Robust and Efficient Implicit Solvation Model for Fast Semiempirical Methods

Unraveling C-Selective Ring-Opening of Phosphiranes with Carboxylic Acids and Other Nucleophiles: A Mechanistically-Driven Approach

Phosphiranes are weak Lewis bases reacting with only a limited number of electrophiles to produce the corresponding phosphiranium ions. These salts are recognized for their propensity to undergo reactions with oxygen pronucleophiles at the phosphorus site, leading to the formation of phosphine oxide adducts. Building on a thorough mechanistic understanding, we have developed an unprecedented approach that enables the selective reaction of carboxylic acids, and other nucleophiles, at the carbon site of phosphiranes. This method involves the photochemical generation of highly reactive carbenes, which react with 1-mesitylphosphirane to yield ylides. The latter undergoes a stepwise reaction with carboxylic acids, resulting in the production of the desired phosphines. In addition to DFT calculations, we have successfully isolated and fully characterized the key intermediates involved in the reaction.

Fine-tuning molecular mechanics force fields to experimental free energy measurements

Alchemical free energy methods using molecular mechanics (MM) force fields are essential tools for predicting thermodynamic properties of small molecules, especially via free energy calculations that can estimate quantities relevant for drug discovery such as affinities, selectivities, the impact of target mutations, and ADMET properties. While traditional MM forcefields rely on hand-crafted, discrete atom types and parameters, modern approaches based on graph neural networks (GNNs) learn continuous embedding vectors that represent chemical environments from which MM parameters can be generated. Excitingly, GNN parameterization approaches provide a fully end-to-end differentiable model that offers the possibility of systematically improving these models using experimental data. In this study, we treat a pretrained GNN force field-here, espaloma-0.3.2-as a foundation simulation model and fine-tune its charge model using limited quantities of experimental hydration free energy data, with the goal of assessing the degree to which this can systematically improve the prediction of other related free energies. We demonstrate that a highly efficient "one-shot fine-tuning" method using an exponential (Zwanzig) reweighting free energy estimator can improve prediction accuracy without the need to resimulate molecular configurations. To achieve this "one-shot" improvement, we demonstrate the importance of using effective sample size (ESS) regularization strategies to retain good overlap between initial and fine-tuned force fields. Moreover, we show that leveraging low-rank projections of embedding vectors can achieve comparable accuracy improvements as higher-dimensional approaches in a variety of data-size regimes. Our results demonstrate that linearly-perturbative fine-tuning of foundation model electrostatic parameters to limited experimental data offers a cost-effective strategy that achieves state-of-the-art performance in predicting hydration free energies on the FreeSolv dataset.

pKa prediction in non-aqueous solvents

Acid dissociation constants (p K a $$ \mathrm{p}{K}_{\mathrm{a}} $$ ) are widely measured and studied, most typically in water. Comparatively few datasets and models for non-aqueousp K a $$ \mathrm{p}{K}_{\mathrm{a}} $$ values exist. In this work, we demonstrate how thep K a $$ \mathrm{p}{K}_{\mathrm{a}} $$ in one solvent can be accurately determined using reference data in another solvent, corrected by solvation energy calculations from the COSMO-RS method. We benchmark this approach in 10 different solvents, and find thatp K a $$ \mathrm{p}{K}_{\mathrm{a}} $$ values calculated in six solvents deviate from experimental data on average by less than 1p K a $$ \mathrm{p}{K}_{\mathrm{a}} $$ unit. We observe comparable performance on a more diverse test set including amino acids and drug molecules, with higher error for large molecules. The model performance in four other solvents is worse, with one MAE exceeding 3p K a $$ \mathrm{p}{K}_{\mathrm{a}} $$ units; we discuss how such errors arise due to both model error and inconsistency in obtaining experimental data. Finally, we demonstrate how this technique can be used to estimate the proton transfer energy between different solvents, and use this to report a value of the proton's solvation energy in formamide, a quantity that does not have a consensus value in literature.

A Combined Experimental and Computational Exploration of Heteroleptic cis-Pd2L2L'2 Coordination Cages through Geometric Complementarity

Heteroleptic (mixed-ligand) coordination cages are of interest as host systems with more structurally and functionally complex cavities than homoleptic architectures. The design of heteroleptic cages, however, is far from trivial. In this work, we experimentally probed the self-assembly of Pd(II) ions with binary ligand combinations in a combinatorial fashion to search for new cis-Pd2L2L'2 heteroleptic cages. A hierarchy of computational analyses was then applied to these systems with the aim of elucidating key factors for rationalising self-assembly outcomes. Simple and inexpensive geometric analyses were shown to be effective in identifying complementary ligand pairs. Preliminary results demonstrated the viability of relatively rapid semi-empirical calculations for predicting the topology of thermodynamically favoured assemblies with rigid ligands, whilst more flexible systems proved challenging. Stemming from this, key challenges were identified for future work developing effective computational forecasting tools for self-assembled metallo-supramolecular systems.

In(III) pyridinecarboxylate complexes: Composition, solution equilibria estimation, bioevaluation and interactions with HSA

Two In(III) - pyridinecarboxylates ([In(Pic)2(NO3)(H2O)] (InPic; HPic = picolinic acid), [In(HDpic)(Dpic)(H2O)2]·5H2O (InDpic; H2Dpic = dipicolinic acid), have been synthesized by one-step procedure. The complexes composition was confirmed by physicochemical analyses and X-ray diffraction confirmed molecular structure of both complexes. Moreover, complex species speciation was described in both systems by potentiometry and 1H NMR spectroscopy and mononuclear complex species were determined; [In(Pic)]2+ (logβ011 = 6.94(4)), [In(Pic)2]+ (logβ021 = 11.98(9)), [In(Dpic)]+ (logβ011 = 10.42(6)), [In(Dpic)2]- (logβ021 = 17.58(7)) and [In(Dpic)2(OH)]2- (logβ-121 = 10.18(6)). To confirm the complexes stability in 1 % DMSO, 1H NMR spectra were measured (immediately after dissolution up to 96 h). Antimicrobial and anticancer assays indicate a more significant sensitivity of S. aureus bacteria and MDA-MB-231 cancer cells to the InPic complex (IC50 = 25 and 340.7 μM) than to the InDpic (IC50 = 50 and 975.4 μM). The interaction and binding mechanism of picolinic/dipicolinic acid and their indium(III) complexes with HSA (human serum albumin) were studied using fluorescence and CD spectroscopy. The results confirmed that the studied compounds had bound successfully to HSA, and the binding parameters and constants (KSV, Kq, Kb) were calculated together with the number of binding sites. The binding forces were identified based on calculated thermodynamic parameters (ΔG, ΔH, ΔS). Synchronous spectra were used to study the microenvironment of Tyr and Trp residues and displacement assays revealed that site I was the preferred binding site. After binding, conformational changes were found to have occurred in the HSA molecule and the % α-helical content had decreased.

Predicting solvation free energies with an implicit solvent machine learning potential

Machine learning (ML) potentials are a powerful tool in molecular modeling, enabling ab initio accuracy for comparably small computational costs. Nevertheless, all-atom simulations employing best-performing graph neural network architectures are still too expensive for applications requiring extensive sampling, such as free energy computations. Implicit solvent models could provide the necessary speed-up due to reduced degrees of freedom and faster dynamics. Here, we introduce a Solvation Free Energy Path Reweighting (ReSolv) framework to parameterize an implicit solvent ML potential for small organic molecules that accurately predicts the hydration free energy, an essential parameter in drug design and pollutant modeling. Learning on a combination of experimental hydration free energy data and ab initio data of molecules in vacuum, ReSolv bypasses the need for intractable ab initio data of molecules in an explicit bulk solvent and does not have to resort to less accurate data-generating models. On the FreeSolv dataset, ReSolv achieves a mean absolute error close to average experimental uncertainty, significantly outperforming standard explicit solvent force fields. Compared to the explicit solvent ML potential, ReSolv offers a computational speedup of four orders of magnitude and attains closer agreement with experiments. The presented framework paves the way for deep molecular models that are more accurate yet computationally more cost-effective than classical atomistic models.

Au-Catalyzed 5C Reaction of Type II Diene-Ynenes toward Dihydrosemibullvalenes: Reaction Development and Mechanistic Study

We report an unexpected gold-catalyzed 5C reaction of type II diene-ynenes to synthesize dihydrosemibullvalenes, which are potential bioisosteres for drug discovery. This 5C reaction occurs through a sequence of elementary reactions of cyclopropanation/Cope rearrangement/carbon shift/cyclopropanation/C-H insertion (shortened here as the 5C reaction), supported by quantum chemistry calculations. Mechanistic studies have also been applied to answer why type-II diene-ynenes cannot access seven-membered carbocycles-embedded bridged molecules under the gold catalysis, finding that the chair-like Cope rearrangement transition state (not the traditional boat-like transition state) is the key to the change of regiochemistry.

A Benchmark Study of DFT-Computed p-Block Element Lewis Pair Formation Enthalpies Against Experimental Calorimetric Data

The quantification of Lewis acidity is of fundamental and applied importance in chemistry. While the computed fluoride ion affinity (FIA) is the most widely accepted thermodynamic metric, only sparse experimental values exist. Accordingly, a benchmark of methods for computing Lewis pair formation enthalpies, also with a broader set of Lewis bases against experimental data, is missing. Herein, we evaluate different density functionals against a set of 112 experimentally determined Lewis acid/base binding enthalpies and gauge influences such as solvation correction in structure optimization. From that, we can recommend r2SCAN-3c for robust quantification of this omnipresent interaction.

Structure and intermolecular interactions in ionic liquid 1-ethyl-3-methylimidazolium bromide and its aqueous solutions investigated by vibrational spectroscopy and quantum chemical computations

The recently developed efficient protocol to explicit quantum mechanical modeling of structure and IR spectra of liquids and solutions (S. A. Katsyuba, S. Spicher, T. P. Gerasimova, S. Grimme, J. Phys. Chem. B 2020, 124, 6664) is applied to ionic liquid (IL) 1-ethyl-3-methylimidazolium bromide (EmimBr), its C2-deuterated analog [Emim-d]Br and its aqueous solutions. It is shown that the solvation strongly modifies frequencies and IR intensities of the CH/CD stretching vibrations (νCH/νCD) of the imidazolium ring. The main vibrational spectroscopic features of the neat IL are reproduced by the simulations for a cluster (EmimBr)9, in which all three imidazolium CH moieties of the solvated cation form short contacts with three Br- anions, and another two Br- anions are located on top and bottom of imidazolium ring. Cluster models of aqueous solutions reproduce the experimental vibrational frequencies of actual solutions, provided that the Br- anion of solvated contact ion pair (CIP) is situated on top of imidazolium ring, and CH/CD moieties of the latter participate in short contacts with surrounding water molecules. Both structural and spectroscopic analysis allow to interpret the short contacts CH/CD⋯Br- and CH/CD⋯OH2 as hydrogen bonds of approximately equal strength. Enthalpies of bonding of these liquid-state H-bonds, estimated with the use of empirical correlations, amount to ca. 1.4 kcal⋅mol-1, while the analogous estimates obtained for the gas-phase charged species [Emim]2Br+ increase to 5.6 kcal⋅mol-1. It is shown that formation of solvent-shared ion pair (SIP) in aqueous solution, where the counterions of IL are separated by two water molecules H-bonded to a Br- anion, produces frequency shifts ΔνCH/CD, strongly different from the case of CIP formation. This difference can be used for IR/Raman spectroscopic differentiation of the type of solvated ion pairs of EmimBr or other related ILs.

Copper(II)-Oxyl Formation in a Biomimetic Complex Activated by Hydrogen Peroxide: The Key Role of Trans-Bis(Hydroxo) Species

Enzymes in nature, such as the copper-based lytic polysaccharide monooxygenases (LPMOs), have gained significant attention for their exceptional performance in C-H activation reactions. The use of H2O2 by LPMOs enzymes has also increased the interest in understanding the oxidation mechanism promoted by this oxidant. While some literature proposes Fenton-like chemistry involving the formation of Cu(II)-OH species and the hydroxyl radical, others contend that Cu(I) activation by H2O2 yields a Cu(II)-oxyl intermediate. In this study, we focused on a bioinspired Cu(I) complex to investigate the reaction mechanism of its oxidation by H2O2 using density functional theory and ab initio molecular dynamics simulations. The latter approach was found to be critical for finding the key Cu intermediates. Our results show that the highly flexible coordination environment of copper strongly influences the nature of the oxidized Cu(II) species. Furthermore, they suggest the favorable formation of trans-Cu(II)-(OH)2 moieties in low-coordinated Cu(II) species. This trans configuration hinders the formation of Cu(II)-oxyl species, facilitating intramolecular H-abstraction reactions in line with experimentally observed ligand oxidation processes.

Molecular Decoration and Unconventional Double Bond Migration in Irumamycin Biosynthesis

Irumamycin (Iru) is a complex polyketide with pronounced antifungal activity produced by a type I polyketide (PKS) synthase. Iru features a unique hemiketal ring and an epoxide group, making its biosynthesis and the structural diversity of related compounds particularly intriguing. In this study, we performed a detailed analysis of the iru biosynthetic gene cluster (BGC) to uncover the mechanisms underlying Iru formation. We examined the iru PKS, including the domain architecture of individual modules and the overall spatial structure of the PKS, and uncovered discrepancies in substrate specificity and iterative chain elongation. Two potential pathways for the formation of the hemiketal ring, involving either an olefin shift or electrocyclization, were proposed and assessed using 18O-labeling experiments and reaction activation energy calculations. Based on our findings, the hemiketal ring is likely formed by PKS-assisted double bond migration and TE domain-mediated cyclization. Furthermore, putative tailoring enzymes mediating epoxide formation specific to Iru were identified. The revealed Iru biosynthetic machinery provides insight into the complex enzymatic processes involved in Iru production, including macrocycle sculpting and decoration. These mechanistic details open new avenues for a targeted architecture of novel macrolide analogs through synthetic biology and biosynthetic engineering.

Synergizing Machine Learning, Conceptual Density Functional Theory, and Biochemistry: No-Code Explainable Predictive Models for Mutagenicity in Aromatic Amines

This study synergizes machine learning (ML) with conceptual density functional theory (CDFT) to develop OECD-compliant predictive models for the mutagenic activity of aromatic amines (AAs) with a fully No-Code methodology using a comprehensive data set of 251 AAs, Leave-One-Out-Cross-Validation (LOOCV), and three distinct data splits. Our research employs the GFN2-xTB method, known for its robustness and speed, to compute descriptors for procarcinogens and their activated metabolites in vacuum and aqueous phases. We evaluate the effectiveness of different theoretical definitions of electrophilicity within CDFT, namely, PSL, GCV, and CDP schemes, and the newly introduced Log QP descriptor to approximate Log P information. SPAARC, RandomTree, and JCHAID* ML methods were used to build explainable predictive models with highly robust internal validation (Avg. Correct Classifications = 76% and Avg. Kappa = 0.29) and external validation (Avg. Correct Classifications = 79% and Avg. Kappa = 0.33) metrics, and the results were compared to those of a two hidden layer Multilayer Perceptron. The results indicate that the second CDP definition for the electrophilicity in both vacuum and aqueous phases and also the newly presented Log QP descriptors are the most important ones for predicting the mutagenic activity of AA (namely ω+VacCDP2+, ω+AqCDP2+, and LogQP1+Vac, respectively). The results indicate that metabolic activation, aqueous solvent properties, and the CDP electrophilicity schemes and Log QP should be considered when building predictive models for the mutagenic activity of AA. This study offers a replicable, No-Code approach to QSAR research, making high-level ML and CDFT applications accessible to a broader audience. Future work will expand these methods to other compound families, enhancing predictive capabilities in the study of mutagenic activities and other biological phenomena.

Unraveling the Source of Self-Induced Diastereomeric Anisochronism in Chiral Dipeptides

Mastering of analytical methods for accurate quantitative determinations of enantiomeric excess is a crucial aspect in asymmetric catalysis, chiral synthesis, and pharmaceutical applications. In this context, the phenomenon of Self-Induced Diastereomeric Anisochronism (SIDA) can be exploited in NMR spectroscopy for accurate determinations of enantiomeric composition, without using a chiral auxiliary that could interfere with the spectroscopic investigation. This phenomenon can be particularly useful for improving the quantitative analysis of mixtures with low enantiomeric excesses, where direct integration of signals can be tricky. Here, we describe a novel analysis protocol to correctly determine the enantiomeric composition of scalemic mixtures and investigate the thermodynamic and stereochemical features at the basis of SIDA. Dipeptide derivatives were chosen as substrates for this study, given their central role in drug design. By integrating the experiments with a conformational stochastic search that includes entropic contributions, we provide valuable information on the dimerization thermodynamics, the nature of non-covalent interactions leading to self-association, and the differences in the chemical environment responsible for the anisochronism, highlighting the importance of different stereochemical arrangement and tight association for the distinction between homochiral and heterochiral adducts. An important role played by the counterion was pointed out by computational studies.

Computational Multiscale Study of the Interaction Between the PDMS Polymer and Sunscreen-Related Pollutant Molecules

Sunscreen molecules play a critical role in protecting skin from ultraviolet radiation, yet their efficient detection and separation pose challenges in environmental and analytical contexts. In this work, we employ a multilevel modeling approach to investigate the molecular interactions between representative sunscreen molecules and the polydimethylsiloxane (PDMS) polymer, a material widely recognized for its sorbent properties. Our goal is to explore how these interactions can be fine-tuned to facilitate the effective separation of sunscreen molecules in portable membrane inlet mass spectrometry (MIMS) systems, potentially leading to the development of new membrane materials. Using a combination of advanced computational techniques-force field molecular dynamics simulations, semiempirical GFN2-xTB, and density functional theory calculations-we assess the interaction strength and noncovalent interactions of sunscreen molecules, namely oxybenzone, naphthalene, benzo[a]anthracene, avobenzone, and 1,3,5-trichlorobenzene, with PDMS. Additionally, the effect of temperature on the interaction dynamics is evaluated, with the aim of extending the sorbent capacities of PDMS beyond light polar molecules to larger, polar sunscreen compounds. This study provides critical insights into the molecular-level interactions that may guide the design of novel membrane materials for efficient molecular separation.

Fast and Robust Modeling of Lanthanide and Actinide Complexes, Biomolecules, and Molecular Crystals with the Extended GFN-FF Model

Lanthanides (Ln) and actinides (An) have recently become important tools in biomedical and materials science. However, the development of computational methods able to describe such elements in various environments has not kept up with the pace of the field. Addressing this challenge, this work introduces and showcases an extension of the GFN-FF to An alongside a reparameterization for Ln. This development fills a gap for fast computational methods that are out-of-the-box applicable to large f-element-containing systems with thousands of atoms. We discuss the reparameterization of the charge model and the covalent topology setup and showcase the model through various applications: Molecular dynamics simulations, optimization of Ln-containing biomolecules, and optimizations of several periodic structures. With the presented improvements, GFN-FF is a powerful method that routinely delivers robust and accurate geometries for large Ln/An systems with thousands of atoms.

Hydrogen-Bond-Assisted Electrocatalytic Semi-Oxidation of 5-Hydroxymethylfurfural into 2,5-Diformylfuran by Operando Dissociated N-Oxyl Mediator

The conversion of 5-hydroxymethylfurfural (HMF) to 2,5-diformylfuran (DFF) is a promising approach for enhancing biomass utilization. Nevertheless, traditional methods using noble metal catalysts face challenges due to high costs and poor selectivity towards DFF. Herein, we developed a novel catalytic electrode integrating N-hydroxyphthalimide (NHPI) into a metal-organic framework on a hydrophilic carbon cloth. This design significantly enhances the selective adsorption of HMF due to stronger hydrogen-bond interaction between the electrode's hydrophilic surface and the C(sp3)-OH group in HMF compared to the C(sp2)=O in DFF. Additionally, the electro-driven dissociation of the NHPI-linker generates stabilized N-Oxyl radicals that promote selective semi-oxidation of HMF under neutral conditions. As a result, this approach achieves a high yield rate of 138.2 mol molcat -1 h-1 with a selectivity of 96.7 % for the HMF-to-DFF conversion. This work introduces a novel strategy for designing catalytic electrodes with stabilized N-Oxyl radicals, and offers a promising method for electrocatalytic DFF synthesis, leveraging hydrogen-bond interaction between electrode surface and HMF.

Atomically precise rhodium-indium carbonyl nanoclusters: synthesis, characterization, crystal structure and electron-sponge features

In this paper we present the investigation of the reactivity of [Rh7(CO)16]3- with InCl3, with the aim of expanding the more general study that allowed us to obtain, among other species, the icosahedral [Rh12E(CO)27]n- (n = 4 when E = Ge or Sn; n = 3 when E = Sb or Bi) family of clusters. Indeed, the study resulted in the isolation and characterization of the analogous In-centred icosahedral [Rh12In(CO)28]3- nanocluster (1), which is isoelectronic and isostructural with the [Rh12E(CO)27]n- congeners. During the course of the reaction two more new species, namely the octahedral [Rh6(CO)15InCl3]2- (2) and the dimeric [{Rh6(CO)15InCl2}2]2- (3) have also been identified. The reaction between [Rh7(CO)16]3- and InCl3 proved to be poorly selective; nevertheless, by fine tuning some reaction parameters it was possible to drive the reaction more towards one product or the other. Alternatively, [Rh6(CO)15InCl3]2- can be more selectively prepared by reacting either [Rh5(CO)15]- or, less efficiently, [Rh6(CO)15]2- with InCl3. As for the dimeric [{Rh6(CO)15InCl2}2]2- species, this was only isolated by carrying out the reaction with [Rh7(CO)16]3- under inert atmosphere, as opposed to under CO. All clusters were characterized by IR spectroscopy and ESI-MS, and their molecular structures were fully established by single-crystal X-ray diffraction studies. The [Rh12In(CO)28]3- species was also analysed by EDS via SEM, and further investigated through in situ infrared spectroelectrochemistry and CV experiments to check its multivalence nature. Indeed, [Rh12In(CO)28]3- can reversibly undergo two monoelectronic oxidation and one bi-electronic reduction processes, behaving like an electron sponge and, thus, giving rise to the further [Rh12In(CO)28]n- derivatives (n = 1, 2 and 5). These results parallel the findings for the [Rh12E(CO)27]n- series. The geometry variations of the metal framework associated with the changes in the cluster negative charge were investigated by means of DFT calculations.

Imidazole-Rich, Four-Armed Host-Defense Peptidomimetics as Promising Narrow-Spectrum Antibacterial Agents and Adjuvants against Pseudomonas Aeruginosa Infections

The development of narrow-spectrum antimicrobial agents is paramount for swiftly eradicating pathogenic bacteria, mitigating the onset of drug resistance, and preserving the homeostasis of bacterial microbiota in tissues. Owing to the limited affinity between the hydrophobic lipid bilayer interior of bacterial cells and most hydrophilic, polar peptides, the construction of a distinctive class of four-armed host-defense peptides/peptidomimetics (HDPs) is proposed with enhanced specificity and membrane perturbation capability against Pseudomonas aeruginosa by incorporating imidazole groups. These groups demonstrate substantial affinity for unsaturated phospholipids, which are predominantly expressed in the cell membrane of P. aeruginosa, thereby enabling HDPs to exhibit narrow-spectrum activity against this bacterium. Computational simulations and experimental investigations have corroborated that the imidazole-rich, four-armed peptidomimetics exhibit notable selectivity toward bacteria over mammalian cells. Among them, 4H10, characterized by its abundant and densely distributed imidazole groups, exhibits impressive activity against various clinically isolated P. aeruginosa strains. Moreover, 4H10 has demonstrated potential as an antibiotic adjuvant, enhancing doxycycline accumulation and exerting effects on intracellular targets by efficiently disrupting bacterial cell membranes. Consequently, the hydrogel composed of 4H10 and doxycycline emerged as a promising topical agent, significantly diminishing the skin P. aeruginosa burden by 97.1% within 2 days while inducing minimal local and systemic toxicity.

Reaction-Free Energies for Complexation of Carbohydrates by Tweezer Diboronic Acids

The accurate calculation of reaction-free energies (ΔrG°) for diboronic acids and carbohydrates is challenging due to reactant flexibility and strong solute-solvent interactions. In this study, these challenges are addressed with a semiautomatic workflow based on quantum chemistry methods to calculate conformational free energies, generate microsolvated solute structural ensembles, and compute ΔrG°. Workflow parameters were optimized for accuracy and precision while controlling computational costs. We assessed the accuracy by studying three reactions of diboronic acids with glucose and galactose, finding that the conformational entropy contributes significantly (by 3-5 kcal/mol at room temperature). Explicit solvent molecules improve the computed ΔrG° accuracy by about 4 kcal/mol compared to experimental data, though using 13 or more water molecules reduced precision and increased computational overhead. After fine-tuning, the workflow demonstrated remarkable accuracy, with an absolute error of about 2 kcal/mol compared to experimental ΔrG° and an average interquartile range of 2.4 kcal/mol. These results highlight the workflow's potential for designing and screening tweezer-like ligands with tailored selectivity for various carbohydrates.

Insight into the Role of Hydroxypropyl Cellulose in Tuning Crystallization Behaviors of dl-Methionine

Efficient regulation of dl-methionine (dl-met) crystallization behavior remains of great interest since it usually appears in the form of needles or flakes, which is prone to cause problems in downstream processes. In this study, the crystallization process and crystal morphology of dl-met were focused with hydroxypropyl cellulose (HPC) as the tuning additive. The crystallization behaviors of dl-met with HPC were investigated through experiments and theoretical calculations from thermodynamic and kinetic perspectives to understand the mechanism of HPC in the dl-met crystal morphology. It was found that HPC could raise the interfacial energy γ, making nucleation more difficult. Furthermore, the inhibitory effect of HPC on the axial direction growth was much stronger than that on the radial direction, thereby changing the dl-met crystal morphology from rod-like to block-like. Theoretical calculations explained the underlying mechanism of HPC in regulating crystal habits. Due to the structural differences between different crystal surfaces, HPC exhibited a stronger hydrogen bonding with the (111) crystal surface, thus notably hindering its growth, specifically in the axial direction.

Emerging Conformational-Analysis Protocols from the RTCONF55-16K Reaction Thermochemistry Conformational Benchmark Set

RTCONF55-16K is a new, reactive conformational data set based on cost-efficient methods to assess different conformational analysis protocols. Our reference calculations underpinned the accuracy of the CENSO (Grimme et al. J. Phys. Chem. A, 2021, 125, 4039) procedure and resulted in alternative recipes with different cost-accuracy compromises. Our general-purpose and economical protocols (CENSO-light and zero, respectively) were found to be 10-30 times faster than the original algorithm, adding only 0.4-0.7 kcal/mol absolute error to the relative free energy estimates.

Exploring Novel GSK-3β Inhibitors for Anti-Neuroinflammatory and Neuroprotective Effects: Synthesis, Crystallography, Computational Analysis, and Biological Evaluation

In the pathogenesis of Alzheimer's disease, the overexpression of glycogen synthase kinase-3β (GSK-3β) stands out due to its multifaced nature, as it contributes to the promotion of amyloid β and tau protein accumulation, as well as neuroinflammatory processes. Therefore, in the present study, we have designed, synthesized, and evaluated a new series of GSK-3β inhibitors based on the N-(pyridin-2-yl)cyclopropanecarboxamide scaffold. We identified compound 36, demonstrating an IC50 of 70 nM against GSK-3β. Subsequently, through crystallography studies and quantum mechanical analysis, we elucidated its binding mode and identified the structural features crucial for interactions with the active site of GSK-3β, thereby understanding its inhibitory potency. Compound 36 was effective in the cellular model of hyperphosphorylated tau-induced neurodegeneration, where it restored cell viability after okadaic acid treatment and showed anti-inflammatory activity in the LPS model, significantly reducing NO, IL-6, and TNF-α release. In ADME-tox in vitro studies, we confirmed the beneficial profile of 36, including high permeability in PAMPA (Pe equals 9.4) and high metabolic stability in HLMs as well as lack of significant interactions with isoforms of the CYP enzymes and lack of considerable cytotoxicity on selected cell lines (IC50 > 100 μM on HT-22 cells and 89.3 μM on BV-2 cells). Based on promising pharmacological activities and favorable ADME-tox properties, compound 36 may be considered a promising candidate for in vivo research as well as constitute a reliable starting point for further studies.

Computational and experimental assessment of efficient dye adsorption method from aqueous effluents by halloysite and palygorskite clay minerals

The removal of dyes from effluents of textile industries represents a technological challenge, due to their significant environmental impact. The application of halloysite (Hal) and palygorskite (Pal) clay minerals as adsorbents for the removal of Congo red (CR) and methylene blue (MB) was evaluated in this work. The materials were applied both in natural and acid-treated forms, and characterized by XRD, XPS, SEM-EDS, FTIR, and N2 adsorption-desorption isotherm techniques to identify their properties and main active sites. The adsorbents showed potential to remove CR (> 98%) and MB (> 85%) within 180 min, using 0.3 g adsorbent and initial dye concentration of 250 mg L-1. Semi-empirical quantum mechanical calculations (SQM) confirmed the interaction mechanism between dyes and the adsorbents via chemisorption (- 69.0 kcal mol-1 < Eads <  - 28.8 kcal mol-1), which was further observed experimentally due to the high fit of adsorption data to pseudo-second order kinetic model (R2 > 0.99) and Langmuir isotherm (R2 > 0.98). The use of Pal and Hal to remove dyes was proven to be economically and environmentally viable for industrial application.

Biomimetic nanoplatforms constructed from dialkylaminostyryl hetarene dyes and phospholipids exhibiting selective fluorescent response to specific proteins

The present work explores the specificity of supramolecular assemblies comprising dialkylaminostyrylhetarene dye molecules incorporated into phosphatidylcholine (PC) or phosphatidylserine (PS) aggregates. In PS-based assemblies, the dyes demonstrate a concentration-dependent fluorescent response, distinguishing anionic proteins such as bovine serum albumin (BSA) and pepsin from lysozyme (LYZ) in aqueous solutions. Conversely, no significant response is observed when the dyes are incorporated into the well-organized bilayers of neutral PC. The fluorescent response arises from the binding of dyes to proteins, leading to the detachment of dye molecules from the assemblies, rather than from the binding of proteins to the assemblies, although the latter process is facilitated by electrostatic attraction. Thus, both the poor ordering of PS molecules and the interfacial arrangement of the dyes are prerequisites for the fluorescent response of dye-PS aggregates. The structure of the dyes significantly impacts the spectral features of dye-PS and dye-protein assemblies. An optimal dye structure has been identified for the recognition of BSA, with a limit of detection (LOD) of 10.8 nM.

Molecular Interactions of Perfluorinated and Branched Fluorine-Free Surfactants at Interfaces: Insights from a New Reliable Force Field

Per- and polyfluoroalkyl substances (PFAS) constitute a class of synthetic compounds with exceptional interfacial properties. Their widespread use in many industrial applications and consumer products, combined with their remarkable chemical and thermal stability, has led to their ubiquitous presence in environmental matrices, including surface water and groundwater. To replace PFAS with fluorine-free surfactants, it is necessary first to develop a deep molecular-level understanding of the mechanisms responsible for the exceptional properties of PFAS. For instance, it has been shown that fluorine-free surfactants with highly branched or methylated chains can achieve low surface tensions at air-water interfaces and can provide highly hydrophobic surface coatings. Although molecular simulations combined with experiments are promising for uncovering these mechanisms, the reliability of simulation results depends strongly on the accuracy of the force fields implemented. At the moment, atomistic force fields are not available to describe PFAS in a variety of environments. Ab initio methods could help fill this knowledge gap, but they are computationally demanding. As an alternative, ab initio calculations could be used to develop accurate force fields for atomistic simulations. In this work, a new algorithm is proposed, which, built from accurate ab initio calculations, yields force fields for perfluorinated sulfonic and perfluoroalkyl acids. The accuracy of the new force field was benchmarked against solvation free energy and interfacial tension data. The new force fields were then used to probe the interfacial behavior of the PFAS surfactants. The interfacial properties observed in our simulations were compared with those manifested by two branched fluorine-free surfactants. The good agreement achieved with experiments and ab initio calculations suggests that the proposed protocol could be implemented to study other perfluorinated substances and help in the design of fluorine-free surfactants for targeted applications.

QupKake: Integrating Machine Learning and Quantum Chemistry for Micro-pKa Predictions

Accurate prediction of micro-pKa values is crucial for understanding and modulating the acidity and basicity of organic molecules, with applications in drug discovery, materials science, and environmental chemistry. This work introduces QupKake, a novel method that combines graph neural network models with semiempirical quantum mechanical (QM) features to achieve exceptional accuracy and generalization in micro-pKa prediction. QupKake outperforms state-of-the-art models on a variety of benchmark data sets, with root-mean-square errors between 0.5 and 0.8 pKa units on five external test sets. Feature importance analysis reveals the crucial role of QM features in both the reaction site enumeration and micro-pKa prediction models. QupKake represents a significant advancement in micro-pKa prediction, offering a powerful tool for various applications in chemistry and beyond.

CREMP: Conformer-rotamer ensembles of macrocyclic peptides for machine learning

Computational and machine learning approaches to model the conformational landscape of macrocyclic peptides have the potential to enable rational design and optimization. However, accurate, fast, and scalable methods for modeling macrocycle geometries remain elusive. Recent deep learning approaches have significantly accelerated protein structure prediction and the generation of small-molecule conformational ensembles, yet similar progress has not been made for macrocyclic peptides due to their unique properties. Here, we introduce CREMP, a resource generated for the rapid development and evaluation of machine learning models for macrocyclic peptides. CREMP contains 36,198 unique macrocyclic peptides and their high-quality structural ensembles generated using the Conformer-Rotamer Ensemble Sampling Tool (CREST). Altogether, this new dataset contains nearly 31.3 million unique macrocycle geometries, each annotated with energies derived from semi-empirical extended tight-binding (xTB) DFT calculations. Additionally, we include 3,258 macrocycles with reported passive permeability data to couple conformational ensembles to experiment. We anticipate that this dataset will enable the development of machine learning models that can improve peptide design and optimization for novel therapeutics.

Chemical accuracy for ligand-receptor binding Gibbs energies through multi-level SQM/QM calculations

Calculating the Gibbs energies of binding of ligand-receptor systems with a thermochemical accuracy of ± 1 kcal mol-1 is a challenge to computational approaches. After exploration of the conformational space of the host, ligand and their resulting complexes upon coordination by semi-empirical GFN2 MD and meta-MD simulations, the systematic refinement through a multi-level improvement of binding modes in terms of electronic energies and solvation is able to give Gibbs energies of binding of drug molecules to CB[8] and β-CD macrocyclic receptors with such an accuracy. The accurate treatment of a small number of structures outperforms system-specific force-matching and alchemical transfer model approaches without an extensive sampling and integration.

Molecular folding governs switchable singlet oxygen photoproduction in porphyrin-decorated bistable rotaxanes

Rotaxanes are mechanically interlocked molecules where a ring (macrocycle) is threaded onto a linear molecule (thread). The position of the macrocycle on different stations on the thread can be controlled in response to external stimuli, making rotaxanes applicable as molecular switches. Here we show that bistable rotaxanes based on the combination of a Zn(II) tetraphenylporphyrin photosensitizer, attached to the macrocycle, and a black-hole-quencher, attached to the thread, are capable of singlet oxygen production which can be switched on/off by the addition of base/acid. However, we found that only a sufficiently long linker between both stations on the thread enabled switchability, and that the direction of switching was inversed with regard to the original design. This unexpected behavior was attributed to intramolecular folding of the rotaxanes, as indicated by extensive theoretical calculations. This evidences the importance to take into account the conformational flexibility of large molecular structures when designing functional switchable systems.

Design of Novel Membranes for the Efficient Separation of Bee Alarm Pheromones in Portable Membrane Inlet Mass Spectrometric Systems

Bee alarm pheromones are essential molecules that are present in beehives when some threats occur in the bee population. In this work, we have applied multilevel modeling techniques to understand molecular interactions between representative bee alarm pheromones and polymers such as polymethyl siloxane (PDMS), polyethylene glycol (PEG), and their blend. This study aimed to check how these interactions can be manipulated to enable efficient separation of bee alarm pheromones in portable membrane inlet mass spectrometric (MIMS) systems using new membranes. The study involved the application of powerful computational atomistic methods based on a combination of modern semiempirical (GFN2-xTB), first principles (DFT), and force-field calculations. As a fundamental work material for the separation of molecules, we considered the PDMS polymer, a well-known sorbent material known to be applicable for light polar molecules. To improve its applicability as a sorbent material for heavier polar molecules, we considered two main factors-temperature and the addition of PEG polymer. Additional insights into molecular interactions were obtained by studying intrinsic reactive properties and noncovalent interactions between bee alarm pheromones and PDMS and PEG polymer chains.

Computational Screening of Putative Catalyst Transition Metal Complexes as Guests in a Ga4L612- Nanocage

Metal-organic cages form well-defined microenvironments that can enhance the catalytic proficiency of encapsulated transition metal complexes (TMCs). We introduce a screening protocol to efficiently identify TMCs that are promising candidates for encapsulation in the Ga4L612- nanocage. We obtain TMCs from the Cambridge Structural Database with geometric and electronic characteristics amenable to encapsulation and mine the text of associated manuscripts to curate TMCs with documented catalytic functionality. By docking candidate TMCs inside the nanocage cavity and carrying out electronic structure calculations, we identify a subset of successfully optimized candidates (TMC-34) and observe that encapsulated guests occupy an average of 60% of the cavity volume, in line with previous observations. Notably, some guests occupy as much as 72% of the cavity as a result of linker rotation. Encapsulation has a universal effect on the electrostatic potential (ESP), systematically decreasing the ESP at the metal center of each TMC in the TMC-34 data set, while minimally altering TMC metal partial charges. Collectively these observations support geometry-based screening of potential guests and suggest that encapsulation in Ga4L612- cages could electrostatically stabilize diverse cationic or electropositive intermediates. We highlight candidate guests with associated known reactivity and solubility most amenable for encapsulation in experimental follow-up studies.

The sugar cube: Network control and emergence in stereoediting reactions

Stereochemical editing strategies have recently enabled the transformation of readily accessible substrates into rare and valuable products. Typically, site selectivity is achieved by minimizing kinetic complexity by using protecting groups to suppress reactivity at undesired sites (substrate control) or by using catalysts with tailored shapes to drive reactivity at the desired site (catalyst control). We propose "network control," a contrasting paradigm that exploits hidden interactions between rate constants to greatly amplify modest intrinsic biases and enable precise multisite editing. When network control is applied to the photochemical isomerization of hexoses, six of the eight possible diastereomers can be selectively obtained. The amplification effect can be viewed as a mesoscale phenomenon between the limiting regimes of kinetic control in simple chemical systems and metabolic regulation in complex biological systems.

Quantum chemical calculations of nitrosamine activation and deactivation pathways for carcinogenicity risk assessment

N-nitrosamines and nitrosamine drug substance related impurities (NDSRIs) became a critical topic for the development and safety of small molecule medicines following the withdrawal of various pharmaceutical products from the market. To assess the mutagenic and carcinogenic potential of different N-nitrosamines lacking robust carcinogenicity data, several approaches are in use including the published carcinogenic potency categorization approach (CPCA), the Enhanced Ames Test (EAT), in vivo mutagenicity studies as well as read-across to analogue molecules with robust carcinogenicity data. We employ quantum chemical calculations as a pivotal tool providing insights into the likelihood of reactive ion formation and subsequent DNA alkylation for a selection of molecules including e.g., carcinogenic N-nitrosopiperazine (NPZ), N-nitrosopiperidine (NPIP), together with N-nitrosodimethylamine (NDMA) as well as non-carcinogenic N-nitrosomethyl-tert-butylamine (NTBA) and bis (butan-2-yl) (nitros)amine (BBNA). In addition, a series of nitroso-methylaminopyridines is compared side-by-side. We draw comparisons between calculated reaction profiles for structures representing motifs common to NDSRIs and those of confirmed carcinogenic and non-carcinogenic molecules with in vivo data from cancer bioassays. Furthermore, our approach enables insights into reactivity and relative stability of intermediate species that can be formed upon activation of several nitrosamines. Most notably, we reveal consistent differences between the free energy profiles of carcinogenic and non-carcinogenic molecules. For the former, the intermediate diazonium ions mostly react, kinetically controlled, to the more stable DNA adducts and less to the water adducts via transition-states of similar heights. Non-carcinogenic molecules yield stable carbocations as intermediates that, thermodynamically controlled, more likely form the statistically preferred water adducts. In conclusion, our data confirm that quantum chemical calculations can contribute to a weight of evidence approach for the risk assessment of nitrosamines.

QM/MM Free Energy Calculations of Long-Range Biological Protonation Dynamics by Adaptive and Focused Sampling

Water-mediated proton transfer reactions are central for catalytic processes in a wide range of biochemical systems, ranging from biological energy conversion to chemical transformations in the metabolism. Yet, the accurate computational treatment of such complex biochemical reactions is highly challenging and requires the application of multiscale methods, in particular hybrid quantum/classical (QM/MM) approaches combined with free energy simulations. Here, we combine the unique exploration power of new advanced sampling methods with density functional theory (DFT)-based QM/MM free energy methods for multiscale simulations of long-range protonation dynamics in biological systems. In this regard, we show that combining multiple walkers/well-tempered metadynamics with an extended system adaptive biasing force method (MWE) provides a powerful approach for exploration of water-mediated proton transfer reactions in complex biochemical systems. We compare and combine the MWE method also with QM/MM umbrella sampling and explore the sampling of the free energy landscape with both geometric (linear combination of proton transfer distances) and physical (center of excess charge) reaction coordinates and show how these affect the convergence of the potential of mean force (PMF) and the activation free energy. We find that the QM/MM-MWE method can efficiently explore both direct and water-mediated proton transfer pathways together with forward and reverse hole transfer mechanisms in the highly complex proton channel of respiratory Complex I, while the QM/MM-US approach shows a systematic convergence of selected long-range proton transfer pathways. In this regard, we show that the PMF along multiple proton transfer pathways is recovered by combining the strengths of both approaches in a QM/MM-MWE/focused US (FUS) scheme and reveals new mechanistic insight into the proton transfer principles of Complex I. Our findings provide a promising basis for the quantitative multiscale simulations of long-range proton transfer reactions in biological systems.

Bartender: Martini 3 Bonded Terms via Quantum Mechanics-Based Molecular Dynamics

Coarse-grained (CG) molecular dynamics (MD) simulations have grown in applicability over the years. The recently released version of the Martini CG force field (Martini 3) has been successfully applied to simulate many processes, including protein-ligand binding. However, the current ligand parametrization scheme is manual and requires an a priori reference all-atom (AA) simulation for benchmarking. For systems with suboptimal AA parameters, which are often unknown, this translates into a CG model that does not reproduce the true dynamical behavior of the underlying molecule. Here, we present Bartender, a quantum mechanics (QM)/MD-based parametrization tool written in Go. Bartender harnesses the power of QM simulations and produces reasonable bonded terms for Martini 3 CG models of small molecules in an efficient and user-friendly manner. For small, ring-like molecules, Bartender generates models whose properties are indistinguishable from the human-made models. For more complex, drug-like ligands, it is able to fit functional forms beyond simple harmonic dihedrals and thus better captures their dynamical behavior. Bartender has the power to both increase the efficiency and the accuracy of Martini 3-based high-throughput applications by producing numerically stable and physically realistic CG models.

Dataset for quantum-mechanical exploration of conformers and solvent effects in large drug-like molecules

We here introduce the Aquamarine (AQM) dataset, an extensive quantum-mechanical (QM) dataset that contains the structural and electronic information of 59,783 low-and high-energy conformers of 1,653 molecules with a total number of atoms ranging from 2 to 92 (mean: 50.9), and containing up to 54 (mean: 28.2) non-hydrogen atoms. To gain insights into the solvent effects as well as collective dispersion interactions for drug-like molecules, we have performed QM calculations supplemented with a treatment of many-body dispersion (MBD) interactions of structures and properties in the gas phase and implicit water. Thus, AQM contains over 40 global and local physicochemical properties (including ground-state and response properties) per conformer computed at the tightly converged PBE0+MBD level of theory for gas-phase molecules, whereas PBE0+MBD with the modified Poisson-Boltzmann (MPB) model of water was used for solvated molecules. By addressing both molecule-solvent and dispersion interactions, AQM dataset can serve as a challenging benchmark for state-of-the-art machine learning methods for property modeling and de novo generation of large (solvated) molecules with pharmaceutical and biological relevance.

Influence of proline and hydroxyproline as antimicrobial and anticancer peptide components on the silver(I) ion activity: structural and biological evaluation with a new theoretical and experimental SAR approach

Silver(I) complexes with proline and hydroxyproline were synthesized and structurally characterized and crystal structure analysis shows that the formulas of the compounds are {[Ag2(Pro)2(NO3)]NO3}n (AgPro) (Pro = L-proline) and {[Ag2(Hyp)2(NO3)]NO3}n (AgHyp) (Hyp = trans-4-hydroxy-L-proline). Both complexes crystallize in the monoclinic lattice with space group P21 with a carboxylate bidentate-bridging coordination mode of the organic ligands Pro and Hyp (with NH2+ and COO- groups in zwitterionic form). Both complexes have a distorted seesaw (C2v) geometry around one silver(I) ion with τ4 values of 58% (AgPro) and 51% (AgHyp). Moreover, the results of spectral and thermal analyses correlate with the structural ones. 1H and 13C NMR spectra confirm the complexes species' presence in the DMSO biological testing medium and their stability in the time range of the bioassays. In addition, molar conductivity measurements indicate complexes' behaviour like 1 : 1 electrolytes. Both complexes showed higher or the same antibacterial activity against Bacillus cereus, Pseudomonas aeruginosa and Staphylococcus aureus as AgNO3 (MIC = 0.063 mM) and higher than silver(I) sulfadiazine (AgSD) (MIC > 0.5 mM) against Pseudomonas aeruginosa. In addition, complex AgPro exerted a strong cytotoxic effect against the tested MDA-MB-231 and Jurkat cancer cell lines (IC50 values equal to 3.7 and 3.0 μM, respectively) compared with AgNO3 (IC50 = 6.1 (5.7) μM) and even significantly higher selectivity than cisplatin (cisPt) against MDA-MB-231 cancer cell lines (SI = 3.05 (AgPro); 1.16 (cisPt), SI - selectivity index). The binding constants and the number of binding sites (n) of AgPro and AgHyp complexes with bovine serum albumin (BSA) were determined at four different temperatures, and the zeta potential of BSA in the presence of silver(I) complexes was also measured. The in ovo method shows the safety of the topical and intravenous application of AgPro and AgHyp. Moreover, the complexes' bioavailability was verified by lipophilicity evaluation from the experimental and theoretical points of view.

Modeling One-Electron Oxidation Potentials and Hole Delocalization in Double-Stranded DNA by Multilayer and Dynamic Approaches

The number of innovative applications for DNA nowadays is growing quickly. Its use as a nanowire or electrochemical biosensor leads to the need for a deep understanding of the charge-transfer process along the strand, as well as its redox properties. These features are computationally simulated and analyzed in detail throughout this work by combining molecular dynamics, multilayer schemes, and the Marcus theory. One-electron oxidation potential and hole delocalization have been analyzed for six DNA double strands that cover all possible binary combinations of nucleotides. The results have revealed that the one-electron oxidation potential decreases with respect to the single-stranded DNA, giving evidence that the greater rigidity of a double helix induces an increase in the capacity of storing the positive charge generated upon oxidation. In addition, the hole is mainly stored in nucleobases with large reducer character, i.e., purines, especially when those are arranged in a stacked configuration in the same strand. From the computational point of view, the sampling needed to describe biological systems implies a significant computational cost. Here, we show that a small number of representative conformations generated by clustering analysis provides accurate results when compared with those obtained from sampling, reducing considerably the computational cost.

Utilization of 13C NMR Carbon Shifts for the Attribution of Diastereomers in Methyl-Substituted Cyclohexanes

In this report, we address the challenge of assigning diastereomers for methyl cyclohexanes, particularly those with quaternary centers, which remains nontrivial despite modern NMR techniques. By utilizing a HSQC NMR experiment to identify methyl-carbons coupled with a simple conformational analysis, we identified an effective and quite general method for assigning stereochemistry, even in cases where diastereomeric mixtures are inseparable.

Finding the Ajoene Sweet-Spot: Structure-Activity Relations that Govern its Blood Stability and Cancer Cytotoxicity

Ajoene is an organosulfur compound found in crushed garlic that exerts its anti-cancer activity by S-thiolating cysteine residues on proteins. Its development is hampered due to limited bioavailability, so in this study, we synthesised analogues of ajoene to probe the significance of the ajoene vinyl disulfide/sulfoxide core with respect to cytotoxicity and blood stability. Polar side groups were also incorporated to improve aqueous solubility. It was found that derivatives containing a vinyl disulfide functional group (4-7, as in ajoene), were more cytotoxic compared to analogues in which the double bond was removed, although the latter showed superior blood stability. It was also found that the allyl-S sulfur of the disulfide was more electrophilic to S-thiolysis based on the global electrophilicity index (ω) and the condensed electrophilic Fukui functionf k + ${{ f}_{\rm{k}}^{\rm{ + }} }$ . S-Thiolysis was found to be exergonic for the vinyl disulfides based on entropy and enthalpy computations with a deprotonated thiolate. Derivatisation to the dihydro (10, 12) and deoxydihydroajoenes (9, 11) produced analogues that were slightly less potent but with greatly improved blood stability. Taken together, the deoxydihydroajoenes present themselves as good candidates for further therapeutic development.

Nonclassical Zwitterions as a Design Principle to Reduce Lipophilicity without Impacting Permeability

The ionization of bioactive molecules impacts many ADME-relevant physicochemical properties, in particular, solubility, lipophilicity, and permeability. Ampholytes contain both acidic and basic groups and are distinguished as ordinary ampholytes and zwitterions. An influential review states that zwitterions only exist if the acidic pKa is significantly lower than the basic pKa. Through concordance of measured and calculated pKa and log P, we show that the zwitterionic behavior of several marketed drugs and natural products occurs despite a low or negative ΔpKa. These nonclassical zwitterions are characterized by a weak acidic and basic pKa and conjugation through an extended aromatic system, often including pseudorings via intramolecular hydrogen bonds. In contrast to most classical zwitterions, nonclassical zwitterions can exhibit excellent permeability. As permeability and lipophilicity are typically correlated, the combination of low lipophilicity and high permeability makes nonclassical zwitterions an attractive design principle in medicinal chemistry.

Application of a modern theoretical approach to the study of the interaction of KR-12 peptides derived from human cathelicidins with Cu(II) ions

The human cationic antimicrobial protein (hCAP) corresponding to the overlapping sequences of 151-162 of hCAP named KR-12 peptide is the smallest portion of the only type of human Cathelicidin, which has been shown to be modifiable into a more effective antimicrobial. In this study, an in silico analysis, supported by potentiometric titration and isothermal titration calorimetry techniques, was performed to identify potential Cu(II) binding sites of KR-12. The analysis of the presented data at the given theoretical level (GFN2-xTB/ALPB) revealed which peptide chain fragments are involved in the most favourable KR-12-Cu(II) binding mode. Based on a quantum chemical approach, the most favourable coordination modes of Cu(II) to peptides are proposed together with the discussion of the chemical nature of the interactions. The presented results demonstrated that KR-12 interacts with metal ions mostly via the main chain's oxygen atoms; however, the two types of amino acids that are expected to be vital for the interaction of Cu(II) are D (aspartic acid) and R29 (arginine). It was demonstrated that in order to explain the complexity of the interaction process in peptide-metal ion systems, the use of theoretical methods is sometimes necessary to explain the details of the experimental results and provide an in-depth understanding of these dynamic systems.

Bicarbonate-binding catalysis for the enantioselective desymmetrization of keto sulfonium salts

Herein, an enantioselective desymmetrization of cyclic keto sulfonium salts through enantioselective deprotonation/ring opening process by anion-binding catalysis is presented. We report a squaramide/HCO3- complex as catalytic active species which is able to stereo-differentiate two enantiomeric protons, triggering the ring opening event taking advantage of the great tendency of sulfonium salts to act as leaving groups. Thus, this desymmetrization methodology give rise to β-methylsulfenylated sulfa-Michael addition type products with excellent yields and very good enantioselectivities. The bifunctional organocatalyst has been demonstrated to be capable of activating simultaneously the base and the keto sulfonium salt by DFT calculations and experimental proofs.

Limiting factors in the accuracy of DFT calculation for redox potentials

In the present study, we have investigated factors affecting the accuracy of computational chemistry calculation of redox potentials, namely the gas-phase ionization energy (IE) and electron affinity (EA), and the continuum solvation effect. In general, double-hybrid density functional theory methods yield IEs and EAs that are on average within ~0.1 eV of our high-level W3X-L benchmark, with the best performing method being DSD-BLYP/ma-def2-QZVPP. For lower-cost methods, the average errors are ~0.2-0.3 eV, with ωB97X-3c being the most accurate (~0.15 eV). For the solvation component, essentially all methods have an average error of ~0.3 eV, which shows the limitation of the continuum solvation model. Curiously, the directly calculated redox potentials show errors of ~0.3 eV for all methods. These errors are notably smaller than what can be expected from error propagation with the two components (IE and EA, and solvation effect). Such a discrepancy can be attributed to the cancellation of errors, with the lowest-cost GFN2-xTB method benefiting the most, and the most accurate ωB97X-3c method benefiting the least. For organometallic species, the redox potentials show large deviations exceeding ~0.5 eV even for DSD-BLYP. The large errors are attributed to those for the gas-phase IEs and EAs, which represents a major barrier to the accurate calculation of redox potentials for such systems.

Development of a multi-step screening procedure for redox active molecules in organic radical polymer anodes and as redox flow anolytes

Benzo[d]-X-zolyl-pyridinyl (XO, S, NH) radicals represent a promising class of redox-active molecules for organic batteries. We present a multistep screening procedure to identify the most promising radical candidates. Experimental investigations and highly correlated wave function-based calculations are performed to determine benchmark redox potentials. Based on these, the accuracies of different methods (semi-empirical, density functional theory, wave function-based), solvent models, dispersion corrections, and basis sets are evaluated. The developed screening procedure consists of three steps: First, a conformer search is performed with CREST. The molecules are selected based on the redox potentials calculated using GFN2-xTB. Second, HOMO energies calculated with reparametrized B3LYP-D3(BJ) and the def2-SVP basis set are used as selection criteria. The final molecules are selected based on the redox potentials calculated from Gibbs energies using BP86-D3(BJ)/def2-TZVP. With this multistep screening approach, promising molecules can be suggested for synthesis, and structure-property relationships can be derived.

Scaffold-Oriented Asymmetric Catalysis: Conformational Modulation of Transition State Multivalency during a Catalyst-Controlled Assembly of a Pharmaceutically Relevant Atropisomer

A new class of superbasic, bifunctional peptidyl guanidine catalysts is presented, which enables the organocatalytic, atroposelective synthesis of axially chiral quinazolinediones. Computational modeling unveiled the conformational modulation of the catalyst by a novel phenyl urea N-cap, that preorganizes the structure into the active, folded state. A previously unanticipated noncovalent interaction involving a difluoroacetamide acting as a hybrid mono- or bidentate hydrogen bond donor emerged as a decisive control element inducing atroposelectivity. These discoveries spurred from a scaffold-oriented project inspired from a fascinating investigational BTK inhibitor featuring two stable chiral axes and relies on a mechanistic framework that was foreign to the extant lexicon of asymmetric catalysis.

Binding energies and hydrogen bonds effects on DNA-cisplatin interactions: a DFT-xTB study

CONTEXT

A systematic study of hydrogen bonds in base pairs and the interaction of cisplatin with DNA fragments was carried out. Structure, binding energies, and electron density were analyzed. xTB has proven to be an accurate method for obtaining structures and binding energies in DNA structures. Our xTB values for DNA base binding energy were in the same order and in some cases better than CAM-B3LYP values compared to experimental values. Double-stranded DNA-cisplatin structures have been calculated and the hydrogen bonds of water molecules are a decisive factor contributing to the preference for the cisplatin-Guanine interaction. Higher values of the water hydrogen bonding energies were obtained in cisplatin-Guanine structures. Furthermore, the electrostatic potential was used to investigate and improve the analysis of DNA-cisplatin structures.

METHODS

We applied the xTB method and the CAM-B3LYP functional combined with def2-SVP basis set to perform and analyze of the bonding energies of the cisplatin interaction and the effects of the hydrogen bonds. Results were calculated employing the xTB and the ORCA software.

Combined Computational and Experimental Study Reveals Complex Mechanistic Landscape of Brønsted Acid-Catalyzed Silane-Dependent P═O Reduction

The reaction mechanism of Brønsted acid-catalyzed silane-dependent P═O reduction has been elucidated through combined computational and experimental methods. Due to its remarkable chemo- and stereoselective nature, the Brønsted acid/silane reduction system has been widely employed in organophosphine-catalyzed transformations involving P(V)/P(III) redox cycle. However, the full mechanistic profile of this type of P═O reduction has yet to be clearly established to date. Supported by both DFT and experimental studies, our research reveals that the reaction likely proceeds through mechanisms other than the widely accepted "dual activation mode by silyl ester" or "acid-mediated direct P═O activation" mechanism. We propose that although the reduction mechanisms may vary with the substitution patterns of silane species, Brønsted acid generally activates the silane rather than the P═O group in transition structures. The proposed activation mode differs significantly from that associated with traditional Brønsted acid-catalyzed C═O reduction. The uniqueness of P═O reduction originates from the dominant Si/O═P orbital interactions in transition structures rather than the P/H-Si interactions. The comprehensive mechanistic landscape provided by us will serve as a guidance for the rational design and development of more efficient P═O reduction systems as well as novel organophosphine-catalyzed reactions involving P(V)/P(III) redox cycle.

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.

Copper-catalysed electrophilic carboamination of terminal alkynes with benzyne looked at through the computational lens

A detailed computational mechanistic study of the copper-catalysed three-component-type electrophilic carboamination of terminal alkynes with benzyne and an archetypal O-benzoylhydroxylamine electrophile is presented. Probing various plausible pathways for relevant elementary steps and scrutinising performance degradation pathways, with the aid of a reliable computational protocol applied to a realistic catalyst model combined with kinetic analysis, identified the pathways preferably traversed in productive catalysis. It entails rapid alkynylcupration of in situ generated benzyne to deliver the arylcopper nucleophile that undergoes amination with the O-benzoylhydroxylamine electrophile to afford copper benzoate. Umpolung-enabled electrophilic amination favours a multistep SN2-type oxidative addition/N-C bond-forming reductive elimination sequence involving a short-lived formal {P^P}CuIII carboxylate amido aryl intermediate. SN2-type displacement of the benzoate leaving group at the arylcopper nucleophile, which represents the catalyst resting state, is predicted to be the turnover limiting step. Alkynolysis transforms copper benzoate back to catalytically competent alkynylcopper. The computational probe of a wider range of substrates reveals that only severely ring-strained C6-arynes, C6-cycloalkynes and electron-deficient cyclopropenes featuring a highly reactive C≡C linkage could replace benzyne. Moreover, strict control of stationary benzyne concentration is indispensable for electrophilic carboamination to ever become achievable.