We report nonadiabatic dynamics computations on C2H4+ initiated on a coherent superposition of the five lowest cationic states, employing the Quantum Ehrenfest method. In addition to the totally symmetric carbon–carbon double bond stretch and carbon-hydrogen stretches, we see that the three non-totally symmetric modes become stimulated; torsion and three different CH stretching patterns. Thus, a coherent superposition of states, of the type involved in an attochemistry experiment, leads to the stimulation of specific non-totally symmetric motions. The computations were also performed on the specific combination of the A and C states. In each case normal mode 9 (cis-asymmetric H2CCH2 stretch), out of the set of non-totally-symmetric normal modes, dominates. Thus, we can steer the nuclear motion along specific non-totally symmetric normal modes using a defined coherent superposition.
Light-driven molecular rotary motors are nanometric machines able to convert light into unidirectional motions. Several types of molecular motors have been developed to better respond to light stimuli, opening new avenues for developing smart materials ranging from nanomedicine to robotics. They have great importance in the scientific research across various disciplines, but a detailed comprehension of the underlying ultrafast photophysics immediately after photo-excitation, that is, Franck–Condon region characterization, is not fully achieved yet. For this aim, it is first required to rely on an accurate description at ab initio level of the system in this potential energy region before performing any further step, that is, dynamics. Thus, we present an extensive investigation aimed at accurately describing the electronic structure of low-lying electronic states (electronic layout) of a molecular rotor in the Franck–Condon region, belonging to the class of overcrowded alkenes: 9-(2-methyl-2,3-dihydro-1H-cyclopenta[a]naphthalen-1-ylidene)-9H-fluorene. This system was chosen since its photophysics is very interesting for a more general understanding of similar compounds used as molecular rotors, where low-lying electronic states can be found (whose energetic interplay is crucial in the dynamics) and where the presence of different substituents can tune the HOMO-LUMO gap. For this scope, we employed different theory levels within the time-dependent density functional theory framework, presenting also a careful comparison adopting very accurate post Hartree–Fock methods and characterizing also the different conformations involved in the photocycle. Effects on the electronic layout of different functionals, basis sets, environment descriptions, and the role of the dispersion correction were all analyzed in detail. In particular, a careful treatment of the solvent effects was here considered in depth, showing how the implicit solvent description can be accurate for excited states in the Franck–Condon region by testing both linear-response and state-specific formalisms. As main results, we chose two cost-effective (accurate but relatively cheap) theory levels for the ground and excited state descriptions, and we also verified how choosing these different levels of theory can influence the curvature of the potential via a frequency analysis of the normal modes of vibrations active in the Raman spectrum. This theoretical survey is a crucial step towards a feasible characterization of the early stage of excited states in solution during photoisomerization processes wherein multiple electronic states might be populated upon the light radiation, leading to a future molecular-level interpretation of time-resolved spectroscopies.
Data analysis is a major task for Computational Chemists. The diversity of modeling tools currently available in Computational Chemistry requires the development of flexible analysis tools that can adapt to different systems and output formats. As a contribution to this need, we report the implementation of goChem, a versatile open-source library for multiscale analysis of computational chemistry data. The library, written in and for the Go programming language, allows for easy integration of different levels of theory, in an easy-to-use API, allowing the development of both one-use and complex analysis programs in Go. We describe the library and detail some selected applications that illustrate the capabilities and potential of this tool. The library is available at http://gochem.org.
Cyclooxygenase-2 (COX-2) is an enzyme that plays a crucial role in inflammation by converting arachidonic acid into prostaglandins. The overexpression of enzyme is associated with conditions such as cancer, arthritis, and Alzheimer's disease (AD), where it contributes to neuroinflammation. In silico virtual screening is pivotal in early-stage drug discovery; however, the absence of coding or machine learning expertise can impede the development of reliable computational models capable of accurately predicting inhibitor compounds based on their chemical structure. In this study, we developed an automated KNIME workflow for predicting the COX-2 inhibitory potential of novel molecules by building a multi-level ensemble model constructed with five machine learning algorithms (i.e., Logistic Regression, K-Nearest Neighbors, Decision Tree, Random Forest, and Extreme Gradient Boosting) and various molecular and fingerprint descriptors (i.e., AtomPair, Avalon, MACCS, Morgan, RDKit, and Pattern). Post-applicability domain filtering, the final majority voting-based ensemble model achieved 90.0% balanced accuracy, 87.7% precision, and 86.4% recall on the external validation set. The freely accessible workflow empowers users to swiftly and effortlessly predict COX-2 inhibitors, eliminating the need for any prior knowledge in machine learning, coding, or statistical modeling, significantly broadening its accessibility. While beginners can seamlessly use the tool as is, experienced KNIME users can leverage it as a foundation to build advanced workflows, driving further research and innovation.
Carbonyl complexes of metals with an α-diimine ligand exhibit both emission and ligand-selective photodissociation from MLCT states. Studying this photodissociative mechanism is challenging for experimental approaches due to an ultrafast femtosecond timescale and spectral overlap of multiple photoproducts. The photochemistry of a prototypical system is investigated with non-adiabatic dynamic simulations. Obtained 86 fs lifetime of the bright state and 13% quantum yield are in good agreement with experimental data. The present simulations suggest a ballistic mechanism of photodissociation, which is irrespective of the occupied electronic state. This is in contrast to the previously established mechanism of competitive intersystem crossing and dissociation. Selectivity of axial photodissociation is shown to be caused by the absence of an avoided crossing in the equatorial direction.
Phosphodiesterase 5 (PDE5) inhibitors have shown great potential in treating Alzheimer's disease by improving memory and cognitive function. In this study, we evaluated fluspirilene, a drug commonly used to treat schizophrenia, as a potential PDE5 inhibitor using computational methods. Molecular docking revealed that fluspirilene binds strongly to PDE5, supported by hydrophobic and aromatic interactions. Molecular dynamics simulations confirmed that the fluspirilene–PDE5 complex is stable and maintains its structural integrity over time. Binding energy calculations further highlighted favorable interactions, indicating that the drug forms a strong and stable bond with PDE5. Additional analyses, including studies of protein dynamics and energy landscape mapping, revealed how the drug interacts dynamically with PDE5, adapting to different conformations and maintaining stability. These findings suggest that fluspirilene may modulate PDE5 activity, potentially offering therapeutic benefits for Alzheimer's disease. This study provides strong evidence for repurposing fluspirilene as a treatment for Alzheimer's and lays the foundation for further experimental and clinical investigations.
This study aims to shed light on the mechanism and kinetics of 1,4-dioxane degradation by hydroxyl radical (OH) across various solvation conditions to evaluate electronic and structural properties at the MP2/aug-cc-pVTZ level. Transition states (TS) structures determined in the gas phase and SMD solvation model reveal similar hydrogen abstraction patterns. In contrast, the explicit solvation model (ES) introduces significant changes, suggesting a kinetic preference for axial pathways. The reaction rate constants, employing Deformed Transition State Theory (d-TST), are consistently higher for axial abstraction. The preference for axial hydrogen abstraction, solvation effects on transition states, and temperature-dependent rate constants are highlighted. Furthermore, the identification of carbon–carbon orbital distortion suggests potential bond breakage. This research provides valuable insights into the reaction between 1,4-dioxane and OH radical across different solvation models and enhances the understanding of the advanced oxidative process.
Continuum solvation models such as the polarizable continuum model and the conductor-like screening model are widely used in quantum chemistry, but their application to large biosystems is hampered by their computational cost. Here, we report the parametrization of the Miertus–Scrocco–Tomasi (MST) model for the prediction of hydration free energies of neutral and ionic molecules based on the domain decomposition formulation of COSMO (ddCOSMO), which allows a drastic reduction of the computational cost by several orders of magnitude. We also introduce several novelties in MST, like a new definition of atom types based on hybridization and an automatic setup of the cavity for charged regions. The model is parametrized at the B3LYP/6-31+G(d) and PM6 levels of theory and compared to the performance of IEFPCM/MST. Then, we demonstrate the robustness of the parametrization on the SAMPL2, SAMPL4, and C10 datasets. The ddCOSMO/MST models provide errors of ~0.8 and ~3.2 kcal/mol for neutrals and ions, respectively, showing a remarkable balanced and accurate description of cations and anions.
A correction to the MMFF molecular mechanics model, based on a neural network trained to reproduce conformer energy differences obtained from ωB97X-V/6-311+G(2df,2p)[6-311G*]//MMFF calculations is described. It is supported for molecules containing H, C, N, O, F, S, Cl, and Br. The correction adds only slightly to the cost of MMFF, and the resulting corrected model is several orders of magnitude faster than ωB97X-V/6-311+G(2df,2p)[6-311G*]. It properly identifies the lowest energy conformer for 82% of the molecules in a test set of flexible organic molecules (3553 total conformers), compared with 38% for MMFF. While the corrected MMFF model cannot be expected to provide sufficiently accurate Boltzmann weights for use in spectra and property calculations on flexible molecules, it is able to reduce the number of “reasonable” conformers that need to be passed on to more rigorous computational models, that can.
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