Electrocatalytic nitrogen reduction reaction (NRR) offers a sustainable alternative to the Haber-Bosch process but is hindered by competing hydrogen evolution and sluggish triple bond activation in N2. Here, density functional theory (DFT) calculations are performed to systematically evaluate the adsorption energies of key NRR intermediates (*N2,*N, *NNH, *NHNH, and *NNH2) on representative transition-metal surfaces, including FCC(110), FCC(111), and HCP(0001). By comparing the adsorption energies of 2*N and *NNH, and those of *NNH2 and *NHNH, we establish a simple adsorption-energy framework to identify the likely dissociative or associative mechanism and, for associative pathways, the distal or alternating route. The framework predicts dissociative pathways for Ni(111), Rh(111), Ir(111), Ni(110), Ru(0001), and Co(0001), whereas Pd(111) and Cu(111) favor the associative distal pathway and most other surfaces favor the associative alternating pathway. Analysis of Bader charge transfer, N-N bond elongation, and minimum metal-N distances provides further insight into the structural and electronic factors governing NRR intermediate adsorption. Overall, these results demonstrate that adsorption-energy differences, complemented by electronic-structure analysis and representative transition-state calculations, provide an efficient framework for screening and understanding NRR mechanisms across diverse transition-metal surfaces.
This study investigates the electrochemical performance, electrochemical performance, and structural stability of halide-doped C6N8 nanocages as potential anode materials for sodium-ion batteries. Utilizing density functional theory (DFT) calculations, the evolution of the framework was systematically evaluated from a simple metal-doped cage (M@C6N8, where M = Zn, Mg) to a complex co-doped system (M/X-/Na+@C6N8). The results show that the undoped metal-nanocage systems exhibit limited electrochemical driving forces, whereas halide incorporation substantially modulates their electronic and thermodynamic properties. Among the investigated configurations, Zn/Cl-/Na+@C6N8 achieves the highest theoretical cell voltage of 3.65V, while Mg/F-/Na+@C6N8 provides a favourable voltage of approximately 2.0V. Hirshfeld population analysis reveals substantial charge redistribution between the metal centres, halide ions, Na+, and the C6N8 framework, confirming that halide incorporation strongly influences the electronic environment of the active metal centre. Frontier molecular orbital analysis further demonstrates redistribution of electron density upon metal oxidation and halide incorporation. Competitive binding calculations indicate that the metal centres remain thermodynamically anchored within the C6N8 framework, while Na+ acts primarily as the mobile charge-compensating species. Overall, the results demonstrate that metal-halide co-modification provides an effective strategy for tuning the electrochemical potential, charge transfer, and structural stability of C6N8-based sodium-ion battery materials.
Modern drug discovery joins machine learning with physics-based simulation, but building such a pipeline needs Linux administration, dependency management, format conversion and scripting, which keeps out many of the chemists and biologists who ask the questions. We describe SilicoXplore, a cloud-hosted platform of 31 interoperable modules covering structure preparation, five docking engines, de novo generation, machine-learning prediction, ADMET estimation, molecular dynamics, free energy calculation and density functional theory, all driven through guided forms. Third-party engines are wrapped under their own licences rather than rewritten. It was applied to New Delhi metallo-β-lactamase-1, generating 752,436 molecules from 455 seed actives and narrowing them to four. Docking was validated by redocking, at 0.840 Å, and by enrichment against 4247 property-matched decoys, at an area under the receiver operating characteristic curve of 0.714. The four molecules and the reference were re-docked with both catalytic Zn2+ ions retained, then carried into triplicate 100 ns molecular dynamics with MM-GBSA and MM-PBSA analysis. Redocking reproduced both the crystallographic pose of the reference and its metal contact, at 2.215 Å, and three of the four molecules contacted a zinc ion directly. Replicate spread in the end-point energies matched the differences between molecules, so no ranking is claimed. The classifier, which reached an area under the curve of 0.985 within its training chemotype space, retained 76.5% of the library, because enforcing novelty places it outside the region in which the model was assessed. Selection was carried by the physics-based stages. All results are computational and require experimental validation.
Lithium-ion solvation in carbonate electrolytes plays a central role in determining the performance and stability of lithium-ion batteries, yet the molecular factors governing solvent competition within the Li+ coordination shell remain incompletely understood. In this work, density functional theory calculations combined with continuum solvation modeling were used to investigate ligand substitution in Li+(EC)4-n(DMC)n complexes, where ethylene carbonate (EC) was systematically replaced by dimethyl carbonate (DMC). The calculated stepwise free energies are -0.58, +0.39, -2.84, and +0.50 kcal mol-1 for n = 1-4, respectively, producing a distinctly non-monotonic substitution profile. The overall free-energy minimum occurs at n = 3 (ΔG = -3.03 kcal mol-1), identifying Li+(EC)(DMC)3 as the lowest-free-energy member of the modeled microsolvation series under the adopted computational conditions. Structural analysis shows that Li+ maintains a four-coordinate inner-shell geometry throughout the substitution series, indicating that ligand exchange occurs within a constrained coordination framework rather than through expansion of the coordination number. An expanded outer-shell analysis further shows that five of six nominally outer-shell Li+(EC)3···(DMC)2 starting topologies underwent inward migration of one DMC molecule during unconstrained optimization. Only the topology in which the two outer-shell DMC molecules were associated with the same EC ligand retained both DMC molecules outside the first coordination shell, and this structure lies 8.53 kcal mol-1 above the lowest-energy optimized structure in that set. Across n = 0-3, the summed principal LP(O) → LP*(Li) natural bond orbital second-order perturbation energies change by +0.52, -0.47, and +0.88 kcal mol-1 for the first three substitutions, respectively, following the same favorable, unfavorable, and strongly favorable pattern as the corresponding stepwise free energies. These NBO quantities are comparative electronic descriptors rather than additive components of ΔG; the calculated free energies also include contributions from structural relaxation, continuum solvation, thermal and entropic effects, and interligand interactions. Published 17O NMR and FTIR/NMR measurements support carbonyl-oxygen coordination and mixed EC/DMC solvation while demonstrating that bulk speciation depends on electrolyte composition, salt concentration, and ion pairing. The present calculations therefore provide a controlled salt- and additive-free thermodynamic baseline for intrinsic EC/DMC competition rather than quantitative populations for a practical electrolyte.
We present a first-principles investigation of pristine and transition-metal (Cu, Ag, Au) decorated TiO2 nanotubes (TiO2-NTs) for CO and CO2 sensing. Density functional theory calculations show that metal substitution at the O1 site significantly tailors the electronic structure through band-gap modulation, localized states, and spin polarization while preserving structural stability. Natural bond orbital and density-of-states analyses reveal strong charge polarization induced by Cu, moderate electron donation from Ag, and partial back-donation effects associated with Au. Adsorption results indicate that CO2 interacts most strongly with pristine TiO2-NTs, whereas CO exhibits pronounced chemisorption on Au-decorated nanotubes via C-Ti bond formation, supported by orbital hybridization and noncovalent interaction analyses. Recovery-time calculations demonstrate that Ag- and Au-decorated TiO2-NTs enable fast and reversible desorption, making them suitable for reusable sensing applications. Overall, transition-metal decoration emerges as an effective strategy to enhance the sensitivity, selectivity, and reversibility of TiO2-NT-based gas sensors for CO and CO2 detection.
The interaction between the transcription factor c-Myb and the CBP/p300 KIX domain is a key regulatory event in transcriptional programs associated with hematologic malignancies, including acute myeloid leukemia, and therefore represents an attractive target for protein-protein interaction (PPI) disruption. In this study, we applied an integrated computational and experimental workflow to identify new quinone-methide triterpenes capable of perturbing the c-Myb-CBP/p300 KIX interface. Using the c-Myb-bound KIX conformation derived from PDB:2AGH as the structural template, a library of 457 quinone-methide-triterpenes was subjected to funnel-based virtual screening. This workflow prioritized isoiguesterin, pristimerin, and tingenone for detailed evaluation. Subsequent 500ns MD simulations revealed distinct ligand-dependent effects on interfacial stability and conformational dynamics. ΔΔGPPI analysis showed that tingenone produced the strongest predicted weakening of the c-Myb-KIX interaction (+2.31 kcal/mol), whereas pristimerin had only a marginal disruptive effect (+0.28 kcal/mol) and isoiguesterin instead favored stabilization of the complex (-5.18 kcal/mol). Consistently, free-energy-landscape (FEL) analysis showed that tingenone induced the most heterogeneous conformational ensemble with multiple low-energy basins, while isoiguesterin and Naphthol-AS-E-phosphate favored more restricted low-energy states. Although isoiguesterin showed the most favorable direct binding energetics, it tended to stabilize or compact the interface. Pristimerin displayed an intermediate profile. In contrast, tingenone produced the clearest signatures of interfacial weakening and the most favorable disruption metrics. Experimental validation by microscale thermophoresis (MST) confirmed the direct Myb-KIX interaction (Kd = 28.49 ± 2.32 μM) and showed that tingenone was the most potent inhibitor among the tested triterpenes (IC50 = 23.6 ± 3.0 μM), outperforming the reference disruptor Naphthol-AS-E-phosphate (IC50 = 29.0 ± 1.6 μM). These findings identify tingenone as a promising scaffold for the development of new c-Myb-CBP/p300 KIX PPI inhibitors.
Highly spin-polarized magnetic materials have a great scope in spin-based device applications. In this study, the geometric, electronic structure, magnetism, and chemical bonding of bulk K2CoCl6 (KCC) and its bare, H- and O-passivated, and doped surfaces were investigated using first-principles DFT calculations. The results showed that KCC is ferromagnetic, with 83% spin polarization at the Fermi level. ELF and COHP calculations confirmed ionic bonding in Co-Cl with an average bond energy of -2.65 and an ICOBI value of 0.4. Among the three investigated surfaces (001), (011), and (111), the (001) surface presented the lowest surface energy and exhibited metallic properties. The H- and O-passivated surfaces exhibited a few lattice distortions. The O-passivated surface exhibited half-metallic properties. Aluminum (Al) doping of bare surfaces slightly modified the electronic structure and magnetism. The passivated and doped surfaces retained the bulk metallic characteristics. These findings provide a theoretical foundation for further experimental and theoretical investigations of K2CoCl6-based thin films and their potential application in future spintronic and spin-dependent electronic devices.
Graph theory offers a mathematical foundation for modeling and analyzing complicated chemical structures and reaction systems. Topological indices are fundamental tools for measuring structural characteristics and predicting physicochemical and biological properties, with broad applications in chemistry, biology, environmental toxicology, and drug discovery. This study focuses on the two molecular graph families, porphyrazine and tetrakis porphyrazine, which contain complex ring structures. For each graph family, we determine the edge partition induced by endpoint connection numbers and use it to derive closed-form expressions for the general Randić connection index, the first and second Zagreb connection indices and coindices, the atom-bond connectivity connection index, the hyper-Zagreb connection index, the geometric-arithmetic connection index, the forgotten connection index, the augmented Zagreb connection index, and three redefined Zagreb connection indices. The dependence of each formula on the graph parameter n is examined in the graphical comparison section. This analysis enables a direct comparison of the growth trends and structural variations of the two graph families.
Acetogenins are polyketides found almost exclusively in plants of the Annonaceae family and exhibit diverse biological activities, including antineoplastic, antiparasitic, cytotoxic, immunosuppressive, neurotoxic, and pesticidal effects. Five highly effective acetogenins (annopurpuricins A-E) showing anticancer properties in the picomolar range, were previously isolated from the roots of Annona purpurea (Hernández-Fuentes et al., 2019). To elucidate the mechanism underlying their potent cytotoxicity, we investigated the binding of annopurpuricins A-E to the ubiquinone-binding site of mitochondrial complex I using AutoDock Vina. Complex stability was assessed by molecular dynamics (MD) simulations. We identified the protein residues involved in binding and characterized the binding poses of the acetogenins. Annopurpuricins A-D adopted elongated conformations that spanned nearly the entire ubiquinone channel, whereas annopurpuricin E adopted a V-shaped conformation. Interactions were predominantly hydrophobic with a limited number of hydrogen bonds. These results support a model in which annopurpuricins inhibit complex I by occupying the ubiquinone channel, thus accounting for their exceptional cytotoxic potency.
Developing organic chromophores with tailored optical properties is a key objective in material chemistry. In the present study, a dataset of 130 organic chromophores was used to build QSPR models for absorption and emission maxima using CORAL. The models were generated using SMILES derived descriptors optimised through Monte-Carlo method. The model stability was evaluated using non-identical data splits and multiple target functions IIC, CII, CCCP and correlation balance. The resulting models showed good predictive performance based on internal and external validation parameters such as R2, Q2, MAE and standard error. Among the developed models, A18 generated using the CCCP-based target function (TF-4) is the best model for absorption maxima with validation statistics of R2 = 0.8404, Q2 = 0.8261, MAE = 39.4, AvRm2 = 0.7703 and ΔRm2 = 0.1077. For emission maxima, F10 provided the best overall performance, giving R2 = 0.8332, Q = 0.8192, MAE = 32.4, AvRm2 = 0.7615 and ΔRm2 = 0.0019. These results confirm the predictive reliability of the SMILES-based CORAL models. Applicability domain assessment indicated that most of the compounds were within the reliable prediction range of the models. These results suggest that the proposed models can be used as a practical tool for estimating chromophores spectral properties and guiding the design of near-infrared materials.
In present work, the degradation mechanisms of 1,2,3-trimethylbenzene (1,2,3-TMB) initiated by NO3 radical were researched using quantum chemical method. Results manifest that both addition and abstraction (including H-abstraction and CH3-abstraction) mechanisms could take place, with the addition and H-abstraction mechanism being competitive with each other. Furthermore, the subsequent reactions of the main products of the addition and H-abstraction reaction channels in the atmosphere were also considered, as well. The rate coefficients were computed in the atmosphere through Rice-Ramsperger-Kassel-Marcus theory and transition-state theory. The computed total rate coefficient at 1atm and 296 K is 1.53 × 10-16 cm3 molecule-1 · s-1, which is in accordance with the now available experimental data in the atmosphere. The present investigation could enhance our understanding of the transformation of 1,2,3-TRT in complex atmospheric environments.
Experimental determination of flash points (FPs) for liquid mixtures is laborious and costly, highlighting the need for reliable predictive approaches for safety assessment and engineering applications. Although numerous models have been reported for binary miscible mixtures, most rely on fixed model parameters or empirical correlations, which limits their ability to capture the nonlinear relationship between molecular structure and FP. In this study, a quantitative structure-property relationship (QSPR) framework that tightly integrates differential evolution (DE) with support vector regression (SVR) was developed to predict the FP values of binary miscible mixtures, where DE was employed to globally optimize key SVR hyperparameters and enhance model generalization capability. A dataset consisting of 332 compositions from 33 binary mixtures formed by pairwise combinations of 20 pure components was employed, and multiple molecular descriptor representation strategies were deliberately adopted to construct distinct DE-SVR models, enabling a systematic investigation of the combined effects of descriptor representation and model optimization on predictive performance. Three DE-SVR models were established based on different descriptor sets, and their predictive accuracy, robustness, and stability were comprehensively evaluated. Among them, the model constructed using physicochemical parameters exhibited the best overall performance. Comparative analyses with existing FP prediction methods reported in the literature further confirmed the effectiveness and superiority of the proposed DE-SVR-based models. The results of this study provide a practical tool for FP estimation of binary mixtures and offer valuable insights into the joint roles of model optimization and molecular representation in mixture property prediction.
Graph-theoretic analysis provides a rigorous mathematical framework for investigating molecular architecture; however most existing studies primarily rely on conventional topological indices and centrality measures that characterize connectivity without explicitly quantifying structural control, redundancy, vulnerability or resilience. This study develops a unified graph-theoretic framework for resilience-oriented analysis of cyclic peptide molecular graphs through a collection of novel connectivity-based descriptors. Oxytocin is selected as the principal case study because of its well-defined cyclic architecture and conserved disulfide bridge, four additional cyclic peptides: Vasopressin, Desmopressin, Octreotide and Somatostatin are analyzed to validate the robustness, discriminative capability and general applicability of the proposed methodology.Hydrogen-suppressed molecular graphs were constructed from experimentally established molecular structures. In addition to classical graph-theoretic measures including degree, betweenness, closeness, eigenvector centralities and network efficiency, the proposed framework introduces the Enhanced Structural Control Index (ESCI), Bond Criticality Index (BCI), Weighted Bond Criticality Index (WBCI), Disulfide Structural Redundancy Index (DSRI), Atom Vulnerability Spectrum (AVS) and Molecular Resilience Ratio (MRR). Structural robustness was further investigated by comparing targeted perturbations performed through sequential removal of the five highest-ranked AVS atoms with random vertex deletions.The oxytocin molecular graph contains 69 vertices, 71 edges and a cyclomatic number of three revealing strong dependence on a limited set of articulation points and bridge edges. AVS exhibits a strong correlation with betweenness centrality (Pearson correlation >0.91 across all investigated peptides) while providing complementary efficiency-based vulnerability information beyond conventional centrality measures. Comparative validation across five cyclic peptide molecular graphs demonstrates that the proposed descriptors consistently distinguish structural control, bond criticality, redundancy, vulnerability and resilience. The proposed framework establishes a mathematically interpretable, computationally efficient and broadly applicable methodology for graph-theoretic analysis of cyclic peptide molecular graphs with potential extensions to larger peptide systems and related biomolecular networks.
Despite progress in antifungal therapeutics, invasive fungal infections are estimated to cause over one million deaths annually. In recent years, antimicrobial peptides (AMPs) have shown promise as a potential therapeutic option against such infections. Although over 2300 types of AMPs have been identified to date, the development of antifungal peptides (AFPs) has lagged behind. Therefore, the discovery of AFPs with strong efficacy against fungi remains a critical challenge. In this study, we developed an artificial intelligence system that learns from the amino acid sequences and physicochemical properties of known AFPs to efficiently identify putative AFP candidates. First, the amino acid sequences were effectively modeled using a combination of advanced algorithms from the field of natural language processing and a multi-layer perceptron. Second, the physicochemical attributes were learned using a model that combines Pfeature-derived features and a random forest classifier. By integrating these two models, we constructed a classification pipeline capable of rapidly identifying previously unreported putative AFP candidates from randomly generated amino acid sequences sampled according to empirical amino acid frequency distributions derived from known AFPs. Bioinformatic analyses suggested that these candidates exhibit structural and physicochemical features commonly associated with known AFPs and may potentially interact with enzymes involved in fungal cell wall biosynthesis. Future work will involve wet-lab validation of the antifungal activity, stability, and cytotoxicity of these putative AFP candidates to further assess the biological relevance and predictive utility of the proposed system.
Internal faults in oil-immersed transformers release characteristic dissolved gases(H2, C2H4 and C2H2), making high-performance gas-sensing materials essential for early diagnosis. Herein, using density functional theory (DFT) construct intrinsic and Fe-and Zn-doped MXene-based Nb2CO2 monolayer models and systematically investigate the adsorption thermodynamics, electronic-structure responses, and sensing kinetics of these three gases on the surfaces. Through multi-dimensional analyses of adsorption energy, charge transfer, density of states, frontier molecular orbitals, work function, desorption time, and sensitivity. The results show that intrinsic Nb2CO2 exhibits only weak physisorption toward all three gases, yielding no detectable electrical response. Fe and Zn doping markedly enhance the surface capture capability: Fe-Nb2CO2 forms chemisorption with C2H4 (-1.840 eV)and C2H2 (-1.556 eV), whereas Zn-Nb2CO2 displays weak chemisorption with C2H4(-1.306 eV) and C2H2(-1.110 eV). Both doped surfaces retain weak physisorption toward H2.Fe-Nb2CO2 achieves a favorable desorption time(∼13 s)and the highest sensitivity for C2H2 at 598 K, revealing outstanding C2H2 sensing potential. However, its excessively slow C2H4 desorption makes it more suitable as a C2H4 adsorbent. Zn-Nb2CO2 gives a C2H4 desorption time of 16.49 s at 498 K with moderate sensitivity, appropriate for resistive C2H4 sensing. This work provides a theoretical foundation for designing low-power, high-sensitivity MXene-based gas-sensing materials for dissolved gas analysis in transformer oil.
