The structures, IR and Raman spectra, electronic and optical properties of ε-2,4,6,8,10,12-hexanitro-2,4,6,8,10,12-hexaazaisowurtzitane (ε-CL-20) crystal under 0-250 GPa have been systematically studied by periodic density functional theory (DFT) method. The crystal structure, molecular configuration and hydrogen bond are discontinuous at 195 GPa, which indicate that the structural mutation of ε-CL-20 occurs. IR and Raman analysis prove that the structure of ε-CL-20 do mutate, and high pressure makes the system appear more absorption peaks of C-H stretching in the high frequency region of more than 3000 cm[-1]. The calculation of electronic properties show that the band gap decreases with the increase of pressure, and the valence band maxima (VBM) and conduction band minima (CBM) mainly come from the contribution of O and N atoms, indicating that N-NO2 group is the active center of ε-CL-20. High pressure broadens the absorption peak and increases the absorption coefficient, which means that the optical absorption activity of the system increases. This study provides a theoretical basis for the high pressure behavior of energetic materials.

This study systematically investigated the effects of Co- and V-doping on the structural stability, mechanical properties, electronic structure, and electrochemical performance of MoS2 using first-principles calculations. Phonon dispersion spectrum analysis confirmed that both Co-MoS2 and V-MoS2 exhibit excellent dynamic stability. Mechanical property tests demonstrated that doping significantly enhances the Young's modulus (Co-MoS2: 220 N/m; V-MoS2: 238 N/m) and maintaining an appropriate Poisson's ratio, effectively mitigating stress concentration. DOS calculations revealed that Co- and V-doping markedly improve the intrinsic electronic conductivity of MoS2, with band gaps reduced to 0.39 eV and 0.98 eV for Co-MoS2 and V-MoS2, respectively. In terms of electrochemical performance, the study yielded breakthrough findings: the doped systems significantly lowered the migration barriers for Li[+]/Na[+]/K[+] (Co-MoS2: 0.06 eV/0.09 eV/0.09 eV, V-MoS2: 0.27 eV/0.24 eV/0.21 eV), achieving fast ion transport kinetics. Charge density difference and adsorption energy analyses showed that Co- and V-doping strengthens the interactions between MoS2 and Li[+]/Na[+]/K[+]. Additionally, doped significantly increased the average open-circuit voltage of MoS2 for Li[+]/Na[+]/K[+] (Co-MoS2: 0.76 V/0.68 V/0.44 V, V-MoS2: 0.83 V/0.71 V/0.51 V), providing a crucial theoretical basis for designing electrode materials with high energy density. These findings provide new design strategies and theoretical guidance for developing high-performance MoS2 materials for alkali metal-ion batteries.

The present study aims to theoretically explore the physical properties of HfHgX2 (X = S, Se, Te) compounds. The structural, electronic, bonding, elastic, and optical properties are investigated using density functional theory based VASP code. Structural analysis unveils that HfHgX2 (X = S, Se, Te) compounds crystallize in the trigonal crystal system with the R 3‾ m (No. 166) space group. The calculated structural and lattice parameters are in good agreement with the available literature data. The calculated formation energy values are -1.195622 eV (HfHgS2), -1.257295 eV (HfHgSe2), and -0.615084 eV (HfHgTe2) showing the thermodynamic stability. The electronic properties reveal that HfHgS2 possesses a direct band gap, whereas HfHgSe2 and HfHgTe2 exhibit indirect band gaps via PBE-GGA potential. The absence of imaginary phonons modes indicates the lattice dynamical stability of these studied compounds. The partial charge density distribution indicates that the occupied electronic states are predominantly localized around the chalcogen atoms, whereas the unoccupied electronic states are mainly localized around the Hf atoms providing the electronic transition route from X → Hf. The examination of -COHP demonstrates that Hf-X bonding interactions are the primary contributors to the structural stability of these compounds with ICOHP values for Hf-X (X = S, Se, Te) bonds are -3.107, -2.808, and -2.383 and corresponding ICOBI values of 0.644, 0.643, and 0.646 respectively. The direction-dependent optical properties were calculated along the ab-plane, c-axis, and total, revealing substantial directional dependence and pronounced optical anisotropy via PBE-GGA and SCAN potentials. The elastic properties confirm the mechanical stability criterion, indicating that the HfHgS2 compound is less brittle than HfHgSe2 and HfHgTe2. The mechanical properties show that the HfHgX2 (X = S, Se, Te) compounds are highly anisotropic due to their distinct bonding environment and the different interactions within the layers of their layered crystal framework.

In a severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), the main protease M[pro] plays a crucial role in the viral replication process. Andrographis paniculata has been reported for anti-SARS-CoV-2 properties. This work aims to identify new SARS-CoV-2 M[pro] inhibitors and investigate the binding roles of major diterpene lactones derived from Andrographis paniculata. Molecular docking and molecular dynamic (MD) simulations with the dimeric SARS-CoV-2 M[pro] were performed to identify the potential compounds which are five major diterpene lactones and four selected compounds. Binding affinities, expressed as binding and interaction energies, were calculated using the molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) method and the quantum mechanics/molecular mechanics (QM/MM) using density functional theory. Principal component analysis (PCA), along with free energy landscape (FEL) analysis, was performed to assess the stability of the ligand-protein binding interactions. The density functional theory in terms of residual interaction energies revealed that H-bond, H-pi and pi-pi interactions were key interactions during the binding. Andrographidine E from Andrographis paniculata was found to be a new SARS-CoV-2 M[pro] inhibitor with the IC50 of 158 μM. The results help explain the roles of binding of compounds against dimeric SARS-CoV-2M[pro] which will be useful for the development of SARS-CoV-2 M[pro] inhibitors.

Short linear motifs (SLiMs) located within intrinsically disordered regions (IDRs) are essential for mediating dynamic protein-protein interactions. However, their short length, sequence degeneracy, and the conformational heterogeneity of IDRs pose significant challenges for accurate computational identification. Traditional methods that rely exclusively on one-dimensional sequences or static three-dimensional structures may not adequately capture the intricate interplay between structure and sequence, while severe class imbalance often results in unacceptably high false-positive rates. To address these challenges, we introduce GeoSeq-EGNN, a Geometry-Sequence Fused Equivariant Graph Neural Network designed for dual-modality SLiMs prediction. This architecture effectively integrates Global-Local Hybrid Equivariant Message Passing Layers (GLH-EMPL) to model the three-dimensional spatial folding dynamics of IDRs, alongside Multi-Scale Geometry-Sequence Fusion Network Layers (MSGS-FNL) that utilize Bidirectional LSTMs to extract critical one-dimensional primary sequence topologies. We also incorporate an adaptive coordinate update mechanism to improve geometric stability in deeper layers and dynamic gate feature fusion to reduce representation over-smoothing. To address class imbalance, we use a confidence-aware focal loss that penalizes overconfident erroneous predictions. On the evaluated SLiMs dataset, GeoSeq-EGNN achieved an accuracy of 92.94% and an AUPRC of 82.17% under the final F6 feature configuration and outperformed the evaluated baseline models in the controlled one-hot comparison, with higher Precision and Matthews Correlation Coefficient (MCC). These results support GeoSeq-EGNN as a computational framework for SLiM prediction and provide a basis for future studies of SLiM-mediated regulatory networks.

Molecular dynamics (MD) simulation is widely used to investigate biomolecular stability, conformational transitions, molecular recognition and membrane transport, yet practical MD research still relies on many experience-driven decisions that are difficult to standardize and reproduce. Here, we present PRAXIS-MD, a case-guided problem-solving protocol that converts a natural-language biological question into an auditable MD workflow. The protocol couples an external long-term brain, comprising case memory, reusable skills and negative cases, with a brain-gated retrieve-plan-execute loop. This design grounds structure selection, system preparation, simulation planning, trajectory analysis and mechanistic interpretation in reusable prior experience rather than one-shot prompting. We distilled nine representative MD studies into reusable workflow knowledge and evaluated the protocol on nine unseen benchmark tasks under bare, memory-only, skill-only, rule-only and full PRAXIS-MD settings. Full PRAXIS-MD achieved the highest benchmark score, and integration with five additional LLM hosts improved performance across all hosts. In an independent membrane lipid transport case, PRAXIS-MD organized a coherent flippase/scramblase workflow without a predefined template. These results suggest that structured case learning can improve the reproducibility, transferability and biological interpretability of automated MD workflows.

Per- and polyfluoroalkyl substances (PFAS) represent persistent contaminants in aquatic environments, and developing effective absorbents for their removal is a major challenge. This study has been conducted for absorbent design using density functional theory (DFT) and molecular dynamics (MD) in order to understand the process of adsorption of the following PFAS species: PFBA[-], PFBS[-], PFOA[-], and PFOS[-] on nitrogen-, oxygen-, and sulfur-containing linker models. For this, geometry optimizations were performed at ωB97XD/6-311++G(d, p) level under implicit aqueous solvation, and the adsorption energies were calculated using the counterpoise correction technique. The final results showed that four PFAS molecules exhibited appropriate counterpoise-corrected adsorption energies ranging from -0.700 to -20.047 kcal mol[-1], with the strongest interaction between the PFOS[-] molecule and the sulfur-containing linker model (-20.047 kcal mol[-1]). As for the effect of sulfur substitution on the mean energy of adsorption for various PFAS species, it was negative, but the identity of the heteroatom affected PFAS adsorption behavior. Conceptual DFT descriptors based on frontier orbitals showed weak and statistically insignificant linear relations with adsorption energies, indicating that a single comprehensive electronic descriptor is not sufficient to explain the trend in adsorption energy across the data set. The analyses based on ALIE, RDG and NCI showed that the electronic activity depends on the ligands and involves both dispersive and attractive non-covalent interactions. Hirshfeld analysis revealed the predominance of ligand-H contacts, while other analyses (ELF and DOS) indicated that electron localization and redistribution depend on the heteroatoms. Finally, the 100 ns aqueous MD simulations provided evidence of continuous interaction between PFOS and ligands, accompanied by significant conformational and interfacial alterations, as well as sampling of low-free-energy states. The conclusions drawn from these results indicate that PFAS adsorption at the linker level arises from the combined effects of molecular structure, the electronic environment influenced by heteroatoms, and noncovalent interactions.

The development of allosteric inhibitors is a promising strategy for targeting epidermal growth factor receptor (EGFR) mutations in non-small cell lung cancer (NSCLC), especially in cases where orthosteric inhibitors face resistance. However, despite their selective inhibition of EGFR mutations, fourth-generation allosteric inhibitors show limited antiproliferative efficacy without cetuximab. The present study aims to identify potent natural product-derived allosteric inhibitors for NSCLC using a comprehensive approach, including pharmacophore modelling, Lipinski's Rule of Five (Ro5), ADMET profiling, density functional theory, and molecular docking. Experimentally validated EGFR tyrosine kinase inhibitors (TKIs) were used to build a pharmacophore model. The validated pharmacophore model was used to screen the Natural Product Activity and Species Source database. Drug-likeness was assessed using Ro5 and ADMET to predict pharmacokinetic and toxicity profiles for further drug development. Molecular docking studies revealed that the identified natural compounds exhibited more favorable docking scores (-8.74 to -7.87 kcal/mol) than the co-crystallized ligand (-7.32 kcal/mol), indicating significant interactions with the receptor residues. Geometry optimization, electrostatic potential, and frontier molecular orbital analyses were performed to assess potential reactive sites and molecular stability. Molecular dynamics (MD) simulations, subsequent post-MD analyses, and binding free energy calculations provided additional support for these findings. The free-energy landscape revealed stable, favorable binding conformations, suggesting that they may act as allosteric inhibitors. These results highlight the potential of natural compounds to combat resistance to EGFR-TKIs in NSCLC. In particular, compounds 1 and 2 emerged as promising candidates targeting mutant EGFR and warrant further experimental validation as potential leads for NSCLC treatment.

The properties of rare-earth spinel chalcogenides depend strongly on how exchange-correlation and relativistic effects are treated. Here, the stability and the mechanical, electronic, magnetic, optical, thermoelectric and thermodynamic properties of the cubic (Fd-3m) spinels BaGd2X4 (X = S, Se) are investigated using the FP-LAPW method. The electronic and transport responses are compared under five schemes: GGA, GGA + mBJ, GGA + SOC, GGA + U and GGA + SOC + U. Negative formation energies (-2.43 and -2.13 eV/atom), a ferromagnetic ground state below two antiferromagnetic configurations, and elastic constants satisfying the Born criteria confirm stability. Pugh (4.60, 4.67) and Poisson (0.39, 0.40) ratios indicate ductility. Both compounds remain spin-polarised semiconductors under every scheme. The Hubbard term (U_eff = 6.6 eV on Gd-4f) widens the gaps to 1.60 eV (BaGd2S4) and 1.40 eV (BaGd2Se4), whereas SOC narrows them to 1.04 and 0.71 eV, with the Se compound more sensitive to relativistic corrections. The total moment of 28 μB per cell is carried mainly by Gd[3+] (≈6.9 μB) and is unchanged by SOC and U, consistent with the half-filled 4f[7] shell. The optical spectra (0-8 eV) show an absorption onset near 1 eV, a static refractive index of 2.3-3.7 depending on the scheme, and strongest absorption at 6-7.5 eV. Boltzmann transport gives positive Seebeck coefficients of about 250 μV/K at 300 K, indicating p-type conduction. Slack's model yields lattice thermal conductivities of ≈0.07 and ≈ 0.26 W m[-1] K[-1] at 300 K. Quasi-harmonic analysis (0-8 GPa, 200-800 K) shows the Debye temperature rising with pressure. These findings identify BaGd2X4 as candidate p-type thermoelectric and ultraviolet optoelectronic materials.

Magnesium hydride nanoparticles are promising combustion promoters with poorly understood atomistic mechanisms. Using ReaxFF molecular dynamics simulations, we elucidate the mechanism by which MgH2 nanoparticles promote methanol combustion. MgH2 reduces the apparent activation energy of methanol combustion by 58% and accelerates methanol decomposition. H-element tracking reveals that MgH2 draws methyl hydrogen from the formaldehyde intermediate and the abstracted hydrogen atoms are oxidized to form H2O, with 73.1% of MgH2-derived hydrogen forming H2O instead of H2, raising the CO2/CO ratio from 0.91 to 1.79 at 3000 K. Furthermore, under an equal number of nanoparticles, the MgH2 loading increases with particle size, and its promoting effect is enhanced with increasing particle size for the three investigated sizes (40, 50, and 60 Å): the 60 Å nanoparticle exhibits the highest peak decomposition rate, the fastest reactant consumption, and the most complete fragmentation, owing to its greater Mg-H bond abundance. These findings establish MgH2 as an active promoter and guide MgH2/methanol nanofluid fuel design.

The automated conversion of 2D molecular structures into physical 3D Cartesian coordinates is a foundational prerequisite for high-throughput virtual screening, structure-based drug design, and geometric deep learning. However, default 2D-to-3D embedding workflows exhibit operational biases and severe structural failure modes that are rarely quantified across large chemical libraries. Here, we present a systematic benchmark evaluating eight execution strategies across five computational suites-RDKit (two strategies), Open Babel (two strategies), HyperChem 8.0, Schrödinger LigPrep (OPLS4), and Wavefunction Spartan (two strategies)-over 33,715 biologically active compounds. To ensure evaluation on a common molecular-mechanics energy scale, all coordinates were rescored using the same MMFF94s potential energy protocol (obenergy). For each molecule, the portfolio global minimum was defined as the lowest MMFF94s energy identified within the finite collection of geometries produced by the eight evaluated strategies; it does not denote the mathematically exact minimum over the complete conformational space or a model-independent physical minimum. Our findings reveal an extreme potential energy surface (PES) sampling bias: 31,121 molecules (92.31%) had portfolio minima uniquely recovered by one strategy, whereas multi-strategy consensus occurred for 2594 molecules (7.69%). The corrected non-disjoint analysis yielded 37,516 portfolio-minimum recovery occurrences because every strategy satisfying the energy-equivalence criterion received recovery credit. Furthermore, we establish a quantitative taxonomy of structural pathologies, cataloging severe energy anomalies and probable steric overlaps (E>10[3]kcal/mol), planar coordinate retention or collapse (maxi|qi|<10[-7]Å for at least one Cartesian coordinate q∈{x,y,z}), and macrocyclic ring interpenetrations. The rdkit0 strategy produced the largest portfolio-minimum recovery count, with 13,867 recoveries (41.13%), followed by rdkit1, with 11,109 recoveries (32.95%), and obabel1, with 6482 recoveries (19.23%). Severe energy anomalies were concentrated in obabel0, which produced 1679 structures above the adopted threshold, whereas no such anomalies were detected for obabel1, rdkit0, rdkit1, or schrodinger. These results demonstrate that standard embedding pipelines operate in strongly workflow-dependent conformational basins and highlight the necessity of multi-strategy generation, common energy evaluation, explicit structural quality control, and subsequent conformational expansion for scalable, high-fidelity dataset curation.

The ability of small molecules to cross the blood-brain barrier (BBB) remains a major bottleneck in neurotherapeutic development. While experimental assays and machine learning approaches provide approximate permeability estimates, they lack atomistic insight into the underlying transport mechanisms. Here, we employ all-atom molecular dynamics simulations of a compositionally realistic BBB lipid bilayer to characterize the passive permeation of two bioactive propolis-derived compounds, Caffeic Acid Phenethyl Ester (CAPE) and Artepillin-C (ARC). Using steered molecular dynamics and umbrella sampling, we computed free energy profiles, diffusion coefficients, and permeability metrics across the membrane. CAPE shows an interfacial free-energy well (mean PMF ≈ -4.6 kcal mol-1 at d ≈ 0.8 nm) and favorable passive permeability (logPeff = +0.68). In contrast, ARC exhibits a substantial energetic barrier within the membrane core and strongly unfavorable permeability (logPeff = -6.76). The heterogeneous lipid model reproduces experimentally consistent membrane properties and reveals how lipid composition modulates transport energetics. These findings provide mechanistic insight into BBB permeability and demonstrate the utility of atomistic simulations for guiding the design of neuroactive therapeutics.

The G1202R solvent-front mutation is a major determinant of resistance to ATP-competitive anaplastic lymphoma kinase (ALK) inhibitors, yet its influence on the broader conformational pocket landscape remains poorly characterized. We used molecular dynamics (MD) simulations and complementary pocket-analysis approaches to characterize recurrent non-orthosteric cavities in wild-type (WT) and G1202R-mutant ALK. Three independent 500-ns apo MD simulations were performed per system, providing 3.0 μs of aggregate sampling. Conformations were extracted every 1 ns, yielding 1503 structures per system and 3006 overall. Each conformation was analyzed using fpocket, P2Rank, PASSer ensemble, PASSer AutoML, and PASSerRank. Top-ranked predictions were integrated through residue-set clustering and structural-anchor matching to identify recurrent dynamic pocket families without restricting discovery to predefined sites. Six prioritized dynamic pocket families, designated ALK-DP1-ALK-DP6, emerged from the non-orthosteric ranking. ALK-DP1 showed the highest integrated recurrence, with mean top-3 occurrence values of 45.7% in WT and 44.4% in G1202R and any-method top-3 occurrence in 82.7% and 81.2% of conformations, respectively. ALK-DP3 showed the largest pooled difference between receptor ensembles, with higher recurrence in G1202R but substantial replica-level heterogeneity: mean top-3 occurrence increased from 13.0% to 25.9%, whereas any-method top-3 occurrence increased from 41.1% to 60.5%. Additional recurrent families emerged de novo, revealing cavities beyond the initial structural anchors. The ATP/peri-ATP region was consistently recovered as an internal positive control. These results define a heterogeneous dynamic pocket landscape in ALK, including receptor-ensemble differences in the recurrence of specific pocket families, and provide structurally prioritized regions for subsequent ligandability and functional studies.

Phosphorene-based nanomaterials have attracted considerable interest because of Physicochemical properties. However, the influence of local molecular geometry on the properties of finite phosphorene-related molecular motifs remains insufficiently explored. In this work, density functional theory (DFT) and time-dependent density functional theory (TD-DFT) calculations are performed for four isolated cyclohexaphosphane structures used as finite molecular motifs associated with the α, β, γ, and δ phosphorene configurations. This molecular-block representation is intended to compare local structure-property trends and does not imply quantitative equivalence with periodic phosphorene sheets. Optimized geometries, vibrational spectra, frontier molecular orbitals, density of states, electrostatic surface potential (ESP), and optical properties are analyzed. The calculations reveal clear phase-dependent electronic characteristics: the α- and β-motifs exhibit larger gaps of 5.34 and 4.89 eV, respectively, whereas the γ- and δ-motifs show smaller gaps of 4.14 and 4.11 eV, together with higher chemical softness, electrophilicity, and Electronic Interaction Index values. The optical calculations also show distinct responses, with absorption maxima at 357 and 339 nm for γ and δ, respectively, both in the near-UV region. The emission-related quantities and Stokes shifts obtained from the adopted triplet-relaxation protocol further indicate phase-dependent excited-state relaxation. ESP maps reveal heterogeneous electrostatic distributions that may be useful for guiding future explicit interaction studies. Overall, the calculations establish phase-dependent molecular structure-property relationships and provide a theoretical basis for subsequent studies of molecular sensing, adsorption, and optoelectronic behavior. Biomedical applications such as targeted delivery, toxicity assessment, and photothermal therapy are considered only as prospective directions requiring explicit intermolecular, environmental, and experimental validation.

Sulfur chloride gases, including S2Cl2, SOCl2, and SO2Cl2, are highly toxic and reactive industrial chemicals that pose significant environmental and safety risks. In this work, density functional theory (DFT) calculations were employed to systematically investigate the structural stability, electronic properties, adsorption behaviour, and optical response of pristine Mo2C and oxygen-functionalized Mo2CO2 MXenes toward these hazardous gases. Structural analysis confirms the high intrinsic stability of Mo2C, which is further enhanced upon O-termination. Electronic structure calculations reveal semiconducting characteristics with moderate energy gap modulation induced by surface functionalization. Adsorption energy results demonstrate exceptionally strong and spontaneous interactions between sulfur chloride molecules and Mo2C, with highly exothermic values indicative of dissociative chemisorption. Although oxygen termination moderates the adsorption strength, Mo2CO2 maintains substantial binding energies while providing improved structural robustness. Optimized geometries confirm bond elongation and cleavage of S-Cl and S-S bonds, highlighting irreversible capture mechanisms rather than reversible physisorption. Comprehensive electronic analyses, including PDOS, HOMO-LUMO distributions, natural bond orbital (NBO) charge transfer, and noncovalent interaction (NCI) analysis, reveal pronounced orbital hybridization, significant charge redistribution, and cooperative attractive interactions at the gas-surface interface. Time-dependent DFT calculations further show that gas adsorption induces controlled modulation of optical absorption without disrupting the intrinsic spectral characteristics of the MXene substrates. Overall, the results identify Mo2C and Mo2CO2 MXenes as highly promising scavenging materials for the effective capture and neutralization of toxic sulfur chloride gases, offering potential applications in environmental remediation and hazardous gas mitigation technologies. Future studies involving experimental validation, defect engineering, and realistic environmental conditions are expected to further optimize the practical performance of these MXene-based scavenging systems.