution. Comparing the experimental SOM-water partition coefficient (LogK_oc) with the simulation predicted LogP suggests that the COSMO-RS based SOM cluster method can predict PAHs adsorption ability in SOM.

Understanding the Interaction of Gluconamides and Gluconates with Amino Acids in Hair Care

A hair care mixture formed from a gluconamide derivative and 3-hydroxypropyl ammonium gluconate is known to strengthen hair fibers; however, the mechanism by which the mixture affects hair is unknown. To give insight into the aggregation of the target gluconamide and potential interactions between the gluconate-derived mixture and hair fibers, a range of systems were characterized by X-ray crystallography namely two polymorphic forms of the target gluconamide and three salts of 3-hydroxypropylammonium with sulfuric acid, methane sulfonic acid, and oxalic acid. The gluconamide proves to aggregate and becomes a supramolecular gelator in aniline and benzyl alcohol solution. The resulting gels were characterized by rheology, scanning electron microscopy, proton nuclear magnetic resonance, Fourier transform infrared spectroscopy, and powder X-ray diffraction.

Predicting the Surface Tension of Deep Eutectic Solvents Using Artificial Neural Networks

Studies on deep eutectic solvents (DESs), a new class of "green" solvents, are attracting increasing attention from researchers, as evidenced by the rapidly growing number of publications in the literature. One of the main advantages of DESs is that they are tailor-made solvents, and therefore, the number of potential DESs is extremely large. It is essential to have computational methods capable of predicting the physicochemical properties of DESs, which are needed in many industrial applications and research. Surface tension is one of the most important properties required in many applications. In this work, we report a relatively generalized artificial neural network (ANN) for predicting the surface tension of DESs. The database used can be considered comprehensive because it contains 1571 data points from 133 different DES mixtures in 520 compositions prepared from 18 ions and 63 hydrogen bond donors in a temperature range of 277-425 K. The ANN model uses molecular parameter inputs derived from the conductor-like screening model for real solvents (S _σ-profiles). The training and testing results show that the best performing ANN architecture consisted of two hidden layers with 15 neurons each (9-15-15-1). The proposed ANN was excellent in predicting the surface tension of DESs, as R ² values of 0.986 and 0.977 were obtained for training and testing, respectively, with an overall average absolute relative deviation of 2.20%. The proposed models represent an initiative to promote the development of robust models capable of predicting the properties of DESs based only on molecular parameters, leading to savings in investigation time and resources.

Direct Electrochemical CO2 Capture Using Substituted Anthraquinones in Homogeneous Solutions: A Joint Experimental and Theoretical Study

Electrochemical capture of carbon dioxide (CO₂) using organic quinones is a promising and intensively studied alternative to the industrially established scrubbing processes. While recent studies focused only on the influence of substituents having a simple mesomeric or nucleophilicity effect, we have systematically selected six anthraquinone (AQ) derivatives (X-AQ) with amino and hydroxy substituents in order to thoroughly study the influence thereof on the properties of electrochemical CO₂ capture. Experimental data from cyclic voltammetry (CV) and UV-Vis spectroelectrochemistry of solutions in acetonitrile were analyzed and compared with innovative density functional tight binding computational results. Our experimental and theoretical results provide a coherent explanation of the influence of CO₂ on the CV data in terms of weak and strong binding nomenclature of the dianions. In addition to this terminology, we have identified the dihydroxy substituted AQ as a new class of molecules forming rather unstable [X-AQ-(CO₂) _n ]^2- adducts. In contrast to the commonly used dianion consideration, the results presented herein reveal opposite trends in stability for the X-AQ-CO₂ ^•- radical species for the first time. To the best of our knowledge, this study presents theoretically calculated UV-Vis spectra for the various CO₂-AQ reduction products for the first time, enabling a detailed decomposition of the spectroelectrochemical data. Thus, this work provides an extension of the existing classification with proof of the existence of X-AQ-CO₂ species, which will be the basis of future studies focusing on improved materials for electrochemical CO₂ capture.

Constructing and interpreting volcano plots and activity maps to navigate homogeneous catalyst landscapes

Volcano plots and activity maps are powerful tools for studying homogeneous catalysis. Once constructed, they can be used to estimate and predict the performance of a catalyst from one or more descriptor variables. The relevance and utility of these tools has been demonstrated in several areas of catalysis, with recent applications to homogeneous catalysts having been pioneered by our research group. Both volcano plots and activity maps are built from linear free energy scaling relationships that connect the value of a descriptor variable(s) with the relative energies of other catalytic cycle intermediates/transition states. These relationships must be both constructed and postprocessed appropriately to obtain the resulting plots/maps; this process requires careful execution to obtain meaningful results. In this protocol, we provide a step-by-step guide to building volcano plots and activity maps using curated reaction profile data. The reaction profile data are obtained using density functional theory computations to model the catalytic cycle. In addition, we provide volcanic, a Python code that automates the steps of the process following data acquisition. Unlike the computation of individual reaction energy profiles, our tools lead to a holistic view of homogeneous catalyst performance that can be broadly applied for both explanatory and screening purposes.

Exhaustive Mapping of the Conformational Space of Natural Dipeptides by the DFT-D3//COSMO-RS Method

We extensively mapped energy landscapes and conformations of 22 (including three His protonation states) proteinogenic α-amino acids in trans configuration and the corresponding 484 (22²) dipeptides. To mimic the environment in a protein chain, the N- and C-termini of the studied systems were capped with acetyl and N-methylamide groups, respectively. We systematically varied the main chain dihedral angles (ϕ, ψ) by 40° steps and all side chain angles by 90° or 120° steps. We optimized the molecular geometries with the GFN2-xTB semiempirical (SQM) method and performed single point density functional theory calculations at the BP86-D3/DGauss-DZVP//COSMO-RS level in water, 1-octanol, N,N-dimethylformamide, and n-hexane. For each restrained (nonequilibrium) structure, we also calculated energy gradients (in water) and natural atomic charges. The exhaustive and unprecedented QM-based sampling enabled us to construct Ramachandran plots of quantum mechanical (QM(BP86-D3)//COSMO-RS) energies calculated on SQM structures, for all 506 (484 dipeptides and 22 amino acids) studied systems. We showed how the character of an amino acid side chain influences the conformational space of single amino acids and dipeptides. With clustering techniques, we were able to identify unique minima of amino acids and dipeptides (i.e., minima on the GFN2-xTB potential energy surfaces) and analyze the distribution of their BP86-D3//COSMO-RS conformational energies in all four solvents. We also derived an empirical formula for the number of unique minima based on the overall number of rotatable bonds within each peptide. The final peptide conformer data set (PeptideCs) comprises over 400 million structures, all of them annotated with QM(BP86-D3)//COSMO-RS energies. Thanks to its completeness and unbiased nature, the PeptideCs can serve, inter alia, as a data set for the validation of new methods for predicting the energy landscapes of protein structures. This data set may also prove to be useful in the development and reparameterization of biomolecular force fields. The data set is deposited at Figshare (10.25452/figshare.plus.19607172) and can be accessed using a simple web interface at http://peptidecs.uochb.cas.cz.

The Role of Packing, Dispersion, Electrostatics, and Solvation in High-Affinity Complexes of Cucurbit[n]urils with Uncharged Polar Guests

The rationalization of non‐covalent binding trends is both of fundamental interest and provides new design concepts for biomimetic molecular systems. Cucurbit[n]urils (CBn) are known for a long time as the strongest synthetic binders for a wide range of (bio)organic compounds in water. However, their host‐guest binding mechanism remains ambiguous despite their symmetric and simple macrocyclic structure and the wealth of literature reports. We herein report experimental thermodynamic binding parameters (ΔG, ΔH, TΔS) for CB7 and CB8 with a set of hydroxylated adamantanes, di‐, and triamantanes as uncharged, rigid, and spherical/ellipsoidal guests. Binding geometries and binding energy decomposition were obtained from high‐level theory computations. This study reveals that neither London dispersion interactions, nor electronic energies or entropic factors are decisive, selectivity‐controlling factors for CBn complexes. In contrast, peculiar host‐related solvation effects were identified as the major factor for rationalizing the unique behavior and record‐affinity characteristics of cucurbit[n]urils.

Cellulose polymers with β-amino ester pendant group: design, synthesis, molecular docking and application in adsorption of toxic metals from wastewater

BACKGROUND

Cellulose polymers with multidentate chelating functionalities that have high efficiency for toxic metal ions present in water were designed, synthesized, and analyzed. The synthesis was carried out by reacting microcrystalline cellulose extracted from the solid waste of the olive industry with tert-Butyl acetoacetate (Cell-AA), produced cellulose with β-ketoester functionality was then reacted with aniline and the amino acid glycine to produce Cell-β-AN and Cell-β-GL, respectively.

RESULTS

The adsorption efficiency of the three polymers toward Pb(II) and various toxic metal ions present in sewage was evaluated as a function of adsorbent dose, time, temperature, pH value, and initial ion concentration to determine optimum adsorption conditions. The three polymers showed excellent efficiency toward about 20 metal ions present in a sewage sample collected from the sewer. The adsorption process follows the Langmuir adsorption isotherm model with a second-order of adsorption rate, the calculated qe values (2.675, 15.252, 20.856 mg/g) were close to the experimental qe values (2.133, 13.91, 18.786 mg/g) for the three polymers Cell-AA, Cell-β-AG and Cell-β-AN, respectively. Molecular Dynamic (MD) and Monte Carlo (MC) simulations were performed on the three polymers complexed with Pb(II).

CONCLUSION

The waste material of the olive industry was used as a precursor for making the target cellulose polymers with β-Amino Ester Pendant Group. The polymer was characterized by SEM, proton NMR, TGA, and FT-IR spectroscopy. The efficacy of adsorption was quantitative for metal ions present in a real sample of wastewater and the efficiency didn't drop even after 7 cycles of use. The results indicate the existence of strong complexation. The thermodynamic study results showed a spontaneous bonding between of Pb(II) and the polymers pendant groups expressed by the negative value of the Gibbs free energy.

SN2 versus E2 reactions in a complex microsolvated environment: theoretical analysis of the equilibrium and activation steps of a nucleophilic fluorination

The reactivity of the fluoride ion towards alkyl halides is highly dependent on the solvating environment. In polar aprotic solvents with large counter-ions is highly reactive and produces substantial E2 product, whereas in polar protic solvents leads to slow kinetics and high selectivity for S_N2 reactions. The use of a more complex environment with stoichiometric addition of tert-butanol to acetonitrile solvent is able to module the reactivity and selectivity of tetrabutylammonium fluoride (TBAF). In the present work, we have performed a detailed theoretical analysis of this complex reaction system by density functional theory, continuum solvation model, and including explicit tert-butanol molecules. A kinetic model based on the free energy profile was also used to predict the reactivity and selectivity. The results indicated that the TBAF(tert-butanol) complex plays the key role to increase the S_N2 selectivity, whereas higher aggregates are not relevant. The E2 product is formed exclusively via free TBAF, because the solvating tert-butanol in the TBAF(tert-butanol) complex inhibits the E2 pathway. Our analysis suggests that diols or tetraols could produce an improved selectivity.