MolGC: molecular geometry comparator algorithm for bond length mean absolute error computation on molecules.

Density Functional Theory (DFT) is extensively used in theoretical and computational chemistry to study molecular and crystal properties across diverse fields, including quantum chemistry, materials physics, catalysis, biochemistry, and surface science. Despite advances in DFT hardware and software for optimized geometries, achieving consensus in molecular structure comparisons with experimental counterparts remains a challenge. This difficulty is exacerbated by the lack of automated bond length comparison tools, resulting in labor-intensive and error-prone manual processes. To address these challenges, we propose MolGC, a Molecular Geometry Comparator algorithm that automates the comparison of optimized geometries from different theoretical levels. MolGC calculates the mean absolute error (MAE) of bond lengths by integrating data from various DFT software. It provides interactive and customizable visualization of geometries, enabling users to explore different views for enhanced analysis. In addition, it saves MAE computations for further analysis and offers a comprehensive statistical summary of the results. MolGC effectively addresses complex graph labeling challenges, ensuring accurate identification and categorization of bonds in diverse chemical structures. It achieves a 98.91% average rate in correct bond label assignments on an antibiotics dataset, showcasing its effectiveness for comparing molecular bond lengths across geometries of varying complexity and size. The executable file and software resources for running MolGC can be downloaded from https://github.com/AbimaelGP/MolGC/tree/main .

Tale of Two Polymorphs: Investigating the Structural Differences and Dynamic Relationship between Nirmatrelvir Solid Forms (Paxlovid).

Two anhydrous polymorphs of the novel antiviral medicine nirmatrelvir were discovered during the development of Paxlovid, Pfizer's oral Covid-19 treatment. A comprehensive experimental and computational approach was necessary to distinguish the two closely related polymorphs, herein identified as Forms 1 and 4. This approach paired experimental methods, including powder X-ray diffraction and single-crystal X-ray diffraction, solid-state experimental methods, thermal analysis, solid-state nuclear magnetic resonance and Raman spectroscopy with computational investigations comprising crystal structure prediction, Gibbs free energy calculations, and molecular dynamics simulations of the polymorphic transition. Forms 1 and 4 were ultimately determined to be enantiotropically related polymorphs with Form 1 being the stable form above the transition temperature of ∼17 °C and designated as the nominated form for drug development. The work described in this paper shows the importance of using highly specialized orthogonal approaches to elucidate the subtle differences in structure and properties of similar solid-state forms. This synergistic approach allowed for unprecedented speed in bringing Paxlovid to patients in record time amidst the pandemic.

Investigation of magnetic ionic liquids for selective and rapid extraction of gallic acid from complex samples using experimental, statistical modeling and density functional theory studies.

Given the high antioxidant capacity of gallic acid (GA), there is a great deal of interest in the development of rapid, selective, simple, and easily accessible analytical methods for its determination from complex samples. Consequently, the present study aimed to develop an ultrasonic assisted magnetic ionic liquid-based dispersive liquid microextraction (UA-MIL-DLLME) method for the extraction of GA from various samples prior to its spectrophotometric detection. The method's key variables were optimized through statistical analysis. Four magnetic liquids (MILs) were prepared and tested to extract the GA-Se complex formed in aqueous solution. Both experimental studies and theoretical calculations demonstrated that the most suitable MIL for the phase separation of the relevant complex is [P6,6,6,14][Mn(hfacac)3]. The developed UA-MIL-DLLME method exhibited a wide linear range (5-400 ng mL-1), a remarkable enhancement factor (133), and a low limit of detection (1.6 ng mL-1). Additionally, high extraction recovery (97 ± 1%) with a low relative standard deviation (1.9%) was achieved. The extraction time for the UA-MIL-DLLME method was 8 min. The precision of the method was evaluated through repeatability and reproducibility studies. Finally, the UA-MIL-DLLME method was successfully applied to the extraction of the GA from complex samples using a reference method.

The 3D pharmacophore modeling to explore new antischistosomal agents among US FDA approved drugs.

Aim: To identify potential antischistosomal agents through 3D pharmacophore-based virtual screening of US FDA approved drugs. Materials & methods: A comprehensive virtual screening was conducted on a dataset of 10,000 FDA approved drugs, employing praziquantel as a template. Promising candidates were selected and assessed for their impact on Schistosoma mansoni viability in vitro and in vivo using S. mansoni infected mice. Results & conclusion: Among the selected drugs, betamethasone and doxazosin demonstrated in vitro efficacy, with effective concentration 50% (EC50) values ranging from 35 to 60 µM. In vivo studies revealed significant (>50%) reductions in worm burden for both drugs. These findings suggest that betamethasone and doxazosin hold promise for repurposing in treating schistosomiasis. Additionally, the study showcases a useful approach for identifying new antischistosomal drugs.

Discovering new treatments for #schistosomiasis is crucial[Formula: see text]. Our study used virtual screening to identify potential antischistosomal drugs from US FDA approved compounds [Formula: see text]. Promising results in vitro and in vivo. [Formula: see text] #drugdiscovery #tropicaldiseases.

Computational Chemistry Strategies to Investigate the Antioxidant Activity of Flavonoids-An Overview.

Despite several decades of research, the beneficial effect of flavonoids on health is still enigmatic. Here, we focus on the antioxidant effect of flavonoids, which is elementary to their biological activity. A relatively new strategy for obtaining a more accurate understanding of this effect is to leverage computational chemistry. This review systematically presents various computational chemistry indicators employed over the past five years to investigate the antioxidant activity of flavonoids. We categorize these strategies into five aspects: electronic structure analysis, thermodynamic analysis, kinetic analysis, interaction analysis, and bioavailability analysis. The principles, characteristics, and limitations of these methods are discussed, along with current trends.

Signal execution modes emerge in biochemical reaction networks calibrated to experimental data.

Mathematical models of biomolecular networks are commonly used to study cellular processes; however, their usefulness to explain and predict dynamic behaviors is often questioned due to the unclear relationship between parameter uncertainty and network dynamics. In this work, we introduce PyDyNo (Python dynamic analysis of biochemical networks), a non-equilibrium reaction-flux based analysis to identify dominant reaction paths within a biochemical reaction network calibrated to experimental data. We first show, in a simplified apoptosis execution model, that despite the thousands of parameter vectors with equally good fits to experimental data, our framework identifies the dynamic differences between these parameter sets and outputs three dominant execution modes, which exhibit varying sensitivity to perturbations. We then apply our methodology to JAK2/STAT5 network in colony-forming unit-erythroid (CFU-E) cells and provide previously unrecognized mechanistic explanation for the survival responses of CFU-E cell population that would have been impossible to deduce with traditional protein-concentration based analyses.

Theoretical Study of Natural Product Biosynthesis Using Computational Chemistry.

The biosynthetic pathways of natural products are complicated, and it is difficult to fully elucidate their details using experimental chemistry alone. In recent years, efforts have been made to elucidate the biosynthetic reaction mechanisms by combining computational and experimental methods. In this review, we will discuss the biosynthetic studies using computational chemistry for various terpene compounds such as cyclooctatin, sesterfisherol, quiannulatene, trichobrasilenol, asperterpenol, preasperterpenoid, spiroviolene, and mangicol.

Electrospray ionization tandem mass spectrometry of 4-aryl-3,4-dihydrocoumarins.

We have investigated the gas-phase fragmentation reactions of 11 synthetic 4-aryl-3,4-dihydrocoumarins by electrospray ionization tandem mass spectrometry (ESI-MS/MS) on a quadrupole-time-of flight (Q-TOF) hybrid mass spectrometer. We have also estimated thermochemical data for the protonated coumarins (precursor ion A) and product ion structures by computational chemistry at a B3LYP level of theory to establish the ion structures and to rationalize the fragmentation pathways. The most abundant ions in the product ion spectra of coumarins 1-11 resulted from C8H8O2, CO2, C4H4O3, C8H10O3, C8H8O2, and CH3OH eliminations through retro-Diels-Alder (RDA) reactions, remote hydrogen rearrangements (ß-eliminations), and ß-lactone ring contraction. Although the investigated coumarins shared most of the fragmentation pathways, formation of a benzylic product ion and its corresponding tropylium ion was diagnostic of the substituents at ring C. The thermochemical data revealed that the nature and position of the substituents at ring C played a key role in the formation of this product ion and determined its relative intensity in the product ion spectrum. The results of this study contribute to knowledge of the gas-phase ion chemistry of this important class of organic compounds.

Prediction of Covalent Metal-Metal Bonding in Cp-M-M'-Nacnac Complexes of Group 2 and 12 Metals (Be, Mg, Ca, Zn, Cd, Hg).

Bimetallic CpMM'Nacnac molecules with group 2 and 12 metals (M = Be, Mg, Ca, Zn, Cd, Hg) that contain novel metal-metal bonding have been investigated in a theoretical study of their molecular and electronic structure, thermodynamic stability, and metal-metal bonding. In all cases the metal-metal bonds are characterized as electron-sharing covalent single bonds from natural bond orbital (NBO) and energy-decomposition analysis with natural orbitals of chemical valence (EDA-NOCV) analysis. The sum of [MM'] charges is relatively constant, with all complexes exhibiting a [MM']2+ core. Quantum theory of atoms in molecules (QTAIM) analysis indicates the presence of non-nuclear attractors (NNA) in the metal-metal bonds of the BeBe, MgMg, and CaCa complexes. There is substantial electron density (0.75-1.33 e) associated with the NNAs, which indicates that these metal-metal bonds, while classified as covalent electron-sharing bonds, retain significant metallic character that can be associated with reducing reactivity of the complex. The predicted stability of these complexes, combined with their novel covalent metal-metal bonding and potential as reducing agents, make them appealing targets for the synthesis of new metal-metal bonds.

Targeting SHP2 Cryptic Allosteric Sites for Effective Cancer Therapy.

SHP2, a pivotal component downstream of both receptor and non-receptor tyrosine kinases, has been underscored in the progression of various human cancers and neurodevelopmental disorders. Allosteric inhibitors have been proposed to regulate its autoinhibition. However, oncogenic mutations, such as E76K, convert SHP2 into its open state, wherein the catalytic cleft becomes fully exposed to its ligands. This study elucidates the dynamic properties of SHP2 structures across different states, with a focus on the effects of oncogenic mutation on two known binding sites of allosteric inhibitors. Through extensive modeling and simulations, we further identified an alternative allosteric binding pocket in solution structures. Additional analysis provides insights into the dynamics and stability of the potential site. In addition, multi-tier screening was deployed to identify potential binders targeting the potential site. Our efforts to identify a new allosteric site contribute to community-wide initiatives developing therapies using multiple allosteric inhibitors to target distinct pockets on SHP2, in the hope of potentially inhibiting or slowing tumor growth associated with SHP2.

Machine Learning of Reactive Potentials.

In the past two decades, machine learning potentials (MLPs) have driven significant developments in chemical, biological, and material sciences. The construction and training of MLPs enable fast and accurate simulations and analysis of thermodynamic and kinetic properties. This review focuses on the application of MLPs to reaction systems with consideration of bond breaking and formation. We review the development of MLP models, primarily with neural network and kernel-based algorithms, and recent applications of reactive MLPs (RMLPs) to systems at different scales. We show how RMLPs are constructed, how they speed up the calculation of reactive dynamics, and how they facilitate the study of reaction trajectories, reaction rates, free energy calculations, and many other calculations. Different data sampling strategies applied in building RMLPs are also discussed with a focus on how to collect structures for rare events and how to further improve their performance with active learning.

In Situ Quantitative Monitoring of Adsorption from Aqueous Phase by UV-vis Spectroscopy: Implication for Understanding of Heterogeneous Processes.

The development of in situ techniques to quantitatively characterize the heterogeneous reactions is essential for understanding physicochemical processes in aqueous phase. In this work, a new approach coupling in situ UV-vis spectroscopy with a two-step algorithm strategy is developed to quantitatively monitor heterogeneous reactions in a compact closed-loop incorporation. The algorithm involves the inverse adding-doubling method for light scattering correction and the multivariate curve resolution-alternating least squares (MCR-ALS) method for spectral deconvolution. Innovatively, theoretical spectral simulations are employed to connect MCR-ALS solutions with chemical molecular structural evolution without prior information for reference spectra. As a model case study, the aqueous adsorption kinetics of bisphenol A onto polyamide microparticles are successfully quantified in a one-step UV-vis spectroscopic measurement. The practical applicability of this approach is confirmed by rapidly screening a superior adsorbent from commercial materials for antibiotic wastewater adsorption treatment. The demonstrated capabilities are expected to extend beyond monitoring adsorption systems to other heterogeneous reactions, significantly advancing UV-vis spectroscopic techniques toward practical integration into automated experimental platforms for probing aqueous chemical processes and beyond.

Toward Rational Design of Mononuclear Nickel Complexes as Water Oxidation Catalysts Exploring the Ligand Effects on the Rate-Determining Step.

The major impediment in realizing a carbon-neutral hydrogen fuel economy is the cost and inadequacy of contemporary electrochemical water splitting approaches towards the energy intensive oxygen evolution reaction (OER). The O-O bond formation in the water oxidation half-cell reaction is both kinetically and thermodynamically challenging and amplifies the overpotential requirement in most of the active water oxidation catalysts. Herein, density functional theory is employed to interrogate 20 Ni(II) complexes, out of which 17 are in silico designed molecular water oxidation catalysts, coordinated to electron-rich tetra-anionic redox non-innocent phenylenebis(oxamidate) and dibenzo-1,4,7,10-tetraazacyclododecane-2,3,8,9-tetraone parent ligands and their structural analogues, and identify the role of substituent changes or ligand effects in the order of their reactivity. Importantly, our computational mechanistic analyses predict that the activation free energy of the rate-determining O-O bond formation step obeys an inverse scaling relationship with the global electrophilicity index of the intermediate generated on two-electron oxidation of the starting complex. Additionally, the driving force is directly correlated with this OER descriptor which enables two-dimensional volcano representation and thereby extrapolation towards the ideal substitution with the chosen ligand. Our study, therefore, establish fundamental insights to overcome the imperative overpotential issue with simple and precise computational rationalization preceding experimental validation.

Docking carbocations into terpene synthase active sites using chemically meaningful constraints-The TerDockin approach.

Terpenes are a diverse class of natural products which have long been sought after for their chemical properties as medicine, perfumes, and for food flavoring. Computational docking studies of terpene mechanisms have been a challenge due to the lack of strong directing groups which many docking programs rely on. In this chapter, we dive into our computational method Terdockin (Terpene-Docking) as a successful methodology in modeling terpene synthase mechanisms. This method could also be used as inspiration for any multi-ligand docking project.

The Excited State Dynamics of a Mutagenic Guanosine Etheno Adduct Investigated by Femtosecond Fluorescence Spectroscopy and Quantum Mechanical Calculations.

Femtosecond fluorescence upconversion experiments were combined with CASPT2 and time dependent DFT calculations to characterize the excited state dynamics of the mutagenic etheno adduct 1,N2-etheno-2'-deoxyguanosine (ϵdG). This endogenously formed lesion is attracting great interest because of its ubiquity in human tissues and its highly mutagenic properties. The ϵdG fluorescence is strongly modified with respect to that of the canonical nucleoside dG, notably by an about 6-fold increase in fluorescence lifetime and quantum yield at neutral pH. In addition, femtosecond fluorescence upconversion experiments reveal the presence of two emission bands with maxima at 335 nm for the shorter-lived and 425 nm for the longer-lived. Quantum mechanical calculations rationalize these findings and provide absorption and fluorescence spectral shapes similar to the experimental ones. Two different bright minima are located on the potential energy surface of the lowest energy singlet excited state. One planar minimum, slightly more stable, is associated with the emission at 335 nm, whereas the other one, with a bent etheno ring, is associated with the red-shifted emission.

Error Awareness in the Volcano Plots of Oxygen Electroreduction to Hydrogen Peroxide.

Electrocatalysis holds the key to the decentralized production of hydrogen peroxide via the two-electron oxygen reduction reaction (ORR, O2g+2H++e-→H2O2aq). However, cost-effective, active, and selective catalysts are still sought after. While density functional theory (DFT) has already led to the discovery of various enhanced catalysts, it has a severe yet often unnoticed drawback: the ill description of O2 and H2O2. Here, we analyze the impact of the errors in those two species on the most widespread activity plots in the literature, namely free-energy diagrams and Sabatier-type volcano plots. Uncorrected or partially corrected gas-phase energies lead to appreciably different activity plots that may provide inaccurate predictions. Indeed, we show for a variety of electrocatalysts that only when the errors in O2 and H2O2 are corrected can DFT mimic the experiments. In sum, this work provides concrete guidelines to avoid a common pitfall of computational models for electrocatalytic hydrogen peroxide production.

AnoChem: Prediction of chemical structural abnormalities based on machine learning models.

De novo drug design aims to rationally discover novel and potent compounds while reducing experimental costs during the drug development stage. Despite the numerous generative models that have been developed, few successful cases of drug design utilizing generative models have been reported. One of the most common challenges is designing compounds that are not synthesizable or realistic. Therefore, methods capable of accurately assessing the chemical structures proposed by generative models for drug design are needed. In this study, we present AnoChem, a computational framework based on deep learning designed to assess the likelihood of a generated molecule being real. AnoChem achieves an area under the receiver operating characteristic curve score of 0.900 for distinguishing between real and generated molecules. We utilized AnoChem to evaluate and compare the performances of several generative models, using other metrics, namely SAscore and Fréschet ChemNet distance (FCD). AnoChem demonstrates a strong correlation with these metrics, validating its effectiveness as a reliable tool for assessing generative models. The source code for AnoChem is available at https://github.com/CSB-L/AnoChem.

Experimental and theoretical insights into Artemisia Stems aqueous extract as a sustainable and eco-friendly corrosion inhibitor for mild steel in 1 M HCl environment.

The present research demonstrates an innovative investigation of environmentally friendly mild steel (M-steel) corrosion inhibition using the artemisia stems aqueous extract (ASAEx) as an inhibitor in hydrochloric acid 1 M. The standard extraction technique of hydrodistillation was used for producing the aqueous solutions of ASAEx. To assess the ratios of the chemical components, phytochemical screening was used to identify the stems of this plant. We used a variety of methods and techniques in our research on corrosion inhibition, including weight loss measures, surface analysis methods like XPS and SEM/EDS, electrochemical testing like PDP and EIS, as well as computational lead compound evaluation. Maximum inhibitory efficacy was achieved with 400 mg/L ASAEx in 1 M HCl at 303 K, i.e. 90%. The PDP investigation verified the mixed-kind inhibitor status of the ASAEx extract. To describe the surface of M-steel, fitting and synthetic data were used to identify a constant phase element (CPE). SEM surface analysis was also used to detect the ASAEx effect on the surface of M-steel. X-ray photoelectron spectroscopy (XPS) analysis shows the presence of trace molecules of ASAEx on M-steel surface characterizing the bands in Maj-ASAEx (major compound of ASAEx). Density functional theory (DFT) and molecular dynamics simulations (MDs) were used in computational chemistry to clarify the adsorption mechanism and inhibitory impact.

Theoretical Rotational and Vibrational Spectral Data for the Hypermagnesium Oxide Species Mg2O and Mg2O.

While magnesium is astronomically observed in small molecules, it largely serves as a contributor to silicate grains, though how these grains form is not well-understood. The smallest hypermagnesium oxide compounds (Mg 2 ${{}_{2}}$ O/Mg 2 ${{}_{2}}$ O + ${{}^{+}}$ ) may play a role in silicate formation, but little vibrational reference data exist. As such, anharmonic spectroscopic data are computed for X ˜ 1 Σ g + ${{{\tilde{\rm {X}}}}^1 {\rm{\Sigma }}_g^+ }$ Mg 2 ${{}_{2}}$ O, a ˜ 1 Σ u + ${{{\tilde{\rm {a}}}}^1 {\rm{\Sigma }}_u^+ }$ Mg 2 ${{}_{2}}$ O, and X ˜ 2 Σ g + ${{{\tilde{\rm {X}}}}^2 {\rm{\Sigma }}_g^+ }$ Mg 2 ${{}_{2}}$ O + ${{}^{+}}$ using quartic force fields (QFFs). Explicitly-correlated coupled-cluster QFFs for the neutral species perform well, implying that full multireference treatment may not be necessary for such systems if enough electron correlation is included. Equation-of-motion ionization potential (EOMIP) methods for X ˜ 2 Σ g + ${{{\tilde{\rm {X}}}}^2 {\rm{\Sigma }}_g^+ }$ Mg 2 ${{}_{2}}$ O + ${{}^{+}}$ QFFs circumvent previous symmetry breaking issues even in explicitly-correlated coupled-cluster results, motivating the need for EOMIP treatments at minimum for such systems. All three species are found to have high-intensity vibrational frequencies. Even so, the highly intense frequency ( X ˜ 1 Σ g + ${{{\tilde{\rm {X}}}}^1 {\rm{\Sigma }}_g^+ }$ Mg 2 ${{}_{2}}$ O: 894.7 cm-1/11.18 µm; a ˜ 1 Σ u + ${{{\tilde{\rm {a}}}}^1 {\rm{\Sigma }}_u^+ }$ Mg 2 ${{}_{2}}$ O: 915.0 cm-1/10.91 µm) for either neutral state may be astronomically obscured by the polycyclic aromatic hydrocarbon 11.2 µm band. Mg 2 ${{}_{2}}$ O + ${{}^{+}}$ may be less susceptible to such obfuscation, and its ν 1 ${{\nu }_{1}}$ intensity is computed to be a massive 4793 km mol-1.

Are Redox-Active Centers Bridged by Saturated Flexible Linkers Systematically Electrochemically Independent?

The extent to which electrophores covalently bridged by a saturated linker are electrochemically independent was investigated considering the charge/spin duality of the electron and functionality of the electrophore as a spin carrier upon reduction. By combining computational modeling with electrochemical experiments, we investigated the mechanism by which tethered electrophores react together within 4,4'-oligo[n]methylene-bipyridinium assemblies (with n=2 to 5). We show that native dicationic electrophores (redox state Z=+2) are folded prior to electron injection into the system, allowing the emergence of supra-molecular orbitals (supra-MOs) likely to support the process of the reductive σ bond formation giving cyclomers. Indeed, for Z=+2, London Dispersion (LD) forces contribute to flatten the potential energy surface such that all-trans and folded conformers are approximately isoenergetic. Then, upon one-electron injection, for radical cations (Z=+1), LD forces significantly stabilize the folded conformers, except for the ethylene derivative deprived of supra-MOs. For radical cations equipped with supra-MOs, the unpaired electron is delocalized over both heterocycles through space. Cyclomer completion (Z=0) upon the second electron transfer occurs according to the inversion of redox potentials. This mechanism explains why intramolecular reactivity is favored and why pyridinium electrophores are not independent.