Journal of Computational Chemistry

Shila, Shamima Rahman; Almatarneh, Mansour H.; Suha, Humaera Noor; Hossain, Istiak; Abdalla, Sahar; Bari, Md Ahsan Ul; Poirier, Raymond A.; Uddin, Kabir M.

doi: 10.1002/jcc.70411pmid: N/A

Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by cognitive impairment and neuronal loss. Aberrant activation of receptor‐interacting protein kinase 1 (RIPK1) plays a critical role in neuroinflammation and programmed neuronal death, making it an attractive therapeutic target. In this computational study, 16 isoxazolidine derivatives (1–16) were evaluated alongside seven reference inhibitors to identify potential RIPK1 blockers. Molecular docking analyses revealed that compound 7 exhibited the highest binding affinity toward RIPK1 (PDB ID: 7XMK), with a binding energy of −9.0 kcal mol−1, outperforming established inhibitors and demonstrating broad activity against AD‐related targets. Density functional theory calculations showed a HOMO–LUMO energy gap of 5.209 eV, indicating favorable electronic stability. Compound 7 complied with Lipinski's rule of five and Veber's criteria and displayed excellent predictive ADMET properties, including high human intestinal absorption (HIA = 1.0), strong blood–brain barrier permeability (BBB = 0.991), and low predicted toxicity. Molecular dynamics (MD) simulations conducted over 100 ns at temperatures ranging from 300 to 320 K confirmed the stability of the RIPK1–compound 7 complex. The root‐mean‐square deviation (RMSD) values ranged from 5.2 to 14.0 Å (0.52–1.40 nm), indicating acceptable structural fluctuations throughout the simulation. Additionally, the radius of gyration (Rg) ranged from 2.8 to 3.8 nm, indicating that the complex maintained a relatively stable, compact conformation throughout the simulation. Principal component analysis further supported these findings, yielding cosine similarity values of 0.86–0.95. Collectively, these results highlight compound 7 as a promising RIPK1 inhibitor with favorable pharmacokinetic, electronic, and dynamic properties, underscoring its potential as a therapeutic candidate for AD.

doi: 10.1002/jcc.70414pmid: N/A

Tao, Yunwen; Wang, Xianlong; Zou, Wenli; Kraka, Elfi

doi: 10.1002/jcc.70432pmid: N/A

Turnstile rotation is a well‐known polytopal rearrangement mechanism in coordination compounds, yet its computational description is often hindered by the challenges of conventional internal coordinates to represent such collective, large‐amplitude motions. In this work, we present a mathematical formulation for generalized N$$ N $$‐arm turnstile rotation, a dedicated PyMOL plugin (gTA) for intuitive structure visualization and a command‐line utility (gTA‐cli) for structure manipulation. On this basis, we develop a practical computational workflow that combines turnstile rotation‐driven relaxed scans with two downstream routes to locate the transition states. The generality of the approach is demonstrated through five chemically diverse case studies, ranging from classical rearrangements in SF4$$ {}_4 $$, IF7$$ {}_7 $$, and [Co(en)3$$ {}_3 $$]3+$$ {}^{3+} $$ to previously unexplored fluxional processes in bismuth and nickel complexes. In all cases, the method enables the identification of transition states associated with pronounced polytopal rearrangements that are difficult to access using standard coordinate schemes. These results establish generalized turnstile rotation as a fundamental molecular motion and highlight its potential as a broadly applicable strategy for studying fluxionality and dynamic stereochemistry in coordination chemistry. The PyMOL plugin gTA and the Python utility gTA‐cli with the associated relaxed scan workflows are open‐source and freely available on Github at https://github.com/smutao/gTA‐plugin and https://github.com/smutao/gTA‐workflow, respectively.

doi: 10.1002/jcc.70435pmid: 42290124

Gholiee, Yasin

doi: 10.1002/jcc.70441pmid: N/A

This study investigates metal–ligand bonding in planar pincer complexes [(tpy)M(X)]+, where M is Ni(II), Pd(II), or Pt(II) and X is a halide, using DFT and energy decomposition analysis. It shows that electrostatic interactions dominate the overall stability, while interaction strength decreases from fluoride to iodide. Orbital interactions contribute significantly (44.4%–50.2%) to the total attraction and are analyzed in detail under C2v symmetry through a1, a2, b1, and b2 components, revealing different metal–ligand overlap patterns. σ‐type interactions are the main orbital contribution (62.7%–76.1%), followed by π interactions (19.4%–29.6%), including in‐plane and out‐of‐plane components. δ‐type interactions are minor (4.8%–7.7%) but arise from metal–ligand π* overlap. Charge transfer trends from ligand to metal correlate well with both electrostatic and total interaction energies, supporting a consistent bonding description across the series.

Conradie, Jeanet

doi: 10.1002/jcc.70399pmid: N/A

The Jahn–Teller (JT) effect plays a central role in determining the structure and electronic properties of open‐shell transition‐metal complexes, yet its reliable theoretical description remains sensitive to the choice of density functional, basis set, and molecular model. In close analogy to the well‐established behavior of d9 Cu(II) systems, low‐spin d7 Ni(III) complexes exhibit an E ⊗ e vibronic instability that gives rise to a multidimensional (“Mexican hat”) potential energy surface and dynamic Jahn–Teller behavior. In this work, we present a systematic density functional theory investigation of the Jahn–Teller effect in a series of seven pseudo‐octahedral Ni(III) complexes bearing bidentate ligands with O,O or N,N donor atoms, of varying size and rigidity. Geometry optimizations initiated from both axially elongated and compressed starting structures were performed under symmetry constraints to map the Jahn–Teller potential energy surface. At the TPSSh/def2‐TZVP level, optimizations from elongated and compressed starting points identify elongated structures as minima and compressed structures as first‐order saddle points. The presence of multiple symmetry‐equivalent elongated minima and low barriers between them is consistent with a dynamically averaged Jahn–Teller distortion rather than a purely static one. The resulting axial–equatorial bond length differences (ΔR ≈ 0.16–0.23 Å) and Jahn–Teller stabilization energies (ΔEJT ≈ 0.02–0.04 eV) indicate a moderate but systematic preference for axial elongation across the ligand series. Frontier molecular orbital and spin density analyses reveal that the distortion is driven by lifting of the eg orbital degeneracy in the low‐spin d7 configuration, with axial elongation stabilizing the predominantly Ni‐centered dz2$$ {d}_{z^2} $$‐based HOMO. Comparative calculations using PW6B95‐D3, M06, B3LYP, and OLYP functionals, as well as alternative triple‐ζ and double‐ζ (def2‐SVP) basis sets, confirm that the qualitative Jahn–Teller behavior and ligand‐dependent trends are robust, while quantitative differences emphasize the importance of hybrid meta‐GGA and dispersion‐corrected approaches. Continuum solvation calculations (water, acetonitrile, and dichloromethane) demonstrate that solvent effects do not alter the qualitative Jahn–Teller distortion pattern or energetic ordering. In addition, simplified ligand models are shown to reliably reproduce the essential features of the Jahn–Teller distortion when the immediate coordination environment is preserved, offering a computationally efficient strategy for mechanistic and methodological studies, albeit with limitations for quantitative spectroscopic predictions and for capturing intermolecular effects such as dispersion and crystal packing. Overall, the results establish a consistent electronic‐structure description of Jahn–Teller distortions in pseudo‐octahedral Ni(III) complexes and highlight both the capabilities and limitations of current DFT approaches for treating vibronically active transition‐metal systems.

Meng, Yizi; Ning, Ziye; Tang, Bowen; Zhang, Yanying; Cui, Leilei; Liu, Xiaoning; Lv, Lingling

doi: 10.1002/jcc.70445pmid: N/A

Ionic thermally activated delayed fluorescence (iTADF) materials are promising for organic optoelectronics due to their solution processability and structural tunability, yet the synergistic regulation of reverse intersystem crossing (RISC) by heteroatom anchoring, heavy‐atom effects, and vibronic coupling remains underexplored. Four molecules (AC‐TPPO+, AC‐TPPS+, AC‐TPPO[Br], and AC‐TPPS[Br]) are systematically investigated via density functional theory (DFT), TD‐DFT, and excited‐state dynamics. All molecules feature twisted donor‐acceptor conformations, enabling spatial separation of frontier molecular orbitals and a small singlet–triplet energy gap (ΔEST) for RISC. The bromide counterion (Br−) induces a heavy‐atom effect, enhancing spin‐orbit coupling strength and accelerating RISC rates by two orders of magnitude. Low‐to‐medium‐frequency vibrations further facilitate RISC by driving S1 and T1 potential energy surfaces to near‐degeneracy. The optimal AC‐TPPO[Br] achieves a high delayed fluorescence quantum yield (ΦDF = 74.88%) with nearly 100% room‐temperature TADF contribution. This work provides a synergistic design paradigm and theoretical guidance for high‐performance OLED emitters.

Arya, Neha; Charde, Vaibhav; Kumar, Vijay; Ranade, Anagha; Meena, Ajay K.; Srikanth, Narayanam; Acharya, Rabinarayan; Mallajosyula, Sairam S.

doi: 10.1002/jcc.70391pmid: 42283337

Polyphenolic phytochemicals derived from Phyllanthus emblica (amla) exhibit diverse chemical functionalities that are poorly represented in existing biomolecular force fields, limiting reliable molecular simulations of their structure and interactions. Here, we report the systematic development and validation of an all‐atom, additive CHARMM force‐field parameter set for major Phyllanthus emblica phytochemicals, including gallic acid, ellagic acid, quinic acid, ascorbic acid, ethyl gallate, and their relevant ionized forms. Parameterization was performed following established CHARMM protocols using high‐level quantum mechanical reference data, with targeted fragmentation and model‐compound strategies employed to address challenging conjugated and polyphenolic motifs. The optimized parameters accurately reproduce quantum mechanical conformational energetics, hydrogen‐bonding interactions, and vibrational characteristics, exhibiting low deviations in both energies and geometries. Further validation is demonstrated through molecular dynamics simulations of experimentally resolved crystal structures and long‐timescale protein–ligand complexes, where the parameters preserve structural stability and native interaction patterns. This work expands the chemical space accessible to CHARMM‐based simulations and provides a robust, transferable framework for realistic molecular modeling of structurally complex natural products relevant to biomolecular recognition and function.

V. N., Dhilshada; Ramachandran, Neeraj; Chattopadhyaya, Mausumi

doi: 10.1002/jcc.70437pmid: N/A

The growing demand for sustainable hydrogen production has intensified research into photocatalytic water splitting driven by solar energy. Graphitic carbon nitride (g‐C3N4), a metal‐free polymeric semiconductor with visible‐light activity, tunable electronic structure, low cost, and environmental compatibility, has emerged as a promising photocatalyst. Nevertheless, pristine g‐C3N4 is limited by insufficient visible‐light absorption, sluggish charge transport, rapid electron–hole recombination, and low surface reactivity. Heterojunction engineering has therefore become a key strategy to overcome these intrinsic limitations and enhance photocatalytic efficiency. Distinct from conventional experimental or performance‐oriented reviews, this work presents a computationally driven and methodology‐oriented review that explicitly addresses how to model g‐C3N4‐based heterojunction photocatalysts using density functional theory (DFT). Rather than merely summarizing reported efficiencies, this review provides a step‐by‐step conceptual and practical framework that enables readers to rationally construct, analyze, and optimize heterojunction photocatalysts at the atomic scale. The review begins with the structural and electronic fundamentals of g‐C3N4, followed by a mechanistic overview of photocatalytic water splitting, including light absorption, charge generation, interfacial charge separation, and surface redox reactions. Various heterojunction architectures: Type I, Type II, Z‐scheme, S‐scheme, p–n junctions, Schottky interfaces, and multicomponent systems are systematically discussed, with particular emphasis on interfacial charge‐transfer mechanisms revealed by first‐principles calculations. A key contribution of this review is the consolidation of DFT‐based modeling protocols and descriptors essential for evaluating and enhancing photocatalytic performance. These include structural stability (lattice mismatch, phonon dispersion, and ab initio molecular dynamics), electronic properties (band structure, density of states, and band‐edge alignment), charge‐transfer characteristics (work function, charge density difference, planar‐averaged charge density, and Bader charge analysis), optical properties (absorption spectra, dielectric function, optical band gap, and solar‐to‐hydrogen efficiency), and carrier transport descriptors (effective mass and carrier mobility). In addition, DFT‐based reaction pathway analysis is discussed to elucidate hydrogen and oxygen evolution mechanisms at heterojunction interfaces. Importantly, this review highlights computational strategies to enhance photocatalytic activity, including strain engineering, external electric fields, defect and dopant engineering, and interface optimization, providing clear guidance on how these approaches can be implemented and interpreted within a DFT framework. Challenges related to computational cost, finite‐size effects, and realistic interface construction are critically evaluated, along with practical mitigation strategies. Emerging directions such as beyond‐DFT methods (GW and TDDFT), machine learning, and high‐throughput screening are also discussed as powerful tools for accelerating heterojunction discovery. By integrating model construction principles, computational descriptors, and activity‐enhancement strategies into a unified roadmap, this review serves as a practical guide for researchers seeking to design, model, and optimize g‐C3N4‐based heterojunction photocatalysts using DFT, thereby bridging the gap between theoretical modeling and experimental realization of efficient, stable, and scalable systems for solar‐driven hydrogen production.

Epih, Jonathan; Arya, Anjali; Gomrok, Saghar; Cheng, Qianyi

doi: 10.1002/jcc.70434pmid: N/A

The glycine N‐methyltransferase (GNMT) reaction was examined using an integrated workflow combining molecular dynamics (MD), quantum mechanical (QM) cluster calculations, and machine learning (ML) analysis. Instead of relying on a single crystal‐like conformation, multiple MD simulations were used to sample diverse reactant (SAM + glycine bound to GNMT) and product (SAH + sarcosine bound to GNMT) state geometries for QM cluster modeling. Across more than 150 QM‐cluster models constructed by the Residue Interaction Network ResidUe Selector (RINRUS) from selected MD frames with and without explicit waters, the computed activation and reaction free energies span broad ranges (7 to 25 kcal mol−1 and −36 to +3 kcal mol−1), demonstrating a strong dependence on the initial MD conformation. Product‐state consistently yields lower reaction barriers, while explicit water introduces only small shifts in energetics and preserves the relative ordering among frames. The two‐coordinate potential energy surface (PES) offers only limited insight and cannot fully account for the observed energetic variability. These QM‐cluster models were further analyzed using machine‐learning methods to identify structural descriptors that correlate with the observed energy variations and provide insight into their structural origin. ML models trained on multiple feature representations show that the donor–methyl–acceptor distances are the most informative and yield the strongest predictive accuracy, while higher dimensional solvent‐ or residue‐based features contribute comparatively little. Overall, the results highlight the importance of conformational sampling for reliable QM‐cluster energetics and point toward more expressive structure‐to‐property representations for analyzing enzymatic reactions.