Optimization of parameters for semiempirical methods V: modification of NDDO approximations and application to 70 elements

Enhanced catalytic activity of polyethylene terephthalate hydrolase by structure-guided loop-focused iterative mutagenesis strategy

The accumulation of polyethylene terephthalate (PET) waste has caused significant environmental pollution. Although biological depolymerization offers a promising solution, its efficiency remains constrained by the limited activity of PET-degrading enzymes. In this study, we designed a Structure-guided Loop-focused Iterative Mutagenesis (SLIM) strategy and rationally engineered the PET degradation enzyme ICCG for higher activity. The strategy was designed by demonstrating the critical role of the β8-α6 loop in type Ⅰ enzymes, which has currently not been reported. The best variant obtained, YITA (H183Y/L202I/I208T/T153A), exhibited 4.46-fold higher hydrolytic activity on amorphous PET at 72 °C compared to ICCG, outperforming other PET hydrolases, and exhibited superior degradation activity on real substrates such as cake containers and PET fibers. Conformational analysis revealed the key role of the remodeled β8-α6 loop in substrate binding and overall stability. Collectively, this study explores a promising approach to modifying PET hydrolase and lays a theoretical foundation for advancing bio-circular economy.

Efficiency of QM/MM optimization and fragment molecular orbital calculations for investigating interactions between zinc metalloprotease and its inhibitors

In this study, we analyzed the binding characteristics of the zinc (Zn) metalloprotease pseudolysin (PLN) derived from Pseudomonas aeruginosa and its inhibitors at the electronic level to elucidate their interactions with PLN and propose novel inhibitors against PLN. A PLN contains a Zn ion in its active site, and describing the electronic states around the Zn ion accurately using conventional molecular mechanics (MM) calculations is challenging. Therefore, we applied a quantum mechanics/molecular mechanics (QM/MM) hybrid approach to optimize the structures of PLN-inhibitor complexes and verified that the structure obtained by QM/MM closely resembled the experimental one. Furthermore, using the ab initio fragment molecular orbital (FMO) method, we performed a high-precision analysis of specific interactions at the electronic level between PLN amino acid residues and each inhibitor, achieving computational results that reproduced the trend of inhibitory effectiveness observed in previous experiments. Based on the FMO results, we propose a new inhibitor with higher binding affinity for PLN, which is potentially capable of effectively inhibiting its enzymatic function.

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.

On the Difficulty to Rescore Hits from Ultralarge Docking Screens

Docking-based virtual screening tools customized to mine ultralarge chemical spaces are consistently reported to yield both higher hit rates and more potent ligands than that achieved by conventional docking of smaller million-sized compound libraries. This remarkable achievement is however counterbalanced by the absolute necessity to design an efficient postprocessing of the millions of potential virtual hits for selecting a few chemically diverse compounds for synthesis and biological evaluation. We here retrospectively analyzed ten successful ultralarge virtual screening hit lists that underwent in vitro binding assays, for binding affinity prediction using eight rescoring methods including simple empirical scoring functions, machine learning, molecular-mechanics and quantum-mechanics approaches. Although the best predictions usually rely on the most sophisticated methods, none of the tested rescoring methods could robustly distinguish known binders from inactive compounds, across all assays. Energy refinement of protein-ligand complexes, prior to rescoring, marginally helped molecular mechanics and quantum mechanics approaches but deteriorates predictions from empirical and machine learning scoring functions.

Polycyclic (anti)aromatic hydrocarbons: interstellar formation and spectroscopic characterization of biphenylene and benzopentalene

Formation of biphenylene (C6H4)2 and its isomer benzopentalene, C12H8, may act as a consumption route for ortho-benzyne (o-C6H4) in interstellar clouds such as TMC-1. MRCI-F12 and CCSD(T)-F12 potential energy surfaces show that o-C6H4 dimerization is possible through a C2h-symmetry single-bond association to a (C6H4)2 precursor before isomerization to (C6H4)2 and subsequently C12H8. Formation of a bimolecular product set from either species is energetically hindered, allowing (C6H4)2 and C12H8 to stabilize radiatively. To remedy the dearth of spectroscopic data for these species, anharmonic frequencies from explicitly-correlated quartic force fields (QFFs) for o-C6H4 and c-C4H4 are employed to reparameterize the semiempirical method PM6 for use in lower-cost QFFs for (C6H4)2 and C12H8. In both cases, at least one reparameterized PM6-QFF spectrum results in the prominent C-H stretch and symmetric C-H out-of-plane-bend features to be accurately predicted with respect to gas-phase experiment or the B3LYP/N07D anharmonic absorption spectrum. B3LYP/N07D accurately recreates the experimental infrared spectrum of (C6H4)2, showing the utility of this method for spectral prediction of small and midsize polycyclic hydrocarbons on the whole. For larger systems, reparameterized PM6-QFF spectra can reproduce the most important infrared features for a species. B3LYP/N07D cascade emission spectra show that the 730 cm-1 C-H symmetric out-of-plane bending feature dominates the emission spectrum of (C6H4)2, while the spectrum of C12H8 becomes characterized by the collective set of C-H out-of-plane bends. As such, infrared emission spectra of (C6H4)2 will likely be overshadowed by C2H2. Derivatives such as cyanobiphenylene are likely better targets for infrared observation.

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.

On-Resin Assembly of Macrocyclic Inhibitors of Cryptococcus neoformans May1: A Pathway to Potent Antifungal Agents

Macrocyclic inhibitors have emerged as a privileged scaffold in medicinal chemistry, offering enhanced selectivity, stability, and pharmacokinetic profiles compared to their linear counterparts. Here, we describe a novel, on-resin macrocyclization strategy for the synthesis of potent inhibitors targeting the secreted protease Major Aspartyl Peptidase 1 in Cryptococcus neoformans, a pathogen responsible for life-threatening fungal infections. By employing diverse aliphatic linkers and statine-based transition-state mimics, we constructed a focused library of 624 macrocyclic compounds. Screening identified several subnanomolar inhibitors with desirable pharmacokinetic and antifungal properties. Lead compound 25 exhibited a Ki of 180 pM, significant selectivity against host proteases, and potent antifungal activity in culture. The streamlined synthetic approach not only yielded drug-like macrocycles with potential in antifungal therapy but also provided insights into structure-activity relationships that can inform broader applications of macrocyclization in drug discovery.

Self-sacrificial synthesis of Cu3(HHTP)2 on Cu substrate for recyclable NH3 gas adsorption with energy-efficient photothermal regeneration

The efficient adsorption and removal of toxic gases, particularly ammonia (NH3), remains a critical challenge in environmental management and industrial safety. Metal-organic frameworks (MOFs) have emerged as promising gas adsorbents due to their tunable structures and high surface area. However, the strong interaction between NH3 and MOFs poses challenges for the regeneration and reusability of MOF adsorbents, often requiring energy-intensive desorption methods. This study proposes a sustainable approach for regenerating adsorption sites for recyclable gas adsorbents. We present a facile method for the direct synthesis of Cu3(HHTP)2 on a Cu mesh substrate (Cu3(HHTP)2@Cu), utilizing the Cu metal itself as a precursor to eliminate the need for external metal sources. The resulting Cu3(HHTP)2@Cu serves as a recyclable NH3 adsorbent, leveraging the π-conjugated hexahydroxytriphenylene (HHTP) ligand for photothermal conversion under sunlight irradiation, where photo-generated heat facilitates NH3 desorption. The study further explores the effect of an external voltage on the NH3 adsorption performance and crystalline structure of Cu3(HHTP)2@Cu. Our findings demonstrate that Cu3(HHTP)2@Cu achieves efficient NH3 desorption through a minimally invasive and energy-efficient mechanism, addressing the limitations of conventional adsorbents.

Vibrational Partition Functions from Bond Order and Populations Relationships

A novel method is presented that computes harmonic vibrational partition functions from bond orders and population relationships (QBOP). The QBOP model first computes zero-point energies (ZPEs) and net vibrational bond energies from our earlier zero-point energies from bond orders and populations (ZPE-BOP) model and then maps these variables to calculate the harmonic vibrational partition function. Combined with traditional rotational, translational, and electronic partition function approximations, the method allows the approximate calculation of finite temperature thermal effects without a Hessian calculation. The method uses a total of 12 parameters that have been fitted to B3LYP/cc-pVTZ+1 d data for first-row elements: H, Li, Be, B, C, N, O, and F. The model is benchmarked to traditional semiempirical models (i.e., AM1, PM6, PM7, and XTB-2) and it is found that QBOP-1 provides similar results. This work shows a novel way to obtain useful thermal energy calculations without a costly Hessian calculation, and thereby shifting standard bottlenecks in computational chemistry applications.

Correction: The p-block challenge: assessing quantum chemistry methods for inorganic heterocycle dimerizations

Correction for 'The p-block challenge: assessing quantum chemistry methods for inorganic heterocycle dimerizations' by Thomas Gasevic et al., Phys. Chem. Chem. Phys., 2024, 26, 13884-13908, https://doi.org/10.1039/D3CP06217A.

Understanding Conformation Importance in Data-Driven Property Prediction Models

The prediction of molecular properties is essential in chemoinformatics and has many applications in drug discovery and materials design. Molecular representations play a key role in the prediction models to achieve high prediction accuracy. Nevertheless, appropriate molecular descriptors, including the utilization of conformational information, have been unclear due to a lack of systematic analysis of property prediction models and control. This study investigates the influence of using multiple conformers in machine learning-based property prediction, comparing two- and three-dimensional descriptors using three independent data sets: a large-scale quantum mechanical property, a medium-scale melting point, and small-scale enantioselective chemical reaction data sets. One unique aspect of this study is creating these carefully controlled data sets for models' performance evaluation in conformational diversity and the target property's dependence on conformation. Our findings show that using all available conformers as simple data augmentation consistently achieves high prediction accuracy among aggregation approaches, followed by mean aggregation. Furthermore, Uni-Mol, an end-to-end prediction model utilizing atomic coordinates and elements, combined with the ground-truth conformation, significantly outperformed traditional 2D and 3D descriptors and predicted conformational-sensitive properties with high accuracy. Although the prediction accuracy of the Uni-Mol model significantly decreased using the wrong conformers, it still outperformed two-dimensional extended connectivity fingerprints, which showed higher prediction accuracy than most of the tested 3D descriptors.

Investigation of Electrostatic Effects on Enyzme Catalysis: Insights from Computational Simulations of Monoamine Oxidase A Pathological Variants Leading to the Brunner Syndrome

Brunner syndrome is a rare genetic disorder characterized by impulsive aggressiveness and intellectual disability, which is linked to impaired function of the monoamine oxidase A (MAO-A) enzyme. Patients with specific point mutations in the MAOA gene have been reported to exhibit these symptoms, along with notably elevated serotonin levels, which suggest a decreased catalytic performance of the mutated MAO-A enzymes. In this study, we present multiscale molecular simulations focusing on the rate-limiting step of MAO-A-catalyzed serotonin degradation for the C266F and V244I variants that are reportedly associated with pathologies characteristic of the Brunner syndrome. We found that the C266F mutation causes an approximately 18,000-fold slowdown of enzymatic function, which is equivalent to a MAOA gene knockout. For the V244I mutant, a somewhat smaller, yet still significant 300-fold slowdown has been estimated. Furthermore, we conducted a comprehensive comparison of the impact of enzyme electrostatics on the catalytic function of the wild-type (WT) MAO-A and both aforementioned mutants (C266F and V244I), as well as on the E446K mutant investigated in one of our earlier studies. The results have shown that the mutation induces a noteworthy change in electrostatic interactions between the reacting moiety and its enzymatic surroundings, leading to a decreased catalytic performance in all of the considered MAO-A variants. An analysis of mutation effects supported by geometry comparison of mutants and the wild-type enzyme at a residue level suggests that a principal driving force behind the altered catalytic performance of the mutants is subtle structural changes scattered along the entire enzyme. These shifts in geometry also affect domains most relevant to catalysis, where structural offsets of few tenths of an Å can significantly change contribution to the barrier of the involved residues. These results are in full agreement with the reasoning derived from clinical observations and biochemical data. Our research represents a step forward in the attempts of using fundamental principles of chemical physics in order to explain genetically driven pathologies. In addition, our results support the view that the catalytic function of enzymes is crucially driven by electrostatic interactions.

Evaluation of Semiempirical Quantum Mechanical Methods for Zr-Based Metal-Organic Framework Catalysts

Zr-based metal-organic frameworks (MOFs) are typically employed in heterogeneous catalysis due to their porosity, chemical and thermal stability, and well-defined active sites. Density functional theory (DFT) is the workhorse to compute their electronic structure; however, it becomes very costly when dealing with reaction mechanisms involving large unit cells and vast configurational spaces. Semiempirical quantum mechanical (SQM) methods appear as an alternative approach to simulate such chemical systems at low computational cost, but their feasibility to model catalysis with MOFs is still unexplored. Thus, here we present a benchmark study on UiO-66 to evaluate the performance of SQM methods (PM6, PM7, GFN1-xTB, GFN2-xTB) against hybrid DFT (M06). We evaluate defective nodes, ligand exchange reactions, barrier heights, and host-guest interactions with metal nanoclusters. Despite some caveats, GFN1-xTB on properly constrained models is the best SQM method across all studied properties. Under proper supervision, this protocol holds promise for application in exploratory high-throughput screenings of Zr-based MOF catalysts, subject to further refinement with more accurate methods.

Design and Computational Study of Sulfonamide-Modified Cannabinoids as Selective COX-2 Inhibitors Using Semiempirical Quantum Mechanical Methods: Drug-like Properties and Binding Affinity Insights

Cyclooxygenase (COX) is one of the concerned targets in the development of anti-inflammatory therapies. Using semiempirical quantum mechanical (SQM) methods with implicit solvation, we investigated the binding free energies and selectivity of natural cannabinoids and their sulfonamide-modified derivatives with the COX and cannabinoid (CB) receptors. Validation against benchmark data sets demonstrated the accuracy of these methods in predicting binding affinities while minimizing false positives and false negatives often associated with conventional docking tools. Our findings indicate that Δ9-THC and its carboxylic acid derivative exhibit strong binding affinities for COX-2 and CB2, suggesting their potential as anti-inflammatory agents, though their significant CB1 affinity suggests psychoactive risks. In contrast, carboxylic acid derivatives such as CBCA, CBNA, CBEA, CBTA, and CBLA demonstrated selective binding to COX-2 and CB2, with low CB1 affinity, supporting their potential as promising anti-inflammatory leads with reduced psychoactive side effects. Sulfonamide-modified analogs further enhanced COX-2 binding affinities and selectivity, displaying favorable drug-like properties, including compliance with Lipinski's rules, noninhibition of cytochromes P450, and oral bioavailability. These results highlight the utility of GFN2-xTB in identifying and optimizing cannabinoid-based therapeutic candidates for anti-inflammatory applications.

The Atomic Density-Based Tight-Binding (aTB) Model: A Robust and Accurate Semiempirical Method Parametrized for H-Ra; Applied to Structures, Vibrational Frequencies, Noncovalent Interactions, and Excited States

This work introduces a semiempirical method, named aTB, based on the tight-binding model and named for its zero-order Hamiltonian that utilizes density-fitting atomic densities. This method can calculate the molecular structure, vibrational frequencies, noncovalent interactions, and excited states of large molecular systems. The parameters of aTB cover elements from Hydrogen (H) to Radium (Ra), and for ground state calculations, it supports the analysis of first- and second-order derivatives. The Hamiltonian of aTB contains a zero-order Hamiltonian, Coulomb term, an explicit second- and third-order expansion of the exchange-correlation term, and a spin-polarization term with only one additional parameter. A series of extensive tests were conducted to compare aTB with existing semiempirical methods.

Structural evolution and electronic properties of boron sulfides (B2S3)n (n = 1-6): insights from DFT calculations

The low-energy isomers of boron sulfides (B2S3)n (n = 1-6) were constructed by using B2S3 as the building block, and their structure, stability, and reactivity toward small molecules have been explored by density functional theory (DFT) calculations. It is found that the low-energy isomers of (B2S3)n clusters are of rich bonding characteristics for their structural constituents, such as [S-B-S], [B2S3], [B3S3], [S], etc. The planar or non-planar B2S2 rings, as the basic structural units, are bridged together by the S atoms to form the most stable structures of (B2S3)n clusters with n ≥ 2. These low-energy (B2S3)n clusters are predicted to be stable both structurally and electronically, and their boron centers show relatively high activity to the binding of small molecules NH3 and CO, until all boron atoms are ligated by NH3 or CO. The present results provide new insights into the structure and properties of (B2S3)n clusters, which are beneficial to the development of boron chemistry and the application of boron-based materials.

Structural Characterization of Dimeric Perfluoroalkyl Carboxylic Acid Using Experimental and Theoretical Ion Mobility Spectrometry Analyses

Per- and polyfluoroalkyl substances (PFAS) are contaminants of increasing concern, with over seven million compounds currently inventoried in the PubChem PFAS Tree. Recently, ion mobility spectrometry has been combined with liquid chromatography and high-resolution mass spectrometry (LC-IMS-HRMS) to assess PFAS. Interestingly, using negative electrospray ionization, perfluoroalkyl carboxylic acids (PFCAs) form homodimers ([2M-H]-), a phenomenon observed with trapped, traveling wave, and drift-tube IMS. In addition to the limited research on their effect on analytical performance, there is little information on the conformations these dimers can adopt. This study aimed to propose most probable conformations for PFCA dimers. Based on qualitative analysis of how collision cross section (CCS) values change with the mass-to-charge ratio (m/z) of PFCA ions, the PFCA dimers were hypothesized to likely adopt a V-shaped structure. To support this assumption, in silico geometry optimizations were performed to generate a set of conformers for each possible dimer. A CCS value was then calculated for each conformer using the trajectory method with Lennard-Jones and ion-quadrupole potentials. Among these conformers, at least one of the ten lowest-energy conformers identified for each dimer exhibited theoretical CCS values within a ±2% error margin compared to the experimental data, qualifying them as plausible structures for the dimers. Our findings revealed that the fluorinated alkyl chains in the dimers are close to each other due to a combination of C-F···O=C and C-F···F-C stabilizing interactions. These findings, together with supplementary investigations involving environmentally relevant cations, may offer valuable insights into the interactions and environmental behavior of PFAS.

Revisiting the Origins of Reactivity and Selectivity in the Pd6L4-Cage-Catalyzed Diels-Alder Reactions: A Combined Computational and Experimental Study

Supramolecular metal-organic cages (MOCs) have gained attention as versatile catalytic platforms due to their self-assembled architectures and well-defined cavities, which mimic enzyme active sites and enable spatial confinement. This confinement modulates the reaction pathways and enhances the catalytic performance. Recent studies highlight their catalytic potential in various organic transformations, but the factors governing the MOC-catalyzed reactions remain incompletely understood. This work builds on prior computational studies of Diels-Alder reactions catalyzed by palladium-based MOCs, showing that the common view of transition-state stabilization via π-π interactions is not valid. Instead, we find that π-π interactions between the substrate and the ligands destabilize the transition state. Additionally, theoretical studies of regioselectivity, validated experimentally, suggest that substrate encapsulation efficiency is key to determining reaction selectivity. These findings provide new insights into the mechanisms of MOC-catalyzed reactions.

Chemo-Selective Transformation of Anthracene Derivative within Water-Soluble Coordination Cages Having Different Cavities

Coordination cages with specific properties and functionalities are utilized as reaction vessels for the desired chemical transformation of substrates. The symmetry and inherent cavity of coordination cages can influence the host-guest interactions and the reaction outcome in their confined space. However, the impact of the cage shape on different transformations remains unclear. In this chapter, we report the chemo-selective transformation of anthracene derivative using three geometrically distinct Pd6 cages (CC2, CC3, and CC4). Photoirradiation of 9-bromoanthracene (G3) in the distorted double-square cage (CC2) yields anthracene-9,10-dione, while the known double-square cage (CC3) forms a [4 + 4] cycloaddition product. The same reaction in a known Pd6 bowl-shaped cage (CC4) resulted in the oxidized product. Through a combination of experimental and computational studies, we demonstrate that the shape and cavity size of coordination cages can significantly influence the reaction pathways of the encapsulated anthracene derivative, leading to chemo-selectivity. Furthermore, we observe that the encapsulation of 9-bromoanthracene (G3) in the cage cavities (CC2 and CC4) leads to a significant enhancement in the rate of photooxidation of G3. This work underscores the versatility of water-soluble coordination cages as reaction vessels in synthetic chemistry, offering interesting avenues for chemo-selective chemical transformation.

Acidity Prediction in Arbitrary Solvents: Machine Learning Based on Semiempirical Molecular Orbital Calculation

Due to the nonlinearity of solvent effects, careful solvent selection is essential when using acids in different applications. However, there is a lack of measurements of pKa while systematically changing molecular structures and solvents. Consequently, there was no protocol to predict the acidity in arbitrary environments. This study developed an arbitrary environment pKa prediction protocol by integrating quantum chemical calculations using a polarizable continuum model and machine learning. This protocol constructed models to predict the acidity of biologically relevant molecules in water and candidate superstrong acids in organic solvents. For both systems, the pKa can be predicted with an average absolute error of 1.1 by learning relatively small number of data. This approach can also account for the nonlinear decay of acidity with solvation in different environments (compression effect). The versatility of the protocol extends to its applicability to a wide range of compounds, including those with complex electronic state changes upon proton dissociation, supporting research in diverse fields including, but not limited to, drug discovery and engineering.

Legion: A Platform for Gaussian Wavepacket Nonadiabatic Dynamics

Nonadiabatic molecular dynamics is crucial in investigating the time evolution of excited states in molecular systems. Among the various methods for performing such dynamics, those employing frozen Gaussian wavepacket propagation, particularly the multiple spawning approach, offer a favorable balance between computational cost and reliability. It propagates on-the-fly trajectories used to build and propagate the nuclear wavepacket. Despite its potential, efficient, flexible, and easily accessible software for Gaussian wavepacket propagation is less common compared to other methods, such as surface hopping. To address this, we present Legion, a software that facilitates the development and application of classical-trajectory-guided quantum wavepacket methods. The version presented here already contains a highly flexible and fully functional ab initio multiple spawning implementation, with different strategies to improve efficiency. Legion is written in Python for data management and NumPy/Fortran for numerical operations. It is created under the umbrella of the Newton-X platform and inherits all of its electronic structure interfaces beyond other direct interfaces. It also contains new approximations that allow it to circumvent the computation of the nonadiabatic coupling, extending the electronic structure methods that can be used for multiple spawning dynamics. We test, validate, and demonstrate Legion's functionalities through multiple spawning dynamics of fulvene (CASSCF and CASPT2) and DMABN (TDDFT).

Facilitating Transition State Search with Minimal Conformational Sampling Using Reaction Graph

Elucidating transition states (TSs) is crucial for understanding chemical reactions. The reliability of traditional TS search approaches depends on input conformations that require significant effort to prepare. Previous automated methods for generating input reaction conformations typically involve extensive exploration of a large conformational space. Such exhaustive search can be complicated by the rapid growth of the conformational space, especially for reactions involving many rotatable bonds, multiple reacting molecules, and numerous bond formations and dissociations. To address this problem, we propose a new approach that generates reaction conformations for TS searches with minimal reliance on sampling. This method constructs a pseudo-TS structure based on a reaction graph containing bond formation and dissociation information and modifies it to produce reactant and product conformations. Tested on three different benchmarks, our method consistently generated suitable conformations without necessitating extensive sampling, demonstrating its potential to significantly improve the applicability of automated TS searches. This approach offers a valuable tool for a broad range of applications such as reaction mechanism analysis and network exploration.

Accurate Prediction of ωB97X-D/6-31G* Equilibrium Geometries from a Neural Net Starting from Merck Molecular Force Field (MMFF) Molecular Mechanics Geometries

Starting from Merck Molecular Force Field (MMFF) geometries, a neural-net based model has been formulated to closely reproduce ωB97X-D/6-31G* equilibrium geometries for organic molecules. The model involves training to >6 million energy and force calculations for molecules with molecular weights ranging from 200 to 600 amu, corresponding to both ωB97X-D/6-31G* and MMFF equilibrium geometries as well as small deviations away from these geometries. 422 natural products not involved in training with molecular weights ranging from 200 to 691 amu have been used to assess the neural net model against changes in bond lengths, bond angles, and dihedral angles, as well as against changes in proton and 13C chemical shifts resulting from using equilibrium geometries from the neural-net in lieu of geometries from ωB97X-D/6-31G*. The neural net reduces calculation times by two or more orders of magnitude.

Redox-Activated Proton Transfer through a Redundant Network in the Qo Site of Cytochrome bc1

Proton translocation catalyzed by cytochrome bc1 (respiratory complex III) during coenzyme-Q redox cycling is a critical bioenergetic process, yet its detailed molecular mechanism remains incompletely understood. In this study, the energetics of the long-range proton transfers through multiple proton-conducting wires in the Qo site of the bc1 complex was investigated computationally using hybrid QM/MM simulations and a specialized reaction coordinate. Key reactive groups and proton transfer mechanisms were characterized, confirming the propionate-A group of heme bL as a plausible proton acceptor. Upon coenzyme-Q oxidation, a Grotthuss hopping mechanism is activated, facilitating proton transfer along three distinct pathways with comparable barriers and stability. These pathways operate redundantly, forming a robust proton-conducting network, and account for the unusual experimental behavior observed in single-point mutations. Energetic analyses exclude charged closed-shell species as likely intermediates and propose a reaction sequence for coenzyme-Q oxidation proceeding as QH2 → QH• → Q0, either via coupled proton-electron transfers or stepwise mechanisms involving open-shell intermediates. These findings elucidate mechanistic details of the Q-cycle and improve our understanding of the catalytic reactions supporting redox-activated proton transfer in respiratory enzymes.

Chiral Pd2L4 Capsules from Readily Accessible Tröger's Base Ligands Inducing Circular Dichroism on Fullerenes C60 and C70

The induction of chirality on pristine fullerenes through non-covalent embedding in an asymmetric nano-confinement has only been rarely reported. Bringing molecules with such a unique electronic structure and broad application range into a chiral environment is particularly appealing for the development of chiroptical materials, enantioselective photoredox catalysts and systems showing chirality-induced spin selectivity (CISS). In this study, we report the formation of a chiral, configurationally stable Pd2L4 capsule assembled from a C2-symmetric, 'ribbon-shaped' ligand with a Tröger's base naphthalimide (TbNaps) backbone, easily synthesized in three steps from commercially available compounds. Embedding chirality directly into the ligand backbone ensures a relatively lightweight receptor design whose aromatic panels create a strongly shielded inner cavity of about 700 Å3 volume. Fullerenes C60 and C70, as well as a pair of corannulenes, can be bound in acetonitrile (where unsubstituted fullerenes are insoluble) and X-ray structures of host-guest complexes were obtained. Tight interactions between the chiral host and the fullerene guests leads to the induction of a circular dichroism (CD) on the characteristic absorption bands of the forbidden π-π* transitions of the fullerenes, backed up by sTDA TD-DFT calculations and detailed investigation of the electronic excited states.

Organic Chimeras Based on Selenosugars, Steroids, and Fullerenes as Potential Inhibitors of the β-amyloid Peptide Aggregation

The aggregation of β-amyloid peptide (Aβ) is associated with neurodegenerative diseases such as Alzheimer's disease (AD). Several therapies aimed at reducing the aggregation of this peptide have emerged as potential strategies for the treatment of AD. This paper describes the design and preparation of new hybrid molecules based on steroids, selenosugars, and [60]fullerene as potential inhibitors of Aβ oligomerization. These moieties were selected based on their antioxidant properties and possible areas of interaction with the Aβ. Cyclopropanations between C60 and malonates bearing different steroid and selenosugar moieties using the Bingel-Hirsch protocol have enabled the synthesis of functionalized molecular hybrids. The obtained derivatives were characterized by physical and spectroscopic techniques. Theoretical calculations for all the selenium compounds were performed using the density functional theory DFT/B3LYP-D3(BJ)/6-311G(2d,p) predicting the most stable conformations of the synthesized derivatives. Relevant geometrical parameters were investigated to relate the stereochemical behavior and the spectroscopic data obtained. The affinity of the compounds for Aβ-peptide was estimated by molecular docking simulation, which predicted an increase in affinity and interactions for Aβ for the hybrids containing the C60 core. In addition, parameters such as lipophilicity, polar surface area, and dipole moment were calculated to predict their potential interaction with membrane cells.

Zero-point energies from bond orders and populations relationships

We report two analytical quantum mechanics (QM) models for approximating appropriately scaled harmonic zero-point energies (ZPEs) without Hessian calculations. Following our earlier bond energies from bond orders and populations model that takes a similar form as an extended Hückel model but uses well-conditioned orbital populations, this work demonstrates a proof of concept for approximating ZPEs, an important component in thermochemistry calculations, while eschewing unfavorably scaling algorithms involving Hessian matrices. The ZPE-BOP1 model uses Mulliken orbital populations from hybrid Kohn-Sham density functional theory calculations within an extended Hückel-type model that defines vibrational bond energy terms using two atom-pairwise parameters that are fit to reproduce ZPEs from B3LYP calculations. The more accurate ZPE-BOP2 model uses Mulliken orbital populations from Hartree-Fock calculations within a different extended Hückel-type model that includes a short-range anharmonic energy term and a coupled three-body oscillator energy term with seven atom-pairwise parameters. Both models predict ZPEs in molecules involving first row elements, but ZPE-BOP2 outperforms ZPE-BOP1 in strained and long-chain molecules and provides ZPEs more competitive with those from semi-empirical QM methods (e.g., AM1, PM6, PM7, and XTB-2) that compute ZPEs with Hessian calculations. This work shows progress and an outlook toward computational models that use well-conditioned orbital populations to efficiently predict useful physicochemical properties. It also shows opportunities for approximate QM models that would shift traditional computational bottlenecks away from costly algorithms such as Hessian calculations to others that focus on reliable orbital populations.

Realizing Ultrafine Color Tuning of Organic Electronics Materials by Electronic State Informatics

Organic electrochromic (EC) materials enabling energy-efficient smart windows, human-friendly displays, and highly transparent medical lenses are key for a future society. However, developing organic EC molecules that change from transparent to pure magenta, one of the three primary colors, had been impossible through simple quantum chemical calculations and experiments alone, as it requires sophisticated spectral optimization involving multiple electronic transitions in neutral and radical states. In this study, we successfully identified structures exhibiting colorless-to-pure-magenta switching from 1.2 million triphenylamine derivatives using electronic state descriptors easily obtained from semiempirical molecular orbital calculations with qualitative accuracy. Subsequent organic synthesis and spectroelectrochemical experiments proved that the identified candidate molecules achieve the desired functionality.

Competing pathways to aromaticity governed by amine dehydrogenation and metal-organic complexation in on-surface synthesis

We investigated the reactivity of a gem-dichlorovinyl-carbazole precursor in the on-surface synthesis approach. Our findings reveal that, on the Au(111) surface, the thermally-induced dehalogenation reaction led to the formation of cumulene dimers. Contrastingly, the more reactive Cu(111) surface promoted the formation of a polyheterocyclic compound exhibiting extended aromaticity. The latter was found to be related to the dehydrogenation of the amine groups, which did not occur on Au(111), thus promoting the different reactivity observed. At higher annealing temperature, selective C-H activation led to the formation of well-defined organometallic chains. In addition, we found that the amine complexation with metal adatom on Cu(111) was an inhibiting factor for the dimerization reaction, a challenge that could be overcome through proper control of the deposition conditions.

DNA Triplet Energies by Free Energy Perturbation Theory

Determining the energetics of triplet electronic states of nucleobases in the biological macromolecular environment of nucleic acids is essential for an accurate description of the mechanism of photosensitization and the design of drugs for cancer treatment. In this work, we aim at developing a methodological approach to obtain accurate free energies of triplets in DNA beyond the state of the art, able to reproduce the decrease of triplet energies measured experimentally for T in DNA (270 kJ/mol) vs in the isolated nucleotide in aqueous solution (310 kJ/mol). For such purposes, we adapt the free energy perturbation method to compute the free energy related to the transformation of a pure singlet state into a pure triplet state via "alchemical" intermediates with mixed singlet-triplet nature. By this means, standard deviation errors are only a few kJ/mol, contrary to the large errors of tenths of kJ/mol obtained by averaging the singlet and triplet energies derived from molecular dynamics simulations. The reduced statistical errors obtained by the free energy perturbation approach allow us to rationalize with confidence the triplet stabilization observed experimentally when comparing the thymine nucleotide and thymine in DNA. Spin polarization rather than excimer interactions between the π-stacked nucleobases originates the lower values of the triplet energies in DNA. The developed approach implemented in QM3 shall be useful for determining free energies of triplets and other states like ionic or charge separation states in any other macromolecular system with impact in biomedicine and materials science.

Antibiofilm and cytotoxic metabolites from the entomopathogenic fungus Samsoniella aurantia

During the course of our studies on the secondary metabolism of rare, hitherto untapped Thai insect-associated fungi, the ethyl acetate (EtOAc) extract derived from solid-state cultivation of Samsoniella aurantia on rice afforded one previously undescribed tetramic acid derivative, farinosone D (1), along with the known 2-pyridones, farinosones A (2) and B (3), and the known cyclodepsipeptides beauvericins A-C (4-6). All isolated compounds were assessed for their antimicrobial and cytotoxic activities while farinosones D (1) and A (2) were selected for biofilm inhibitory activity assay. Farinosone B (3) and beauvericins A-C (4-6) showed significant cytotoxic activities with IC50 values in the low micromolar to nanomolar range against several mammalian cell lines. On the other hand, farinosone A (2), which lacked potent cytotoxic effects, revealed potent antibiofilm activity, inhibiting approximately 70% of Staphylococcus aureus biofilms at concentrations as low as 3.9 µg/mL.

Gold Nanorod Surface-Enhanced Raman Spectroscopy Substrate for l-DOPA Detection: Experimental and Theoretical Approaches

Experimental efforts aimed at detecting levodopa (l-DOPA) using surface-enhanced Raman scattering (SERS) face a persistent challenge in obtaining a SERS signal with negatively charged nanoparticles. This challenge stems from the repulsion between deprotonated l-DOPA in aqueous solution and the charged surface of the nanoparticles, revealing dependencies on time and concentration to achieve the SERS signal. This study explores the adsorption mechanism of l-DOPA on the surface of gold nanorods (AuNRs) covered with a cetrimonium bromide (CTAB) bilayer as a colloidal solution, subsequently dried onto a solid substrate such as glass, silicon, and Au substrate. Experimental findings are supported by density functional theory theoretical calculations. The comparison between experimental and theoretical results highlights that the SERS profile can be attributed to the adsorption of l-DOPA via the catechol ring, leading to the formation of anionic and dianionic species.

Novel tryptophyllin peptides from Physalaemus centralis inhibit oxidative stress-induced endothelial dysfunction in rat aorta preparation

Amphibian skin is a rich source of molecules with biotechnological potential, including the tryptophyllin family of peptides. Here, we report the identification and characterization of two tryptophyllin peptides, FPPEWISR and FPWLLS-NH2, from the skin of the Central Dwarf Frog, Physalaemus centralis. These peptides were identified through cDNA cloning and sequence comparison. FPWLLS-NH2 shares its primary structure with a previously identified peptide from the skin of Pelophylax perezi, named PpT-2. Another peptide, FPPEWISR, is novel and was named PcT-1. After solid-phase peptide synthesis, both peptides exhibited significant antioxidant activity, with PcT-1 and PpT-2 demonstrating ABTS radical scavenging capacities of 0.305 and 0.269 mg Trolox equivalents/mg peptide, respectively, and ORAC values of 0.319 and 0.248 mg Trolox equivalents/mg peptide. Additionally, PcT-1 and PpT-2 inhibited AAPH-induced hemolysis in human red blood cells, achieving a protection level comparable to Trolox at 0.2 mg/mL. In rat aorta preparations, both peptides partially restored acetylcholine-induced vasorelaxation following pyrogallol-induced oxidative stress, with a greater protective effect of PpT-2. Hemolytic activity assay indicated no cytotoxicity in human red blood cells, and tests on Galleria mellonella larvae confirmed their low toxicity in vivo. These findings highlight the biotechnological potential of PcT-1 and PpT-2 as antioxidant agents, paving the way for new therapeutic applications in combating oxidative stress-related diseases.

Polyphenolic Profile and In vitro, In Silico Study of Stem Extracts from J. Maritimus (Juncaceae) Harvested from Eastern Algeria as Potential Anti-Inflammatory and Antioxidant Agents

Juncus maritimusan extremophilous microorganism speciesknown for its medicinal properties was collected in the Biskra region with the aim of its valorization. The volume size of polyphenols, flavonoids and condensed tannins was performed using the Folin-Ciocalteu method, aluminum trichloride and vanillin respectively. The volume of Polyphenols was 127.73±0.20 μg EAG/mg ES, the volume offlavonoids was 16.42±0.42 μg EQ/mg ES, and the volume ofcondensed tannins was 10.10±0.35 μg EC/mg ES. The results of the antioxidant activity tests using the DPPH and ABTS methods reveal that the ethyl acetate extract had the highest activity in both tests. The results of the in vitro anti-inflammatory activity, using the BSA protein denaturation assay, showed that the percentage of denaturation inhibition wasproportional to the concentration of the extract. At a concentration of 5 mg/mL, the inhibition percentage of the extract were 82.03 % and 80.23 %, respectively,which wereclose tothose of theanti-inflammatory drug Diclofenac.Furthermore, molecular docking simulations indicated that Berberine has high binding affinity to the targets COX-2 and PLA-2 In fact,bioisosteric replacement is being usedto discover new analogs of Berberine. Finally, ADME-Tox predictions demonstrated that this compound and its analogs were not hepatotoxic. This result may lead to the selection ofBerberine and its analogs as active compounds with anti-inflammatory and antioxidant effects.

Preparation and Biodistribution Assessment of 177Lu-curcumin as a Possible Therapeutic Agent

Purpose

Curcumin as a potent anti-inflammatory and cancer-prevention molecule was labeled with n.c.a 177Lu. The combination of 177Lu as a theranostic agent and curcumin as an anti-cancer can be considered for nuclear medicine.

Methods

First, n.c.a 177Lu (specific activity = 48 Ci/mg) was prepared using the extraction chromatography method. Then, semi-empirical quantum chemical calculations were applied to get a deeper insight into the complexation reaction between Lu+ 3 and curcumin ligand. UV-Vis spectrophotometry was used for the determination of the metal-to-curcumin ratio. Subsequently, a mixture of (111-333 MBq) n.c.a 177Lu, 50 µL curcumin solution in ethanol, and 450 µL acetate buffer at pH = 5 was incubated for 1 h at 95 ºC. The Lu-curcumin complex chemical structure was characterized using IR spectroscopy. Finally, the prepared complex was analyzed by different quality control tests.

Results

Complexometry using UV-Vis studies showed a 1:2 ratio for Lutetium: curcumin complex which is in agreement with theoretical calculations. The IR-spectra analysis also confirmed the complex formation. The radiochemical purity of n.c.a 177Lu -curcumin was more than 95% as determined by radio-TLC. The stability of up to 48 h was observed for the prepared complex in serum. The partition coefficient was calculated for the compound (log P = -0.31). Evaluating biodistribution in tumoral mice exhibited high tumor uptake (%ID/gtissue = 2.03).

Conclusion

The promising results showed that n.c.a 177Lu-curcumin can be considered as a possible radiopharmaceutical agent for therapeutic applications.

Single-Molecule Electrical Conductance in Z-form DNA:RNA

Nucleic acids have emerged as new materials with promising applications in nanotechnology, molecular electronics, and biosensing, but their electronic properties, especially at the single-molecule level, are largely underexplored. The Z-form is an exotic left-handed helical oligonucleotide conformation that may be involved in critical biological processes such as the regulation of gene expression and epigenetic processes. In this work, the electrical conductance of individual Guanine Cytosine (GC)-rich DNA:RNA molecules is measured in physiological buffer and 2,2,2-Trifluoroethanol (TFE) solvent, corresponding to the natural (right-handed helix) A-form typical in DNA:RNA hybrids and the (left-handed) Z-form conformations, respectively. Single-molecule conductance measurements are performed using the Scanning Tunneling Microscopy (STM)-assisted break-junction method in the so-called "blinking" approach, recording the spontaneous formation of single-biomolecule junctions and performing statistical analysis of the signals. Circular Dichroism (CD) experiments and ab initio calculations are also done to rationalize the measured molecular conductivity with a simple structural and electronic model. These results show that the electrical conductivity of the Z-form is one order of magnitude lower than that of the more compact A-form. The longer molecular length and higher energy for the Highest Occupied Molecular Orbital (HOMO) of the Z-form account for the differences in single-molecule conductance observed experimentally.

Reactivity of amino acids and short peptide sequences: identifying bioactive compounds via DFT calculations

Bioactive peptides are short amino acid sequences that play important roles in various physiological processes, including antioxidant and protective effects. These compounds can be obtained through protein hydrolysis and have a wide range of potential applications in a variety of areas. However, despite the potential of these compounds, more in-depth knowledge is still necessary to better understand details regarding their chemical reactivity and electronic properties. In this study, we used molecular modeling techniques to investigate the electronic structure of isolated amino acids (AA) and short peptide sequences. Details on the relative alignments between the frontier electronic levels, local chemical reactivity and donor-acceptor properties of the 20 primary amino acids and some di- and tripeptides were evaluated in the framework of the density functional theory (DFT). Our results suggest that the electronic properties of isolated amino acids can be used to interpret the reactivity of short sequences. We found that aromatic and charged amino acids, as well as Methionine, play a key role in determining the local reactivity of peptides, in agreement with experimental data. Our analyses also allowed us to identify the influence of the relative position of AA and terminations on the local reactivity of the sequences, which can guide experimental studies and help to propose/evaluate possible mechanisms of action. In summary, our data indicate that the position of active sites of polypeptides can be predicted from short sequences, providing a promising strategy for the synthesis and bioprospection of new optimized compounds.

Convergent Protocols for Computing Protein-Ligand Interaction Energies Using Fragment-Based Quantum Chemistry

Fragment-based quantum chemistry methods offer a means to sidestep the steep nonlinear scaling of electronic structure calculations so that large molecular systems can be investigated using high-level methods. Here, we use fragmentation to compute protein-ligand interaction energies in systems with several thousand atoms, using a new software platform for managing fragment-based calculations that implements a screened many-body expansion. Convergence tests using a minimal-basis semiempirical method (HF-3c) indicate that two-body calculations, with single-residue fragments and simple hydrogen caps, are sufficient to reproduce interaction energies obtained using conventional supramolecular electronic structure calculations, to within 1 kcal/mol at about 1% of the computational cost. We also demonstrate that the HF-3c results are illustrative of trends obtained with density functional theory in basis sets up to augmented quadruple-ζ quality. Strategic deployment of fragmentation facilitates the use of converged biomolecular model systems alongside high-quality electronic structure methods and basis sets, bringing ab initio quantum chemistry to systems of hitherto unimaginable size. This will be useful for generation of high-quality training data for machine learning applications.

PM6-ML: The Synergy of Semiempirical Quantum Chemistry and Machine Learning Transformed into a Practical Computational Method

Machine learning (ML) methods offer a promising route to the construction of universal molecular potentials with high accuracy and low computational cost. It is becoming evident that integrating physical principles into these models, or utilizing them in a Δ-ML scheme, significantly enhances their robustness and transferability. This paper introduces PM6-ML, a Δ-ML method that synergizes the semiempirical quantum-mechanical (SQM) method PM6 with a state-of-the-art ML potential applied as a universal correction. The method demonstrates superior performance over standalone SQM and ML approaches and covers a broader chemical space than its predecessors. It is scalable to systems with thousands of atoms, which makes it applicable to large biomolecular systems. Extensive benchmarking confirms PM6-ML's accuracy and robustness. Its practical application is facilitated by a direct interface to MOPAC. The code and parameters are available at https://github.com/Honza-R/mopac-ml.

End-Point Affinity Estimation of Galectin Ligands by Classical and Semiempirical Quantum Mechanical Potentials

The use of quantum mechanical potentials in protein-ligand affinity prediction is becoming increasingly feasible with growing computational power. To move forward, validation of such potentials on real-world challenges is necessary. To this end, we have collated an extensive set of over a thousand galectin inhibitors with known affinities and docked them into galectin-3. The docked poses were then used to systematically evaluate several modern force fields and semiempirical quantum mechanical (SQM) methods up to the tight-binding level under consistent computational workflow. Implicit solvation models available with the tested methods were used to simulate solvation effects. Overall, the best methods in this study achieved a Pearson correlation of 0.7-0.8 between the computed and experimental affinities. There were differences between the tested methods in their ability to rank ligands across the entire ligand set as well as within subsets of structurally similar ligands. A major discrepancy was observed for a subset of ligands that bind to the protein via a halogen bond, which was clearly challenging for all the tested methods. The inclusion of an entropic term calculated by the rigid-rotor-harmonic-oscillator approximation at SQM level slightly worsened correlation with experiment but brought the calculated affinities closer to experimental values. We also found that the success of the prediction strongly depended on the solvation model. Furthermore, we provide an in-depth analysis of the individual energy terms and their effect on the overall prediction accuracy.

Beyond chemical structures: lessons and guiding principles for the next generation of molecular databases

Databases of molecules and materials are indispensable for advancing chemical research, especially when enriched with electronic structure information from quantum chemistry methods like density functional theory. In this perspective, we review and analyze the current landscape of materials and molecular databases containing quantum chemical data. Our analysis reveals that the materials community has significantly benefited from data platforms such as the Materials Project, which seamlessly integrate chemical structures, electronic structure data, and open-source software. Conversely, quantum chemical data for molecular systems remains largely fragmented across individual datasets, lacking the comprehensive framework of a unified database. We distilled insights from these existing data resources into seven guiding principles termed QUANTUM, which build upon the foundational FAIR principles of data sharing (Findable, Accessible, Interoperable, and Reusable). These principles are aimed at advancing the development of molecular databases into robust, integrated data platforms. We conclude with an outlook on both short- and long-term objectives, guided by these QUANTUM principles, to foster future advancements in molecular quantum databases and enhance their utility for the research community.

Calculated and structural analyses of self-assembly formed by [7]thiaheterohelicene-2,13-carboxaldehyde molecules on Au(111)

Recently, the electronic and structural properties of large self-assembled domains of [7]thiaheterohelicene-2,13-carboxaldehyde helicene ([7]TH-dial) molecules on Au(111), Cu(001), and NiAl(110) metal surfaces have been characterized by scanning tunneling microscopy (STM). Several distinct areas of the self-assembled structures can be observed. To describe and explore the morphology of and the interactions in these distinct self-assembled nanostructures, we combine the results obtained through calculations in a semi-empirical framework and calculated STM images. It is revealed that these supramolecular nanostructures, on metallic substrates, originate from the two orientations P and M of the [7]TH-dial molecules linked in different orientations (head-to-tail, sideways, head-on, and tail-on) through van der Waals interactions. The results presented here provide valuable insights for understanding the intermolecular and substrate-molecule interactions within the self-assembled nanostructures of [7]TH-dial molecules on metallic surfaces.

Geometry-Corrected Quadratic Optimization Algorithm for NDDO-Descendant Semiempirical Models

The long-held assumption that the optimization of parameters for NDDO-descendant semiempirical methods may be performed without precise geometry optimization is assessed in detail; the relevant equations for the analytical evaluation of the geometry-corrected derivatives of molecular properties that account for changes in the optimum geometry are then presented. The first and second derivatives calculated from our implementation of MNDO are used for a limited reparameterization of 1,113 CHNO molecules taken from the PM7 training set, demonstrating an improvement over the PARAM program used in the optimization of parameters for the PMx methods.

Molecular dynamics-guided reaction discovery reveals endoperoxide-to-alkoxy radical isomerization as key branching point in α-pinene ozonolysis

Secondary organic aerosols (SOAs) significantly impact Earth's climate and human health. Although the oxidation of volatile organic compounds (VOCs) has been recognized as the major contributor to the atmospheric SOA budget, the mechanisms by which this process produces SOA-forming highly oxygenated organic molecules (HOMs) remain unclear. A major challenge is navigating the complex chemical landscape of these transformations, which traditional hypothesis-driven methods fail to thoroughly investigate. Here, we explore the oxidation of α-pinene, a critical atmospheric biogenic VOC, using a novel reaction discovery approach based on molecular dynamics and state-of-the-art enhanced sampling techniques. Our approach successfully identifies all established reaction pathways of α-pinene ozonolysis, as well as discovers multiple novel species and pathways without relying on a priori chemical knowledge. In particular, we unveil a key branching point that leads to the rapid formation of alkoxy radicals, whose high and diverse reactivity help to explain hitherto unexplained oxidation pathways suggested by mass spectral peaks observed in α-pinene ozonolysis experiments. This branching point is likely prevalent across a variety of atmospheric VOCs and could be crucial in establishing the missing link to SOA-forming HOMs.

N-Tosyl Hydrazone Benzopyran, a New Ligand of PPARα Obtained from Mapping the Conformational Space of Its Active Site

We report here a new ligand for the peroxisome-proliferator-activated receptor type α (PPARα), an N-tosyl hydrazone benzopyran that was designed throughout the mapping of the polar zone of the binding site of PPARα; such a compound displays a strong activity on this receptor that is comparable to that of the reference compound WY-14643. For the design of the N-tosyl hydrazone benzopyran, we have carried out an exhaustive conformational study of WY-14643 and a previously reported hydrazine benzopyran derivative using conformational potential energy surfaces (PES). This study allowed us to map in a systematic way the entire binding site of the PPARα. PESs allowed us to evaluate all of the critical points on the surface (minimum, transition states, and maxima) and determine the different conformational interconversion paths. Once the geometries of the different complexes were determined, we carried out the study of the different molecular interactions that stabilize these complexes through the use of QTAIM calculations. We report here for the first time the molecular behavior of WY-14643 and two compounds synthesized in our lab interacting in the active site of the PPARα providing all of the details about the different interactions that stabilize the formation of these complexes. On the basis of such information, we were able to design and synthesize a new N-tosyl hydrazone benzopyran possessing a strong agonist effect on PPARα. The information provided by this study is very useful to get a better understanding of the behavior with this type of ligand on the PPARα, giving also interesting information as a guide for the design of new ligands for this receptor.

In silico Evaluation of H1-Antihistamine as Potential Inhibitors of SARS-CoV-2 RNA-dependent RNA Polymerase: Repurposing Study of COVID-19 Therapy

Introduction

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), from the family Coronaviridae, is the seventh known coronavirus to infect humans and cause acute respiratory syndrome. Although vaccination efforts have been conducted against this virus, which emerged in Wuhan, China, in December 2019 and has spread rapidly around the world, the lack of an Food and Drug Administration-approved antiviral agent has made drug repurposing an important approach for emergency response during the COVID-19 pandemic. The aim of this study was to investigate the potential of H1-antihistamines as antiviral agents against SARS-CoV-2 RNA-dependent RNA polymerase enzyme.

Materials and Methods

Using molecular docking techniques, we explored the interactions between H1-antihistamines and RNA-dependent RNA polymerase (RdRp), a key enzyme involved in viral replication. The three-dimensional structure of 37 H1-antihistamine molecules was drawn and their energies were minimized using Spartan 0.4. Subsequently, we conducted a docking study with Autodock Vina to assess the binding affinity of these molecules to the target site. The docking scores and conformations were then visualized using Discovery Studio.

Results

The results examined showed that the docking scores of the H1-antihistamines were between 5.0 and 8.3 kcal/mol. These findings suggested that among all the analyzed drugs, bilastine, fexofenadine, montelukast, zafirlukast, mizolastine, and rupatadine might bind with the best binding energy (< -7.0 kcal/mol) and inhibit RdRp, potentially halting the replication of the virus.

Conclusion

This study highlights the potential of H1-antihistamines in combating COVID-19 and underscores the value of computational approaches in rapid drug discovery and repurposing efforts. Finally, experimental studies are required to measure the potency of H1-antihistamines before their clinical use against COVID-19 as RdRp inhibitors.

Revisiting Butafulvene Formation by Thermal Dimerization of Fluorene-Based Dialkynes - Effects of Aromatic Substituents

Butafulvenes, together with pentafulvenes and [3]radialenes, form a series of constitutional benzene isomers in which aromaticity changes significantly and can be strongly substituent dependent. Butafulvene, as a member of this series, is frequently proposed to be antiaromatic. Based on butafulvenes Hopf, Zimmerman and coworkers first time described, derivatives thereof were synthesized and the effects of substituents on both the stability of the intermediate isobenzenes and on their optoelectronic and (anti)aromatic properties are discussed.

Sulfobetaine ionic liquid crystals based on strong acids: phase behavior and electrochemistry

A group of new zwitterion based ionic liquid crystals (ILCs) have been synthesized. Depending on the counter anion (mesylate or hydrogen sulfate) the phase behavior of the resulting ILCs is quite different. Mesylate based ILCs show complex phase behavior with multiple phases depending on the alkyl chain length. In contrast, hydrogen sulfate based systems always exhibit Colr phases irrespective of the alkyl chain length. The latter show much larger ILC mesophase windows and are thermally stable up to ca. 200 °C. All ILCs show reasonable ionic conductivities of up to 10-4 S cm-1 at elevated temperatures, making these ILCs candidates for intermediate temperature ionic conductors.

Aromatic polyketides isolated from the marine-derived fungus Didymella aeria and their neuroprotective activity

Two novel aromatic polyketides, penicanesins J and K (1, 2), were isolated from the marine-derived fungus Didymella aeria, along with the known compound integrastatin B (3). The structures of the new compounds were determined by NMR spectroscopy and synthetic methods. The isolated compounds were tested for monoamine oxidase (MAO) B inhibition, anti-amyloid beta (Aβ) aggregation, and protective activity against H2O2-induced cell death in human neuroblastoma SH-SY5Y cells. Integrastatin B (3) showed potential activity for inhibition of Aβ aggregation and protection against H2O2-induced cell death.

Fuentes L, Pimenta A, Nagem RAP, Chávez-Olórtegui C, Schneider FS, Molina F, Sanchez EF, Suárez D, Ferreira RS. Assessing the Interactions between Snake Venom Metalloproteinases and Hydroxamate Inhibitors Using Kinetic and ITC Assays, Molecular Dynamics Simulations and MM/PBSA-Based Scoring Functions

Bothrops species are the main cause of snake bites in rural communities of tropical developing countries of Central and South America. Envenomation by Bothrops snakes is characterized by prominent local inflammation, hemorrhage and necrosis as well as systemic hemostatic disturbances. These pathological effects are mainly caused by the major toxins of the viperidae venoms, the snake venom metalloproteinases (SVMPs). Despite the antivenom therapy efficiency to block the main toxic effects on bite victims, this treatment shows limited efficacy to prevent tissue necrosis. Thus, drug-like inhibitors of these toxins have the potential to aid serum therapy of accidents inflicted by viper snakes. Broad-spectrum metalloprotease inhibitors bearing a hydroxamate zinc-binding group are potential candidates to improve snake bites therapy and could also be used to study toxin-ligand interactions. Therefore, in this work, we used both docking calculations and molecular dynamics simulations to assess the interactions between six hydroxamate inhibitors and two P-I SVMPs selected as models: Atroxlysin-I (hemorrhagic) from Bothrops atrox, and Leucurolysin-a (nonhemorrhagic) from Bothrops leucurus. We also employed a large variety of end-point free energy methods in combination with entropic terms to produce scoring functions of the relative affinities of the inhibitors for the toxins. Then we identified the scoring functions that best correlated with experimental data obtained from kinetic activity assays. In addition, to the characterization of these six molecules as inhibitors of the toxins, this study sheds light on the main enzyme-inhibitor interactions, explaining the broad-spectrum behavior of the inhibitors, and identifies the energetic and entropic terms that improve the performance of the scoring functions.