GFN2-xTB-An Accurate and Broadly Parametrized Self-Consistent Tight-Binding Quantum Chemical Method with Multipole Electrostatics and Density-Dependent Dispersion Contributions

Molecular insights into chromatography: Automated workflows for the virtual design of methacrylate-based chromatography resins

Computational chemistry provides invaluable insights into the behaviors and properties of various materials at the molecular level. This capability is of particular interest in chromatography where adsorbents engage with target molecules through intricate interactions. However, the broad integration of molecular simulations into the field of chromatography has been notably limited, despite significant achievements in previous studies. One potential reason is the requirement for considerable expertise to effectively configure these simulations, presenting a significant barrier to entry. In this context, workflow management systems (WMSs) provide a viable solution by allowing experts to automate complex simulation tasks, making them accessible to the wider research community without necessitating in-depth knowledge of the simulation process. This manuscript outlines the creation and application of two automated workflows designed to generate comprehensive all-atom models of methacrylate-based chromatography resin surfaces and to rapidly calculate binding free energies with peptides as target molecules. These innovations represent a significant advancement in the field by streamlining the simulation process, enhancing predictive accuracy, and making complex molecular modeling more accessible to researchers across disciplines. By publishing these workflows, we aim to catalyze molecular modeling in the field of chromatography by encouraging scientists to utilize and build upon our work.

Engaging an engineered PARP-2 catalytic domain mutant to solve the complex structures harboring approved drugs for structure analyses

The PARP-1/2 inhibitors have been approved for the treatment of cancers by modulating the enzymatic activity and/or the trapping ability for damaged DNA of PARP-1 and/or PARP-2, and the selective PARP-1 inhibitors are now attracting considerable attention with an aim to search for drug candidates with an improved safety. Exploring the structural basis of the selectivity and trapping capability of known PARP-1/2 inhibitors would be beneficial for the discovery of the improved inhibitors. Herein, a mutated PARP-2 catalytic domain, designated as catPARP-2SE, was engineered. It could be expressed in an elevated level and had capability to crystalize at 25 °C, which greatly facilitated obtaining PARP-2 crystals. Consequently, the complex structures of Fluzoparib, Pamiparib, Rucaparib, and Niraparib within PARP-2 were achieved. Taking advantage of these complexed structures, the detailed and quantitative analyses of protein-ligand and intra-protein interactions (αB-αF, αJ-αB, αJ-αF, ASL-αD and ASL-αF interfaces) were conducted with quantum chemistry methods (GFN2-xTB and IGMH). It suggested that the residues adjacent to Asp766 in the HD and ASL domains and the αJ-αF and ASL-αD interfaces were closely related to the selectivity and trapping mechanism. These results would provide some insights for the design and development of novel PARP-1/2 inhibitors with improved pharmacodynamic properties.

Automated Microsolvation for Minimum Energy Path Construction in Solution

Describing chemical reactions in solution on a molecular level is a challenging task due to the high mobility of weakly interacting solvent molecules which requires configurational sampling. For instance, polar and protic solvents can interact strongly with solutes and may interfere in reactions. To define and identify representative arrangements of solvent molecules modulating a transition state is a nontrivial task. Here, we propose to monitor their active participation in the decaying normal mode at a transition state, which defines active solvent molecules. Moreover, it is desirable to prepare a low-dimensional microsolvation model in a well-defined, fully automated, high-throughput, and easy-to-deploy fashion, which we propose to derive in a stepwise protocol. First, transition state structures are optimized in a sufficiently solvated quantum-classical hybrid model, which are subjected to a redefinition of a then reduced quantum region. From the reduced model, minimally microsolvated structures are extracted that contain only active solvent molecules. Modeling the remaining solvation effects is deferred to a continuum model. To establish an easy-to-use free-energy model, we combine the standard thermochemical gas-phase model with a correction for the cavity entropy in solution. We assess our microsolvation and free-energy models for methanediol formation from formaldehyde; for the hydration of carbon dioxide (which we consider in a solvent mixture to demonstrate the versatility of our approach); and, finally, for the chlorination of phenol with hypochlorous acid.

Light-Driven Lithium Extraction from Mixtures of Alkali Cations Using an Azobipyridine Ligand

Humanity's replacement of fossil-fuel energy with renewable electricity will require extensive deployment of lithium batteries, which necessitates an increase in the rate of production of this vital metal. Current extraction methods from brines and ores are very energy-intensive, as are recycling methods. Here we report a novel Li5L2 sandwich structure in which five lithium ions are bound between the azobipyridine groups of two pentagonal ligands. The geometry of these ligands leads to the selectivity for binding Li+ in the presence of Na+ and K+. Upon illumination, the trans-azobipyridine moieties of the ligands isomerize to their cis form, promoting the release of free Li+ as the sandwich comes apart. The selective complexation of Li+, and its photostimulated release, were used as the basis of a cycle for selectively extracting and releasing Li+ ions from a mixture with Na+ and K+. Solar energy may thus be used directly to purify this metal, which is essential for storing increasingly cheap electricity produced through renewable means.

Studying Noncovalent Interactions in Molecular Systems with Machine Learning

Noncovalent interactions (NCIs) is an umbrella term for a multitude of typically weak interactions within and between molecules. Despite the low individual energy contributions, their collective effect significantly influences molecular behavior. Accordingly, understanding these interactions is crucial across fields like catalysis, drug design, materials science, and environmental chemistry. However, predicting NCIs is challenging, requiring at least molecular mechanics-level pairwise energy contributions or efficient quantum mechanical electron correlation treatment. In this review, we investigate the application of machine learning (ML) to study NCIs in molecular systems, an emerging research field. ML excels at modeling complex nonlinear relationships, and is capable of integrating vast data sets from experimental and theoretical sources. It offers a powerful approach for analyzing interactions across scales, from small molecules to large biomolecular assemblies. Specifically, we examine data sets characterizing NCIs, compare molecular featurization techniques, assess ML models predicting NCIs explicitly, and explore inverse design approaches. ML enhances predictive accuracy, reduces computational costs, and reveals overlooked interaction patterns. By identifying current challenges and future opportunities, we highlight how ML-driven insights could revolutionize this field. Overall, we believe that recent proof-of-concept studies foreshadow exciting developments for the study of NCIs in the years to come.

Native globular ferritin nanopore sensor

High-resolution nanopore analysis technology relies on the design of novel transmembrane protein platforms. Traditional barrel-shaped protein channels are preferred for constructing nanopore sensors, which may miss protein candidates in non-barrel structures. Here, we demonstrate the globular ferritin displays excellent membrane-insertion capacity and stable transmembrane ionic current owing to its hydrophobic four-fold channels and hydrophilic three-fold channels. The ionic current rectification and voltage-gating characteristics are discovered in single-ferritin ionic current measurement. Notably, the ferritin is used as a nanopore sensor, by which we achieve the high resolution discrimination of L-cysteine, L-homocysteine, and cysteine-containing dipeptides with the assistance of equivalent Cu2+. The mechanistic studies by multiple controlled experiments and quantum mechanics/all-atom/coarse-grained multiscale MD simulations reveal that analytes are synergistically captured by His114, Cys126, and Glu130 within C3 channel, causing the current blockage signals. The promising ferritin nanopore sensor provides a guide to discovering new protein nanopores without shape restrictions.

Enantioselective Photochemical Generation of a Short-Lived, Twisted Cycloheptenone Isomer: Catalytic Formation, Detection, and Consecutive Chemistry

Cyclohept-2-enone-3-carboxylic acid undergoes a photochemical isomerization from its cis- to its trans-form either upon direct irradiation (λ = 366 nm) or in the presence of a triplet sensitizer (λ = 459 nm). The intermediate chiral trans-isomer was detected by step-scan FTIR, displaying a lifetime of 130 µs (r.t., CH2Cl2). Ensuing Diels-Alder reactions of the trans-isomer occurred smoothly and produced chiral trans-fused cycloaddition products (14 examples, 24%-98% yield). Benzylation led to esters, which were separated by chiral HPLC and which were employed to evaluate a possible enantioselective reaction course. It was discovered that a chiral phosphoric acid with a pendant sensitizing group induces a notable enantioselectivity in the photoisomerization step. The planar chirality of the trans-cycloheptene translates into point chirality in the Diels-Alder reaction (seven examples, up to 38% ee). Computational studies suggest that the chiral conformation of the cis-isomer adopted within the assembly to the chiral phosphoric acid induces the enantioselectivity in a one-bond flip (OBF) toward the trans-isomer. Trajectory surface hopping (TSH) simulations showed exemplarily how a chiral trans-cyclohept-2-enone is formed from a chiral cis-conformer. For the Diels-Alder reaction, a weak ground state selectivity was found to attenuate the enantioselectivity achieved in the photochemical step.

Phyllanfranins A-F, anti-inflammatory ent-cleistanthane diterpenoids from Phyllanthus franchetianus

A comprehensive chemical investigation of the EtOAc extract derived from the dried branches and leaves of Phyllanthus franchetianus H. Lév had successfully resulted to the isolation of six undescribed cleistanthane diterpenoids phyllanfranins A-F (1-6), along with three known compounds phyllarheophol C (7), phyacioid C (8), and spruceanol (9). The chemical structures of these compounds were elucidated by combined means of HRESIMS, 1D and 2D NMR spectra, together with ECD calculations. The absolute configuration of phyllanfranin A (1) was established by single crystal X-ray diffraction analysis. Notably, phyllanfranin F (6) represents the first ent-cleistanthane diterpenoid with the unique 6/6/6/6 tetracyclic system occurring in nature. Additionally, all the isolates were evaluated for anti-inflammatory activities. As a result, compounds 4 and 8 showed notable inhibitory activity against NO production in LPS-stimulated macrophages RAW264.7 cells, with IC50 values of 19.03 and 18.14 μM, respectively.

Parametrization of Zirconium for DFTB3/3OB: A Pathway to Study Complex Zr-Compounds for Biomedical and Material Science Applications

This work presents the extension of the semi-empirical density functional tight binding method, DFTB3, to include zirconium for biomedical and material science applications. The parametrization of Zr has been carried out in consistency with already established 3OB parameters including the elements C, H, N, O, S, P, Mg, Zn, Na, K, Ca, F, Cl, Br, and I. Zirconium-ligand association and reaction energies have been compared with results from quantum chemical calculations obtained at MP2 and DFT (PBE and B3LYP) level of theory, as well as with those from the semi-empirical methods DFTB2/PTBP and GFN2-xTB. A structural validation has been carried out on 1,897 compounds reported in the Cambridge Structural Database, revealing an average root mean square deviation comparable to that obtained at semi-empirical (DFTB2/PTBP and GFN2-xTB) level of theory and via the novel neural network potential MACE-MP-0. To provide a critical validation of the newly derived parameters, the structure of the biomedically relevant Zr-DFO complex has been evaluated with respect to a DFT (B3LYP) reference calculation. In addition, extensive DFTB3 MD simulations of the two prominent metal-organic frameworks UiO-66 and UiO-67 have been performed. The results demonstrate the applicability of the newly developed parameters for the study of zirconium-containing metal-organic frameworks, when compared to experimental measurements, as well as computational approaches.

Insight into the mechanism of different substituents on the zinc-oxo cluster in solubility switching

A photoresist based on Zn(MA)n(TFA)6-n (n ≤ 6; MA and TFA are methacrylate and trifluoroacetate, respectively) shows excellent performance in extreme ultraviolet lithography (EUVL). The material is a mixture based on the ratio of MA and TFA, which could affect the production and reproducibility of the photoresist. In this paper, the structural characteristics and solubility switch mechanism of diverse compositions based on Zn(MA)n(TFA)6-n were systematically investigated using DFT calculations and molecular dynamics (MD) simulations. Our results reveal the relationship between the structure and properties from an atomistic insight into these compounds with different ligand ratios. Diverse organic ligands can generate different free radicals via decarboxylation after ionization. These radicals could determine the energy landscape of the subsequent solubility switch (chain propagation) in EUVL. In addition, the desired compositions in Zn(MA)n(TFA)6-n were obtained using different temperatures and solvents, which would offer helpful guidance for synthesis and separation experiments.

The contribution of cyclic imide stereoisomers on cereblon-dependent activity

Thalidomide, lenalidomide, and their derivatives mimic glutarimide and aspartimide protein modifications that give rise to a motif recognized by the E3 ligase substrate adapter cereblon (CRBN). These cyclic imides have a chiral center that, given the biological significance of chirality, may influence CRBN's function and therapeutic applications. Here, we systematically examine cyclic imides in small molecules, peptides, and proteins to assess their racemization, CRBN engagement, ternary complex formation in vitro, and resulting degradation outcomes in cells. While the thalidomide-binding domain of CRBN consistently favors the (S)-stereoisomer across all cyclic imide small molecule ligands and engineered proteins, we find that, in some cases, the (R)-stereoisomer can bind to CRBN, either enhancing or hindering the eventual target engagement and degradation. Lenalidomide and its derivatives racemize more rapidly (t 50%ee = 4-5 h) than the C-terminal cyclic imide under non-enzymatic conditions. These findings highlight that although the (S)-stereoisomer of the cyclic imide is the primary ligand for the thalidomide-binding domain of CRBN, the (R)-stereoisomer, if present, has the potential to contribute to CRBN-dependent cellular activity.

MACE-OFF: Short-Range Transferable Machine Learning Force Fields for Organic Molecules

Classical empirical force fields have dominated biomolecular simulations for over 50 years. Although widely used in drug discovery, crystal structure prediction, and biomolecular dynamics, they generally lack the accuracy and transferability required for first-principles predictive modeling. In this paper, we introduce MACE-OFF, a series of short-range transferable force fields for organic molecules created using state-of-the-art machine learning technology and first-principles reference data computed with a high level of quantum mechanical theory. MACE-OFF demonstrates the remarkable capabilities of short-range models by accurately predicting a wide variety of gas- and condensed-phase properties of molecular systems. It produces accurate, easy-to-converge dihedral torsion scans of unseen molecules as well as reliable descriptions of molecular crystals and liquids, including quantum nuclear effects. We further demonstrate the capabilities of MACE-OFF by determining free energy surfaces in explicit solvent as well as the folding dynamics of peptides and nanosecond simulations of a fully solvated protein. These developments enable first-principles simulations of molecular systems for the broader chemistry community at high accuracy and relatively low computational cost.

Modeling infrared and vibrational circular dichroism spectra of complex systems: the DFTB/fluctuating charges route

Simulating vibrational spectra of large biomolecular systems in aqueous environments remains a challenge in computational chemistry due to the complex interactions between solutes and solvents. In this study, we employ the density functional tight-binding (DFTB) method, coupled with the fluctuating charges (FQ) force field, to simulate infrared (IR) and vibrational circular dichroism (VCD) spectra of solvated large biomolecules. We focus on three representative systems: the doxorubicin/DNA intercalation complex, ubiquitin, and hen egg white lysozyme. By using molecular dynamics (MD) trajectories to sample the conformational space, we compute spectra for multiple snapshots, employing different DFTB Hamiltonians, including SCC-DFTB, DFTB3, and GFN1-xTB. Our results demonstrate the accuracy and computational efficiency of the DFTB/FQ method in reproducing experimental spectral features, particularly for large, solvated systems which cannot be afforded by other ab initio methodologies. The results of this work highlight the potential of DFTB/FQ as a scalable method for simulating vibrational properties in complex molecular systems.

Data-Driven Kinetic Reaction Networks for Separation Chemistry

Understanding complex, multistep chemical reactions at the molecular level is a major challenge whose solution would greatly benefit the design and optimization of numerous chemical processes. The separation of rare-earth (4f) and actinide (5f) elements is an example where improving our chemical understanding is important for designing and optimizing new chemistries, even with a limited number of observations. In this work, we leverage data-driven artificial intelligence and machine-learning approaches to develop kinetic reaction networks that describe the liquid-liquid extraction mechanism of uranium using N,N-di-2-ethylhexyl-isobutyramide (DEHiBA). Specifically, we compare and contrast the properties of two classes of models: (1) purely data-driven models that are regularized using chemistry-agnostic, L1 regression and (2) chemistry-informed models that are regularized using relative reaction energies provided by quantum mechanical calculations. We observe that purely data-driven models are unbiased, simple, and accurate in their predictions of experimental measurements when provided with sufficient data but are difficult to fully constrain and interpret. In contrast, chemistry-informed models exhibit significantly improved chemical interpretability and consistency, providing a detailed description of the separation process while achieving high accuracy through ensemble averaging. Overall, the dominant species predicted to be extracted into the organic phase is UO2(NO3)2(DEHiBA)2, agreeing with experimental slope analysis, thermodynamic modeling, EXAFS, and crystal structures. This work demonstrates that leveraging the fundamental structure of the problem can lead to efficient learning schemes that provide both accurate predictions and chemical insights at a low computational cost.

Moltiverse: Molecular Conformer Generation Using Enhanced Sampling Methods

Accurately predicting the diverse bound-state conformations of small molecules is crucial for successful drug discovery and design, particularly when detailed protein-ligand interactions are unknown. Established tools exist, but efficiently exploring the vast conformational space remains challenging. This work introduces Moltiverse, a novel protocol using enhanced sampling molecular dynamics (MD) simulations for conformer generation. The extended adaptive biasing force (eABF) algorithm combined with metadynamics, guided by a single collective variable (radius of gyration, RDGYR), efficiently samples the conformational landscape of a small molecule. Moltiverse demonstrates comparable accuracy and, in some cases, superior quality when benchmarked against established software like RDKit, CONFORGE, Balloon, iCon, and Conformator in the Platinum Diverse Data set for drug-like small molecules and the Prime data set for macrocycles. We present multiple quantitative metrics and statistical analysis for robust conformer generation algorithm comparisons and provide recommendations for their improvement based on our findings. Our extensive evaluation shows that Moltiverse is particularly effective for challenging systems with high conformational flexibility, such as macrocycles, where it achieves the highest accuracy among the tested algorithms. The physics-based approach employed by Moltiverse effectively handles a wide range of molecular complexities, positioning it as a valuable tool for computational drug discovery workflows requiring accurate representation of molecular flexibility.

Probing intermediate folding patterns determined the carbon skeleton construction mechanism of cyathane diterpene

Cyathane diterpenes exhibit a range of notable pharmacological activities. These compounds share a common skeleton, the cyathin tricyclic core, whose synthesis is intricate, involving carbon cation rearrangement that results in the formation of three carbon rings and multiple stereocenters. Through DFT calculations, we found that the folding pattern of intermediates significantly impacts the reaction. Firstly, the A ring adopts a chair-like conformation, which is more favorable than the boat-like conformation. Secondly, a hydrogen atom attached to the terminal double bond can adopt either an up or down conformation, leading to different mechanisms for B expansion and C ring formation: concerted or stepwise, respectively. The stepwise mechanism, induced by the up conformation, is energetically more favorable than the down conformation. Further analysis of bond order, key distances and natural bond orbital revealed that the transition from the concerted mechanism to the stepwise mechanism is due to van der Waals repulsion between two H atoms attached to the reactive carbons involved in C ring formation. Finally, during QM(GFN2-xTB)/MM MD simulations, it was observed that the A ring transitions from a boat-like conformation to a chair-like conformation, and the H-down conformation switches to the H-up conformation within the cyathane synthase pocket. These transitions are consistent with the preferences observed in gas-phase calculations. This research reveals that distinct conformations give rise to different reaction mechanisms, an intriguing finding that provides deeper insight into the biosynthetic pathways of natural compounds and offers theoretical guidance for their biomimetic synthesis.

Hybrid Functional DFTB Parametrizations for Modeling Organic Photovoltaic Systems

Density functional tight binding (DFTB) is a quantum chemical simulation method based on an approximate density functional theory (DFT), known for its low computational cost and comparable accuracy to DFT. For several years, the application of DFTB in organic photovoltaics (OPV) has been limited by the absence of an appropriate set of parameters that adequately account for the relevant elements and necessary corrections. Here we have developed new parametrizations using hybrid functionals, including B3LYP and CAM-B3LYP, for OPV applications within the DFTB method in order to overcome the self-interaction error present in DFT functionals lacking long-range correction. These parametrizations encompass electronic and repulsive parameters for the elements H, C, N, O, F, S, and Cl. A Bayesian optimization approach was employed to optimize the free atom eigenenergies of unoccupied shells. The effectiveness of these new parametrizations was evaluated by a data set of 12 OPV donor and acceptor molecules, showing consistent performance when compared with their corresponding DFT references. Frontier molecular orbitals and optimized geometries were examined to evaluate the performance of the new parametrizations in predicting ground-state properties. Furthermore, the excited-state properties of monomers and dimers were investigated by means of real-time time-dependent DFTB (real-time TD-DFTB). The appearance of charge-transfer (CT) excitations in the dimers was observed, and the influence of alkyl side-chains on the photoinduced CT process was explored. This work paves the way for studying ground- and excited-state properties, including band alignments and CT mechanisms at donor-acceptor interfaces, in realistic OPV systems.

Multiobjective Evolutionary Strategy for Improving Semiempirical Hamiltonians in the Study of Enzymatic Reactions at the QM/MM Level of Theory

Quantum mechanics/molecular mechanics (QM/MM) simulations are crucial for understanding enzymatic reactions, but their accuracy depends heavily on the quantum-mechanical method used. Semiempirical methods offer computational efficiency but often struggle with accuracy in complex systems. This work presents a novel multiobjective evolutionary strategy for optimizing semiempirical Hamiltonians, specifically designed to enhance their performance in enzymatic QM/MM simulations while remaining broadly applicable to condensed-phase systems. Our methodology combines automated parameter optimization, targeting ab initio or density functional theory (DFT)-reference potential energy surfaces, atomic charges, and gradients, with comprehensive validation through minimum free energy path (MFEP) calculations. To demonstrate its effectiveness, we applied our approach to improve the GFN2-xTB Hamiltonian using two enzymatic systems that involve hydride transfer reactions where the activation energy barrier is severely underestimated: Crotonyl-CoA carboxylase/reductase (CCR) and dihydrofolate reductase (DHFR). The optimized parameters showed significant improvements in reproducing potential and free energy surfaces, closely matching higher-level DFT calculations. Through an efficient two-stage optimization process, we first developed parameters for CCR using reaction path data, then refined these parameters for DHFR by incorporating a targeted set of additional training geometries. This strategic approach minimized the computational cost while achieving accurate descriptions of both systems, as validated through QM/MM simulations using the Adaptive String Method (ASM). Our method represents an efficient approach for optimizing semiempirical methods to study larger systems and longer time scales, with potential applications in enzymatic reaction mechanism studies, drug design, and enzyme engineering.

Predicting the Brain-To-Plasma Unbound Partition Coefficient of Compounds via Formula-Guided Network

Blood-brain barrier (BBB) permeability plays a crucial role in determining drug efficacy in the brain, with the brain-to-plasma unbound partition coefficient (Kp,uu) recognized as a key parameter of BBB permeability in drug development. However, Kp,uu data are scarce and mostly in-house. In predicting Kp,uu the generality and applicability of existing empirical scoring models remain underexplored. To address this, we established a public rat Kp,uu data set through data mining and developed a formula-guided deep learning model, CMD-FGKpuu, which performed well on multiple benchmark tests, marking good demonstration of the potential of deep learning for Kp,uu prediction. Additionally, the model can be fine-tuning with project-specific experimental data, thus improving its practical utility. The findings offer an effective tool for predicting BBB permeability in drug development and introduce a new perspective for applying few-shot learning in the pharmaceutical field.

Non-Aqueous Binary and Ternary nHF·Base Fluoride Reagents: Characterization of Structure, Properties, and Reactivity

Binary and ternary nHF·base mixtures are an important class of nucleophilic fluorinating reagents used in myriad fluorination reactions. These reagents are soluble in organic media, and by varying n, the reactivity of fluoride can be controlled and tuned. Of particularly frequent utility are the ternary mixtures of nHF·amine, in which the binary 9HF·py and 3HF·NEt3 mixtures are combined, the ratio (n) of which has a strong influence on the reaction yields and selectivity. The structure, properties, and reactivity of these non-aqueous ionic liquid mixtures vary considerably with n. Herein, we disclose a combined experimental and theoretical study aimed at characterizing binary and ternary nHF·base mixtures. We have measured the concentration of components, their Hammett acidity H0, nucleophilicity, and basicity, while using theory to calculate the lowest energy size and structure of the clusters formed at different ratios of HF to base and analyzed the noncovalent interactions present. The quantification of properties and enhanced understanding presented should facilitate the further development and use of this important family of fluorination reagents.

Applying Active Learning toward Building a Generalizable Model for Ni-Photoredox Cross-Electrophile Coupling of Aryl and Alkyl Bromides

When developing machine learning models for yield prediction, the two main challenges are effectively exploring condition space and substrate space. In this article, we disclose an approach for mapping the substrate space for Ni/photoredox-catalyzed cross-electrophile coupling of alkyl bromides and aryl bromides in a high-throughput experimentation (HTE) context. This model employs active learning (in particular, uncertainty querying) as a strategy to rapidly construct a yield model. Given the vastness of substrate space, we focused on an approach that builds an initial model and then uses a minimal data set to expand into new chemical spaces. In particular, we built a model for a virtual space of 22,240 compounds using less than 400 data points. We demonstrated that the model can be expanded to 33,312 compounds by adding information around 24 building blocks (<100 additional reactions). Comparing the active learning-based model to one constructed on randomly selected data showed that the active learning model was significantly better at predicting which reactions will be successful. A combination of density function theory (DFT) and difference Morgan fingerprints was employed to construct the random forest model. Feature importance analysis indicates that key DFT features that are related to the reaction mechanism (e.g., alkyl radical LUMO energy) were crucial for model performance and predictions on aryl bromides outside the training set. We anticipate that combining DFT featurization and uncertainty-based querying will help the synthetic organic community build predictive models in a data-efficient manner for other chemical reactions that feature large and diverse scopes.

Probing Host-Guest Interactions of the Dual Anion Receptor Harmane with Halide and HSO4- Anions

Harmane is a polycyclic amine that can recognize F- and HSO4- via the ═N-H or ≡N binding site. The active binding site depends on whether the solvent is protic or aprotic, but the underlying molecular mechanism remains unclear. As a first step toward obtaining such mechanisms in solutions, we investigated the interactions of harmane with halide anions (F-, Cl-, Br-, and I-) and HSO4- in the gas phase using negative ion photoelectron spectroscopy combined with theoretical calculations. The adiabatic/vertical detachment energies for deprotonated harmane and harmane·X- (X = F, Cl, Br, I, and HSO4) were determined to be 2.72/2.79, 3.25/3.38, 4.19/4.43, 4.35/4.40, 3.93/3.99, and 4.49/4.75 eV, respectively, with an uncertainty of ±0.05 eV. All the X- anions were found to form hydrogen bonds with harmane through the ═N-H site. A nearly complete proton transfer was observed within the harmane·F- complex anion. Larger halide anions in other harmane-halide complexes remain relatively intact. Four closely lying isomers of harmane·HSO4- were identified. The photodetachment locations of the harmane complex anions were also revealed by electronic state calculations and molecular orbital analyses. The current work lays out a foundation for future work on microsolvated clusters to probe how solvent molecules influence the harmane-anion binding motif.

Development of Diaryl Hydrazones for Alleviation of Mitochondrial Oxidative Stress in Preeclampsia

Preeclampsia is a pregnancy-specific syndrome, linked to oxidative stress, affecting 5-8% of pregnancies, with no effective treatment available. Here, diaryl-hydrazones have been designed, synthesized, and investigated as mitochondria-targeting antioxidants to reduce placental oxidative stress and mitigate preeclampsia symptoms. The design, based on density functional theory studies, revealed that conjugated electron structure with the NH-motif appeared to explain their effect. Thirty compounds were synthesized and tested in three assays, where they exhibited excellent radical scavenging activity, significantly greater than that of the standard, Trolox. Based on the data, eight compounds were selected for cell-based assays. Oxidative stress was induced in human trophoblast cells and assessed whether the compounds reduced downstream antiangiogenic responses using ascorbic acid and MitoTEMPO as standards. The pretreatment with the hydrazones reduced mitochondrial superoxide and sFLT-1 production in H2O2-exposed trophoblast cells, indicating that mitochondrial oxidative stress and the anti-angiogenic response can be alleviated by these compounds.

Systematic improvement of redox potential calculation of Fe(III)/Fe(II) complexes using a three-layer micro-solvation model

Electrochemical transformations of metal ions in aqueous media are challenging to model accurately due to the dynamic solvation structure surrounding ions at different charge states. Predictive modeling at the atomistic scale is essential for understanding these solvation architectures but is often computationally prohibitive. In this contribution, we present a simple, fast, and accurate three-layer micro-solvation model to evaluate the redox potential of metal ions in aqueous solutions. Our model, developed and validated for Fe3+/Fe2+ redox potentials, combines the DFT-based geometry optimizations of the octahedral Fe complex with two layers of explicit water molecules to capture solute-solvent interactions and an implicit solvation model to account for bulk solvent effects. This approach yields accurate predictions for Fe3+/Fe2+ redox potentials in water, achieving errors of 0.02 V with ωB97X-V, 0.01 V with ωB97X-D3, 0.04 V with ωB97M-V, and 0.02 V with B3LYP-D3 functionals. We further demonstrate the generality of our model by applying it to additional metal complexes, including the challenging Fe(CN)63-/4- system, where our model successfully achieves close agreement with experimental values, with an error of 0.07 V and an average error of 0.21 V for all five systems. In summary, the presented simple solvation model has broad applicability and potential for enhancing computational efficiency in redox potential predictions across various chemical and industrial processes of metal ions.

Intramolecular proton bonding in aliphatic dicarboxylate anions: dynamic conformational landscapes and spectral signatures

Carboxylate moieties are central to organic chemistry and a main driving force of biomolecular recognition. Their diffuse anionic structure is prone to building proton-mediated supramolecular bonds with a marked delocalization of charge, a chemical motif that is key to processes of proton storage and transfer. We investigate intramolecular proton bonding interactions in benchmark aliphatic dicarboxylate anions of the form HOOC(CH2)n-2COO- (n = 4-8), hence succinate, glutarate, adipate, pimelate and suberate. Infrared ion spectroscopy is employed to expose the vibrational fingerprints of the mass-selected anions isolated at room temperature. Ab initio molecular dynamics calculations are applied to rationalize the fluxional character of the shared proton and its impact on the cyclic structure adopted by the anion. Our findings indicate that anions with shorter alkyl chains are constrained to symmetrically share protons in anti-anti configurations of the carboxylate moieties. Longer chain lengths increase the conformational flexibility of the alkyl backbone and stabilize anti-syn configurations with asymmetric proton sharing. As a result, the vibrational spectrum evolves towards progressively more differentiated carboxylic and carboxylate stretching modes. In all systems, the dynamic character of the proton bond can be recognized through a characteristic broad O-H stretching band that narrows down and blue shifts as proton delocalization is reduced in the larger anions.

Photoinduced energy and electron transfer processes in a supramolecular system combining a tetrapyrenylporphyrin derivative and arene-ruthenium metalla-prisms

A supramolecular system, consisting of a tetrapyrenylporphyrinic core surrounded by arene-ruthenium prisms, has been assembled and characterized by means of electrochemical and photophysical techniques. The photophysical study shows that quantitative energy transfer from the peripheral pyrenyl units towards the central porphyrin core is operative in the tetrapyrenylporphyrinic system. Interestingly, encapsulation of the pyrenyl units into the ruthenium cages affects the photophysics of the central porphyrin component, since its emission quantum yield is reduced in the supramolecular array. Femtosecond transient absorption analysis evidenced a complex interplay of deactivation pathways, including energy and electron transfer processes from the porphyrin to the metalla-prisms, associated with different conformations of the system allowed by the flexibility of the linkers. Moreover, the non-emissive arene-ruthenium cages present a peculiar excited-state dynamics, here disentangled for the first time by means of transient absorption investigations.

Gas Phase Mass- and Mobility-Resolved Structures of Metalated Glyphosate Dimers

Metalated glyphosate dimers were investigated by using electrospray ionization ion mobility-mass spectrometry and tandem ion mobility-infrared multiple photon dissociation-mass spectrometry. [M(glyphosate)(glyphosate-H)]+ dimers where M = Mg2+, Ca2+, Sr2+, Ba2+, Mn2+, Cu2+ and Zn2+ were mass-selected prior to mobility separation. Each possessed a single mobility resolved isomer, with measured collision cross sections (N2CCSexp) ranging from 165 to 175 Å2. The dimers were all of similar size, with size trends consistent with periodic differences in the incorporated metals' cationic radii, except M = Cu2+. Upon IR irradiation between 2700 and 3700 cm-1, the experimental IR spectra of mass- and mobility-resolved [M(glyphosate)(glyphosate-H)]+ dimers revealed two significant absorption peaks at 3550 and 3660 cm-1. These correspond to the O-H stretching on both the carboxylate and phosphonate groups of the substituent glyphosate molecules. A thorough isomer search using CREST-CENSO algorithms and DFT optimization predicted the energetically preferred gas-phase structures of [M(glyphosate)(glyphosate-H)]+ dimers. Comparing calculated collision cross sections (N2CCScalc) and predicted vibrational frequencies with experimental data confirmed the predicted structures of the [M(glyphosate)(glyphosate-H)]+ dimers, which all share a common structural motif. In all cases, the incorporated deprotonated glyphosate is deprotonated at the phosphonate group. The divalent metal cation coordinates the deprotonated phosphonate group in a bidentate fashion and is located in the center of the dimer. The neutral glyphosate molecule is wrapped around the metal cation in an octahedral coordination. As the metal cation increases in size, the coordination distance increases, thereby increasing the overall size of the dimer. The different bonding afforded by M = Cu2+ to the amine nitrogen center leads to the observed structural difference for this metal, through the modulation of a key hydrogen bond in the dimer.

Proximity-Induced Fluorescence Quenching in Rhodamine Systems In Vacuo: Effect of Charges and Aromatic Moieties

FRET is a valuable technique for exploring conformations of macromolecules in solution and in the gas phase. Donor fluorescence quenching is often identified from shortened excited-state lifetimes. However, when dyes are incorporated into proteins, the local microenvironment can affect the photophysics and energy transfer. To examine the effect of nearby charges and aromatic moieties on lifetimes, we investigated different cationic rhodamine-575 model systems in vacuo. In homodimers, the internal Coulomb repulsion induces a distance-dependent increase in lifetime, ranging from 5.90 ns (single dye) to 6.78 ns (shortest interdye linker), which we attribute to reduced oscillator strengths as corroborated by TD-DFT calculations. Our findings highlight that excited-state lifetimes are not necessarily directly correlated with fluorescence quantum yields, contrasting typical intuition. We discuss different quenching mechanisms and compare with results obtained in solution, as the opposite effect is observed in methanol, where excited-state lifetimes decrease upon bringing the dyes closer together. In the case of homotrimers in the gas phase, lifetimes systematically decrease with the number of nonprotonated (neutral) dyes. This suggests enhanced nonradiative decay rates driven by strong interactions between dyes and nearby aromatic moieties.

Synergistic interaction of amphotericin B and betulinic acid against clinically important fungi: evidence from in vitro and in silico techniques

Betulinic acid (BA), in combined application with amphotericin B, shows a synergistic effect against Candida, Aspergillus, Scedosporium, Fusarium, and Mucorales fungi at a concentration as low as 0.125 µg/mL. Amphotericin B showed slightly higher affinity towards BA than toward ergosterol, according to our in silico molecular docking results, explaining the observed Eagle effect. Moreover, it can bind both molecules simultaneously, suggesting the possibility of the formation of mixed pores, thus increasing the membrane-disrupting activity.IMPORTANCEThe rising incidence of invasive fungal infections, coupled with the emergence of antifungal resistance, presents a significant challenge in clinical settings. The inherent resistance of certain fungi to conventional antifungal agents, alongside the limitations posed by side effects and drug interactions, necessitates the exploration of alternative therapeutic strategies. This study highlights the potential of combining amphotericin B (AmB) with betulinic acid (BA) to enhance antifungal efficacy against clinically relevant pathogens, including Candida albicans and Aspergillus fumigatus, as well as mucormycosis-causing fungi. The results demonstrate the synergistic interactions between AmB and BA, which effectively inhibited fungal growth at lower concentrations and are within reported serum levels. In silico molecular docking studies further support the hypothesis that BA may facilitate AmB's mechanism of action, potentially leading to increased pore formation in fungal membranes.

Vibrationally Resolved Photoelectron Spectroscopy and Quantum Chemical Calculations of InCn- (n = 3-10) Clusters

The vibrationally resolved photoelectron spectra of InCn- (n = 3-10) clusters were measured using size-selected anion photoelectron spectroscopy. The structures of InCn-/0 (n = 3-10) clusters were investigated through quantum chemical calculations. A pronounced odd-even alternation in vertical detachment energies (VDEs) of InCn- (n = 3-10) is observed as the carbon chain length increases. The InCn- (n = 4, 6, 8, and 10) anions exhibit higher VDEs than their adjacent anions with odd carbon numbers. The most stable structures of InCn- and InCn (n = 3-10) adopt C∞v symmetric linear structures with the indium atom located at the end of the carbon chain, except for InC10. InC10 exhibits a C10 ring structure with the indium atom bonding externally to one carbon atom, which is slightly more stable than the linear structure. Bonding analysis reveals the monovalent nature of the indium atom and the covalent character of the In-C bond. Finally, the limitations of the CCSD(T) method in achieving high-accuracy calculations for this system are discussed.

Simple Hückel Molecular Orbital Theory for Möbius Carbon Nanobelts

The recently synthesized Möbius carbon nanobelts (CNBs) have gained attention owing to their unique π-conjugation topology, which results in distinctive electronic properties with both fundamental and practical implications. Although Möbius conjugation with phase inversion in atomic orbital (AO) basis is well-established for monocyclic systems, the extension of this understanding to double-stranded Möbius CNBs remains uncertain. This study thoroughly examines the simple Hückel molecular orbital (SHMO) theory for describing the π electronic structures of Möbius CNBs. We demonstrate that the adjacency matrix for Möbius CNB can preserve its eigenvalues and eigenvectors under different placements of the sign inversion, ensuring identical SHMO results regardless of AO phase inversion location. Representative examples of Möbius CNBs, including the experimentally synthesized one, show that the Hückel molecular orbitals (MOs) strikingly resemble the DFT-computed π MOs, which were obtained using a herein proposed technique based on the localization and redelocalization of DFT canonical MOs. Interestingly, the lower-lying π MOs exhibit an odd number of nodal planes and are doubly quasidegenerate as a consequence of the phase inversion in Möbius macrocycles, contrasting with macrocyclic Hückel systems. Coulson bond orders derived from SHMO theory correlate well with DFT-calculated Wiberg bond indices for all C-C bonds in tested Möbius CNBs. Additionally, a remarkable correlation is observed between HOMO-LUMO gaps obtained from the SHMO and GFN2-xTB calculations for a large number of topoisomers of Möbius CNBs. Thus, the SHMO model not only captures the essence of π electronic structure of Möbius CNBs, but also provides reliable quantitative predictions comparable to DFT results.

In situ construction of a Li-Ag&LiF interface enables stable cycling of all-solid-state lithium-metal batteries

Solid polymer electrolytes (SPEs) are regarded as promising candidates for developing high energy-density Li metal batteries because of their flexible processability and low cost. However, the application of SPEs is still inherently impeded by the mediocre ionic conductivity and unstable Li/electrolyte interface. In this work, the silver fluoride (AgF) additive is introduced to optimize the ionic conductivity of PEO and induce the formation of stable solid electrolyte interphase (SEI) layer between Li metal and SPEs interface, thereby inhibiting the growth of lithium dendrites. AgF can be complex with anions to promote the dissociation of Li+-TFSI- ion pairs, improving the mobility of Li+ ions, as confirmed by experimental and computational studies. Moreover, the AgF converses to LiF and Li-Ag alloys via in-situ electrochemical reaction with Li anode, which can not only prevent the Li metal from parasitic reactions, but also reduce the concentration gradient of Li+ ions. Hence, the Li|Li symmetric cell containing PEO-3 % AgF electrolyte demonstrates stable cyclability for 1800 h at 0.2 mA cm-2 (60 °C). When paired with a commercial LiFePO4 cathode, the resulting all-solid-state Li-metal battery delivers remarkable cyclic performance of 126.9 mAh g-1 after 400 cycles (0.5 C). This work provides a new approach for the development of composite solid-state electrolyte films and lithium metal anodes in all-solid-state batteries.

TrIP2: Expanding the Transformer Interatomic Potential Demonstrates Architectural Scalability for Organic Compounds

TrIP2 is an advanced version of the transformer interatomic potential (TrIP) trained on the expanded ANI-2x data set, including more diverse molecular configurations with sulfur, fluorine, and chlorine. It leverages the equivariant SE(3)-transformer architecture, incorporating physical biases and continuous atomic representations. TrIP was introduced as a highly promising transferable interatomic potential, which we show here to generalize to new atom types with no alterations to the underlying model design. Benchmarking on COMP6 energy and force calculations, structure minimization tasks, torsion drives, and applications to molecules with unexpected conformational energy minima demonstrates TrIP2's high accuracy and transferability. Direct architectural comparisons demonstrate superior performance against ANI-2x, while holistic model evaluations─including training data and level-of-theory considerations─show comparative performance with state-of-the-art models like AIMNet2 and MACE-OFF23. Notably, TrIP2 achieves state-of-the-art force prediction performance on the COMP6 benchmarks and closely approaches DFT-optimized structures in torsion drives and geometry optimization tasks. Without requiring any architectural modifications, TrIP2 successfully capitalizes on additional training data to deliver enhanced generalizability and precision, establishing itself as a robust and scalable framework capable of accommodating future expansions or applications to new domains with minimal reengineering.

IMPRESSION generation 2 - accurate, fast and generalised neural network model for predicting NMR parameters in place of DFT

Predicting 3D-aware Nuclear Magnetic Resonance (NMR) properties is critical for determining the 3D structure and dynamics, both stereochemical and conformational, of molecules in solution. Existing tools for such predictions are limited, being either relatively slow quantum chemical methods such as Density Functional Theory (DFT), or niche parameterised empirical or machine learning methods that only predict a single parameter type, often across only a limited chemical space. We present here IMPRESSION-Generation 2 (G2), a transformer-based neural network which can be used as a much faster alternative to high level DFT calculations in computational workflows of multiple classes of NMR parameter simultaneously, with time-savings of several orders of magnitude. IMPRESSION-G2 is the first system that simultaneously predicts all NMR chemical shifts, as well as scalar couplings for 1H, 13C, 15N and 19F nuclei up to 4 bonds apart, in a single prediction event starting from a 3D molecular structure. Rapid NMR predictions take <50 ms to predict on average ∼5000 chemical shifts and scalar couplings per molecule, which is approximately 106-times faster than DFT-based NMR predictions starting from a 3D structure. When combined with fast GFN2-xTB geometry optimisations to generate the 3D input structures themselves in just a few seconds, a complete workflow for NMR predictions on a new molecule is 103-104 times faster than a wholly DFT-based workflow for this. The accuracy of this multi-parameter predictor in reproducing DFT-quality results for a wide chemical space of organic molecules up to ∼1000 g mol-1 containing C, H, N, O, F, Si, P, S, Cl, Br exceeds that of existing state-of-the-art empirical or machine learning systems (∼0.07 ppm for 1H chemical shifts, ∼0.8 ppm for 13C chemical shifts, <0.15 Hz for 3 J HH scalar coupling constants) and, critically, it also demonstrates generalisability when tested against molecules from sources that are completely independent of its own training data. When compared to experimental NMR data for ∼5000 compounds, IMPRESSION-G2 gives results in minutes on a standard laptop which are almost indistinguishable from DFT results that took days on a large scale High Performance Computing system. This accuracy and speed of IMPRESSION-G2 coupled to GFN-xTB shows that it can be used to simply replace DFT for predicting 3D-aware NMR parameters inside the wide chemical space of its training data.

Two-dimensional electronic spectroscopy of Betaine-30

Betaine-30 is well-established as a standard dye for solvatochromism and has long been studied by ultrafast spectroscopy. Electronic excitation leads to rapid intramolecular electron transfer, while the decay of the resulting state corresponds to back electron transfer to the electronic ground state. Thus, Betaine-30's photophysics offers a route to probing the role that vibrational excitation and solvent dynamics play in electron transfer reaction rates. Here, we probe the excited state dynamics of Betaine-30 in two solvents (ethanol and acetonitrile) by means of two-dimensional electronic spectroscopy. Population dynamics in ethanol are measured at two pump wavelengths, and global analysis reveals a wavelength dependence of the electron transfer rate. This is assigned to excitation of distinct ground state conformers, which is confirmed by quantum chemical calculations. "Beatmaps" of coherently excited vibrations are recovered and analyzed in terms of the contribution of Raman active modes in ground and excited states. The contribution of modes in the excited state is a strong function of the rate of the electron transfer reaction.

A computational study on the photophysics of methylpheophorbide a

Pheophorbide a is a dephytylation and demetallation product of chlorophyll a isolated from plants and algae. Pheophorbide a has been used as a photosensitizer to treat microbes, cancer and multidrug resistance. Methylpheophorbide a (MPh) or its methyl ester is another photosensitizer with interesting photophysical properties such as stronger absorption at longer wavelengths compared to the absorption of porphyrins and a high singlet oxygen production quantum yield (ΦΔ = 0.62). To gain deeper insight into the photophysics of MPh, a computational protocol was employed that allows the elucidation of the photophysical properties of methylpheophorbide a (MPh). This protocol uses Fermi's golden rule within a path integral formalism. Time-dependent density functional theory (TD-DFT) calculations at the CAM-B3LYP/def2-SVP(C-PCM) level of theory were performed. Our calculations reproduce acceptably well the vibronic structure of the Q-band of the absorption spectrum of MPh. After photoexcitation, MPh can decay to the ground state via fluorescence or it can undergo intersystem crossing. Three triplet excited states (T1, T2 and T3) are found below the S1 state with an overall spin-vibronic ISC rate constant of 6.14 × 107 s-1, in good agreement with the experimental value of 7.90 × 107 s-1. The calculated fluorescence rate is approximately five times higher than the experimental value, which can be attributed to an overestimation of the adiabatic energy of the S1 state and to the inherent limitations of the approach employed. Consistent with the experimentally observed behavior, our calculations predict that MPh is not phosphorescent.

Supramolecular Polyanions as Effective Interphase Layers for Anode-Free Lithium Metal Batteries

Supramolecular polymers (SPs) driven by host-guest interactions have seen considerable advancements over the past decade, but the guest species involved are predominantly neutral molecules or cations. Strong and dependable macrocycle-anion interactions for supramolecular polymerization are highly sought after, yet the functionalities of this class of SPs featuring negatively charged backbones remain largely unexplored. Here, we report a novel host-guest interaction between one tetraurea macrocycle and two organophosphate anions with high affinity (association constant Ka = 1.06 × 109 M-2). It can serve as noncovalent joints to efficiently link low-molecular-mass polyethylene oxide (PEO) diorganophosphates into long linear SPs. Being used as an interphase layer in anode-free lithium (Li) metal batteries, the polymer material promotes Li+ absorption and desolvation through electrostatic attractions and enables fast Li+ transport by the alternatingly arranged anionic linkages and PEO units in the polymer main chain. As a result, the SP layer homogenizes Li+ flux to achieve uniform Li deposition and significantly improves the electrochemical performance of LiFePO4 full batteries by a factor of 285% with cycling stability over 200 cycles. These findings pave the way for a new family of anion-based SPs with tunable architectures and compositions and suggest their promising applications in future batteries.

Shadow Molecular Dynamics for a Charge-Potential Equilibration Model

We introduce a shadow molecular dynamics (MD) approach based on the Atom-Condensed Kohn-Sham second-order (ACKS2) charge-potential equilibration model. In contrast to regular flexible charge models, the ACKS2 model includes both flexible atomic charges and potential fluctuation parameters that allow for physically correct charge fragmentation and improved scaling of the polarizability. Our shadow MD scheme is based on an approximation of the ACKS2's flexible charge-potential energy function, in combination with extended Lagrangian Born-Oppenheimer MD. Utilizing this shadow charge-potential equilibration approach mitigates the costly overhead and stability problems associated with finding well-converged iterative solutions to the charges and potential fluctuations of the ACKS2 model in an MD simulation. Our work provides a robust and versatile framework for efficient, high-fidelity MD simulations of diverse physical phenomena and applications.

Multiscale Embedding for Quantum Computing

We present a novel multiscale embedding scheme that links conventional QM/MM embedding and bootstrap embedding (BE) to allow simulations of large chemical systems on limited quantum devices. We also propose a mixed-basis BE scheme that facilitates BE calculations on extended systems using classical computers with limited memory resources. Benchmark data suggest the combination of these two strategies as a robust path in attaining the correlation energies of large realistic systems, combining the proven accuracy of BE with chemical and biological systems of interest in a lower computational cost method. Due to the flexible tunability of the resource requirements and systematic fragment construction, future developments in the realization of quantum computers naturally offer improved accuracy for multiscale BE calculations.

Development of Parallel On-the-Fly Crystal Algorithm for Reaction Discovery in Large and Complex Molecular Systems

The parallel on-the-fly Crystal algorithm is a new, efficient global search algorithm for exploring single-state potential energy surfaces and conical intersection seam spaces of a wide range of molecules. Despite major developments, its application to complex molecular systems, especially in the condensed phase, remains challenging due to the high dimensionality of the configurational space. In this work, we address this challenge and extend its applicability to the reaction discovery of large and complex molecular photoswitches in various molecular environments, including in the condensed phase with explicit solvent molecules. This is achieved by performing an explicit exploration of a comparatively large Crystal configurational subspace, while gradually relaxing the remaining degrees of freedom. The new Crystal algorithm is applied to the reaction discovery of bilirubin and donor-acceptor Stenhouse adducts, a next-generation class of molecular photoswitches, in vacuum and in the aqueous solution. To this end, we designed an automated and systematic workflow for Crystal to discover and characterize new minima and low-energy reaction pathways in these challenging and complex systems. Our findings demonstrate the algorithm's effectiveness in quickly exploring the configuration space and uncovering kinetically accessible products, offering new insights into the intricate chemical reactivities of these molecules and the roles of molecular environments on the reaction pathways. The results underscore the promising potential of parallelized global exploration methods for reaction discovery in biomolecular systems.

The Structural Stability of Enzymatic Proteins in the Gas Phase: A Comparison of Semiempirical Hamiltonians and the GFN-FF

The study of the gas-phase behavior of proteins has recently gained momentum due to numerous prospective applications in, e.g., the construction of molecular sensors or nano-machines. The study of proteins outside their standard water environment, necessary to arrive at their successful applied use, is, however, limited by the loss of the structure and function of the macromolecules in the gas phase. We selected two enzymatic proteins with great potential for applied use, the digestive enzyme trypsin and the cytochrome sterol demethylase, for which to develop gas-phase structural models. The employed levels of theory were semiempirical, density functional tight binding, and polarizable force-field techniques. The convergence of the self-consistent field equations was very slow and in most cases led to oscillatory behavior, encouraging careful tuning of the convergence parameters. The structural optimization and molecular dynamics simulations indicated the parts of the proteins most prone to structural distortion under gas-phase conditions with unscreened electrostatics. This problem was more pronounced for cationic trypsin, for which the stability of the simulation was lower. The fate of the hydrogen bonding network of the catalytic triad in the gas phase was also investigated.

Symmetry-constrained generation of diverse low-bandgap molecules with Monte Carlo tree search

Organic optoelectronic materials are a promising avenue for next-generation electronic devices due to their solution processability, mechanical flexibility, and tunable electronic properties. In particular, near-infrared (NIR) sensitive molecules have unique applications in night-vision equipment and biomedical imaging. Molecular engineering has played a crucial role in developing non-fullerene acceptors (NFAs) such as the Y-series molecules, which feature a rigid fused-ring electron donor core flanked by electron-deficient end groups, leading to strong intramolecular charge-transfer and extended absorption into the NIR region. However, systematically designing molecules with targeted optoelectronic properties while ensuring synthetic accessibility remains a challenge. To address this, we leverage structural priors from domain-focused, patent-mined datasets of organic electronic molecules using a symmetry-aware fragment decomposition algorithm and a fragment-constrained Monte Carlo Tree Search (MCTS) generator. Our approach generates candidates that retain symmetry constraints from the patent dataset, while also exhibiting red-shifted absorption, as validated by TD-DFT calculations.

Cis-Chelating Diphosphanes for Intracavity Nickel(II)-Catalyzed Ethylene Oligomerization

Four cis-chelating diphosphanes derived from cyclodextrins (CDs), each featuring a distinct intracavity environment, compel NiII or PdII metal centers to reside within α- or β-CD cavities. Nickel(II) complexes of these metal-confining ligands act as active catalysts in ethylene oligomerization upon activation with modified methylaluminoxane (MMAO). The size of the cavity and the position of the P2Ni fragment relative to the cavity affect both the activity and selectivity of the reaction. In all instances, 1-butene is the major product (up to 98% C4 products and 90% 1-butene within the C4 fraction). Extensive theoretical studies with state-of-the-art methods carried out on the most selective system suggest that the CD cavity restricts isomerization pathways by limiting the mobility of the coordinated olefin in this constrained supramolecular environment, thereby enhancing α-olefin formation.

Ultrafast photophysics of the cyan fluorescent protein chromophore in solution

Incorporation of fluorescent proteins (FPs) into biological systems has revolutionised bioimaging and the understanding of cellular processes. Ongoing developments of FPs are driving efforts to characterise the fundamental photoactive unit (chromophore) embedded within the protein. Cyan FP has a blue emitting chromophore and is widely used in Förster resonance energy transfer studies. Here, we probe the ultrafast photophysics of the cyan FP chromophore in solution using time-resolved fluorescence up-conversion and transient absorption spectroscopies. The ultrafast dynamics are characterised by two lifetimes, sub-picosecond τ1 (or τF) associated with loss of the fluorescent Franck-Condon state, and lifetime τ2 on the order of several picoseconds that is linked with cooling of a hot ground state. MRSF-TDDFT calculations show that the relaxed S1 state equilibrium geometry is classified as a partial twisted intramolecular charge-transfer state, and lies close in energy to a conical intersection seam associated with torsion about the central double bond leading to facile internal conversion. The excited state dynamics exhibit only a weak viscosity dependence, consistent with a barrierless and near-volume-conserving non-radiative decay mechanism. Fluorescence lifetimes for the deprotonated anion are twice those for the neutral.

Unveiling the mechanism of dodecylphosphorylcholine as an extremely promising drug delivery system: From self-assembly clusters to drug encapsulation pathways

Significant progress has been achieved in cancer treatment with Doxorubicin (DOX), yet its low toxicity and poor bioavailability have long troubled scientists. Dodecylphosphorylcholine (DPC), as a candidate material for drug delivery systems (DDS), holds promise in assisting DOX to overcome its application bottleneck. In this study, employing a combination of quantum chemical calculations and molecular simulations, we delve into the dynamic processes of the interaction between DPC and DOX molecules for the first time. The results indicate that, under the synergistic effect where electrostatic repulsion plays a minor role and van der Waals attraction predominates, the end (containing choline group) of DPC molecules aggregate, self-assembling into multiple molecular clusters. There is a notable presence of electrostatic attraction and van der Waals attraction between DPC and DOX, which drives the adsorption or encapsulation of DOX molecules by DPC molecular clusters, thus presenting a favorable drug-loading conformation. During these processes, a substantial number of DPC molecules aggregate around DOX, with typical distances for interaction around 0.5 nm. The shape and position of DPC-DOX molecular clusters undergo significant dynamic changes within a simulated time of 0-70 ns, stabilizing thereafter. Our findings elucidate the interaction mechanism between DPC and DOX at the molecular scale, paving new avenues for the experimental synthesis of promising DDS eagerly sought by DOX.

Modulation of Photoluminescence of BODIHY Dye Using Water-Soluble Coordination Cages With Different Shapes

The confinement of guest molecules within supramolecular hosts can alter their photophysical properties. However, the shapes of the hosts in regulating the guest's emission remains underexplored. Herein, we investigate how the shape of the host alters the emission behavior of boron difluoride hydrazone (BODIHY) (G1) dye encapsulated within two iso-stoichiometric water-soluble coordination cages: MC1 (double-square cage) and MC2 (octahedral cage). Encapsulation of G1 within MC1 results in a highly emissive solution, whereas similar confinement in MC2 leads to a non-emissive host-guest solution. A similar trend was observed with different sets of iso-stoichiometric cages MC3 (double-square cage) and MC4 (octahedral cage). Using a combination of femtosecond transient absorption and time-resolved fluorescence spectroscopy, we observed that the disparity in fluorescence behavior of BODIHY is attributed to charge transfer interactions between the guest and ligand panels of cages. The shape of the coordination cage dictates the preorganization of the guest within the cavity, thereby suppressing or promoting this charge transfer interactions. Moreover, we demonstrate a turn-on emission of BODIHY dye due to its preferential binding to a double-square cage. These findings provide fundamental insights into host-mediated modulation of the guest's photophysics and offer a blueprint for designing supramolecular systems with tunable emissive behavior.

A Crystalline Annelated Pyridin-1-ylidene and Its Isomerization into a Pyridin-3-ylidene

An isolable ring-fused pyridinylidene (a so-called Hammick intermediate) was synthesized from a benzo[h]isoquinolinium salt, and its structure in the solid state was determined. The isolation of this first-of-its-kind pyridine-derived aromatic N-heterocyclic carbene was made possible due to the presence of an adamantyl group on nitrogen, forcing the nitrogen to be planar and enhancing both π-donation and steric protection, and to its unique polycyclic structure, which displays an intramolecular C-H···Ccarbene interaction. Additionally, an example of isomerization of a carbene into another carbene, namely, a benzo[h]isoquinolin-1-ylidene into a benzo[h]isoquinolin-3-ylidene, is reported.

Discovery of a molecular adsorbent for efficient CO2/CH4 separation using a computation-ready experimental database of porous molecular materials

The development and sharing of computational databases for metal-organic frameworks (MOFs) and covalent organic frameworks (COFs) have significantly accelerated the exploration and application of these materials. Recently, molecular materials have emerged as a notable subclass of porous materials, characterized by their crystallinity, modularity, and processability. Among these, macrocycles and cages stand out as representative molecules. Experimental discovery of a target molecular material from a vast possibility of structures for defined applications is generally impractical due to high experimental costs. This study presents the most extensive Computation-ready Experimental (CoRE) database of macrocycles and cages (MCD) to date, comprising 7939 structures. Using the MCD, we conducted simulations of binary CO2/CH4 competitive adsorption under conditions relevant to industrial applications. These simulations established a structure-property-function relationship, enabling the identification of materials with potential for CO2/CH4 separation. Among them, a macrocycle, NDI-Δ, exhibited promising CO2 adsorption capacity and selectivity, as confirmed by gas sorption and breakthrough experiments.

Theoretical Studies in Molecular Dynamics and DFT of the Interaction between Imidacloprid in Polyethylene and Polypropylene Surfaces

Pesticides are chemical substances that are often used in agriculture to correct soil deficiencies, control pests, and eradicate destructive plants. However, it is imperative to assess their effectiveness to avoid potential harm to human health. In addition, microplastics (MP) have been the subject of research into their spread from marine and agricultural environments. Considering the possibility of contact between pesticides and microplastics, with the subsequent possibility of them acting as vectors of dispersion through adsorption between the two, it is imperative to evaluate the effectiveness of pesticides in order to avoid potential harm to human health. The current study used computational calculations to analyze the possible interactions between polyethylene (PE) and polypropylene (PP) microplastics with the pesticide imidacloprid (IMI), which is used in the cultivation of bananas, one of the most widely grown fruits in the world. Molecular dynamics (MD) and density functional theory (DFT) calculations indicated favorable adsorption energies for the interaction of the two microplastics. The results obtained by applying MD and DFT indicate that the nature of the IMI-MP interaction is van der Waals. Consequently, the theoretical approaches suggest that the pesticide under study has a strong propensity to interact with PE and PP, providing a significant incentive for future experimental investigations in this area.

Spontaneous aqueous defluorination of trifluoromethylphenols: substituent effects and revisiting the mechanism

Trifluoromethylphenols (TFMPs) are environmental contaminants that exist as transformation products of aryl-CF3 pharmaceuticals and agrochemicals. Their -CF3 moiety raises concerns as it may form problematic fluorinated transformation products such as the persistent pollutant trifluoroacetic acid (TFA). This study investigates the hydrolysis and spontaneous defluorination mechanisms of 2-TFMP, 3-TFMP, 4-TFMP, and 2-Cl-4-TFMP under environmentally relevant aqueous conditions, and under alkaline pH to investigate the mechanism of defluorination. 3-TFMP did not undergo hydrolysis. The other TFMPs reacted to primarily form the corresponding hydroxybenzoic acids and fluoride. High-resolution mass spectrometry identified a benzoyl fluoride intermediate in the hydrolysis of 4-TFMP and other dimer-like transformation products of the 4- and 2-Cl-4-TFMP. Density functional theory calculations revealed that the key defluorination step likely proceeds via an E1cb mechanism, driven by β-elimination. Experimental and computational results demonstrated substituent-dependent differences in reactivity, and the importance of the deprotonation of TFMPs for the hydrolysis reaction to proceed. These findings provide mechanistic insights into the complete defluorination of TFMPs and broader implications for the environmental defluorination of other PFAS.