Molecular Structural Information Research Articles

Enhancing the Search Performance of Bayesian Optimization by Creating Different Descriptor Datasets Using Density Functional Theory.

Descriptors calculated from molecular structure information can be used as explanatory variables in Bayesian optimization (BO). Even though structural and descriptor information can be obtained from various databases for general compounds, information on highly confidential compounds such as pharmaceutical intermediates and active pharmaceutical ingredients cannot be retrieved from these databases. In particular, determining the stable structure and electronic state of a compound via quantum chemical calculations from descriptor information requires considerable computational time. Although descriptor information can be obtained using density functional theory (DFT), which has a relatively light computational load, only conventional combinations of basis sets and functionals can be selected before experiments instead of the best ones. Few studies have discussed these effects on the search performance of BO, and good search performance is highly dependent on the application. Therefore, we developed a method to improve the search performance of BO by using descriptors computed from several combinations of basis sets and functionals. The dataset obtained from averaging multiple descriptor sets exhibited better BO search performance than that of a single descriptor dataset. In addition, the more descriptor sets used for averaging, the better the search performance. This method has a relatively small computational load and can be easily used by those who are unfamiliar with quantum chemical calculations.

HF-DDI: Predicting Drug-Drug Interaction Events Based on Multimodal Hybrid Fusion.

Drug-drug interactions (DDIs) can have a significant impact on patient safety and health. Predicting potential DDIs before administering drugs to patients is a critical step in drug development and can help prevent adverse drug events. In this study, we propose a novel method called HF-DDI for predicting DDI events based on various drug features, including molecular structure, target, and enzyme information. Specifically, we design our model with both early fusion and late fusion strategies and utilize a score calculation module to predict the likelihood of interactions between drugs. Our model was trained and tested on a large data set of known DDIs, achieving an overall accuracy of 0.948. The results suggest that incorporating multiple drug features can improve the accuracy of DDI event prediction and may be useful for improving drug safety and patient outcomes.

Synthesis, crystal structure, spectral analysis, DFT calculations, docking studies, in vitro biological activity evaluation and in silico drug-likeness prediction of a novel L-xylose derivative

In this research, we have reported the synthesis and crystal structure analysis of compound 1, a novel xylose derivative functioning as a dual SGLT1/2 inhibitor. Compound 1 exhibited outstanding inhibitory activity against both SGLT1 (IC50=101.1 ± 0.4 nm) and SGLT2 (IC50=8.2 ± 0.2 nm). Consequently, compound 1 was selected for a comprehensive investigation into its molecular structural information and its interaction with SGLT1 and SGLT2. The structure of the compound 1 was characterized by X-ray diffraction, Mass spectrometry, 1H and 13C-NMR , FT-IR, FT-Raman and ECD spectroscopy. The conformations were optimized and the vibrational spectrum was predicted using density functional theory (DFT), and compared with the crystal structure and experimental data. Furthermore, MEP, RDG and ELF analyses were conducted to characterize the reaction sites susceptible to electrophilic and nucleophilic attacks. FMOs was used to evaluate the stability of the molecular structure. Subsequently, molecular docking studies were performed to investigate the interaction between SGLT1/2 and compound 1. The ADMET predictions indicate that compound 1 exhibits favorable druggability and pharmacokinetic properties

Real-time and accurate calibration detection of gout stones based on terahertz and Raman spectroscopy.

Gout is a metabolic disease that can result in the formation of gout stones. It is essential to promptly identify and confirm the type of gout stone to alleviate pain and inflammation in patients and prevent complications associated with gout stones. Traditional detection methods, such as X-ray, ultrasound, CT scanning, and blood uric acid measurement, have limitations in early diagnosis. Therefore, this article aims to explore the use of micro Raman spectroscopy, Fourier transform infrared spectroscopy, and Terahertz time-domain spectroscopy systems to detect gout stone samples. Through comparative analysis, Terahertz technology and Raman spectroscopy have been found to provide chemical composition and molecular structure information of different wavebands of samples. By combining these two technologies, faster and more comprehensive analysis and characterization of samples can be achieved. In the future, handheld portable integrated testing instruments will be developed to improve the efficiency and accuracy of testing. Furthermore, this article proposes establishing a spectral database of gout stones and urinary stones by combining Raman spectroscopy and Terahertz spectroscopy. This database would provide accurate and comprehensive technical support for the rapid diagnosis of gout in clinical practice.

Mid-infrared chemical imaging of intracellular tau fibrils using fluorescence-guided computational photothermal microscopy

Amyloid proteins are associated with a broad spectrum of neurodegenerative diseases. However, it remains a grand challenge to extract molecular structure information from intracellular amyloid proteins in their native cellular environment. To address this challenge, we developed a computational chemical microscope integrating 3D mid-infrared photothermal imaging with fluorescence imaging, termed Fluorescence-guided Bond-Selective Intensity Diffraction Tomography (FBS-IDT). Based on a low-cost and simple optical design, FBS-IDT enables chemical-specific volumetric imaging and 3D site-specific mid-IR fingerprint spectroscopic analysis of tau fibrils, an important type of amyloid protein aggregates, in their intracellular environment. Label-free volumetric chemical imaging of human cells with/without seeded tau fibrils is demonstrated to show the potential correlation between lipid accumulation and tau aggregate formation. Depth-resolved mid-infrared fingerprint spectroscopy is performed to reveal the protein secondary structure of the intracellular tau fibrils. 3D visualization of the β-sheet for tau fibril structure is achieved.

The types of research data receiving scholarly credit within and across the science, engineering, and mathematics fields

This study examined the types of data that receive formal scholarly credit within and across the science, engineering, and mathematics (SEM) fields. The topics of whether data types are used in a way that encourages data reuse has not been actively studied. This study applied an exploratory method because formal data citation is a relatively new area. The Data Citation Index (DCI) of the Web of Science (WoS) was selected because the DCI provides a single access point to 400 data repositories worldwide across multiple disciplines. Nearly all citations were of quantitative data. The types that received the most credit were, in descending order, ribonucleic acid (RNA), crystal structure, protein sequence data, crystallographic data, Sequence Read Archive (SRA), genomic, images, nucleotide sequencing information, molecular structure, and crystallographic information, though citation was diverse across the various disciplines within these fields. In particular, qualitative data received no scholarly credit. This study contributes to better understanding of data types for data reuse.

Recent advances in Raman and surface enhanced Raman spectroelectrochemistry

Raman spectroscopy could supply the molecular vibrational process giving the detailed information of molecular structure. At the same time, electrochemistry could provide kinetic and thermodynamic processes. Integration of both technology with the general definition of using spectroscopic approaches for assaying the variations triggered through an electrochemistry-based system within an electrochemical cell induces a novel technique, spectroelectrochemistry (SEC). Raman-spectroelectrochemistry possesses interdisciplinary advantages and can further expand the scopes in the fields of analysis and other applications, emphasizing the cutting-edge issues of analytical chemistry, materials science, biophysics, chemical biology, and so on. This review emphasizes on the recent progress of integrated Raman/Surface enhanced Raman scattering-spectroelectrochemistry and aimed to summarize the spectroelectrochemistry device, electrode materials and applications of Raman/Surface enhanced Raman scattering-spectroelectrochemistry over the past several decades. Finally, the key issues, future perspectives and trends in the development of Raman/Surface enhanced Raman scattering-spectroelectrochemistry are discussed.

Identification of 4-hydroxyphenylpyruvate dioxygenase inhibitors by virtual screening, molecular docking, molecular dynamic simulation.

4-Hydroxyphenylpyruvate dioxygenase (HPPD) herbicides control broadleaf and gramineous weeds with better crop safety for corn, sorghum and wheat. Multiple screening models in silico have been established to obtain novel lead compounds as HPPD inhibition herbicides. Topomer comparative molecular field analysis (CoMFA) combined with topomer search technology and Bayesian, genetic approximation functions (GFA) and multiple linear regression (MLR) models generated by calculating different descriptors were constructed for the quinazolindione derivatives of HPPD inhibitors. The coefficient of determination (r2 ) of topomer CoMFA, MLR and GFA were 0.975, 0.970 and 0.968, respectively; all the models established displayed excellent accuracy and high predictive capacity. Five compounds with potential HPPD inhibition were obtained via screening fragment library combined with the validation of the above models and molecular docking studies. After molecular dynamics (MD) validation and absorption, distribution, metabolism, excretion and toxicity (ADMET) prediction, the compound 2-(2-amino-4-(4H-1,2,4-triazol-4-yl) benzoyl)-3-hydroxycyclohex-2-en-1-one not only exhibited stable interactions with the protein but also high solubility and low toxicity, and has potential as a novel HPPD inhibition herbicide. In this study, five compounds were obtained through multiple quantitative structure-activity relationship screening. Molecular docking and MD experiments showed that the constructed approach had good screening ability for HPPD inhibitors. This work provided molecular structural information for developing novel, highly efficient and low-toxicity HPPD inhibitors. © 2023 Society of Chemical Industry.

Diversified crop rotations and organic amendments as strategies for increasing soil carbon storage and stabilisation in UK arable systems

Adaptations in crop rotation with the inclusion of temporary grass-clover leys and organic amendments, have been promoted as effective ways to improve soil carbon (C) sequestration and mitigate climate change in agricultural systems. However, there are still a lot of uncertainties related to i) the combined effects of different crop rotations and different fertilisation sources, e.g., organic amendments, on soil C stocks; and ii) their potential effect on C stabilisation. The objective of this study was to evaluate the effect of different arable crop rotations with varying degrees of diversity in crop type and lengths of grass-clover ley periods and fertilisation sources on soil C stocks and C stabilisation down to 0.60 m soil depth. This was investigated in a long-term factorial field experiment-combining different crop rotation (cereal-intensive conventional vs. diversified legume-intensive organic) with different lengths of grass-clover ley periods (2 vs. 3 years), fertilisation sources (mineral vs. compost), and years (samples taken at the beginning and at the last year of one complete cycle of rotation; 8 years apart)-to explore their individual and interactive effect on soil C stock and C stabilisation at two soil depths (0–0.30 and 0.30–0.60 m). Soil C stabilisation was assessed using a unique combination of three different techniques: physical fractionation for separation of C associated to organic and mineral fractions, thermal analysis combined with differential scanning calorimetry and a quadrupole mass spectrometry (TG-DSC-QMS) for physical-chemical aspects, and pyrolysis coupled with gas chromatography-mass spectrometry (Py-GC/MS) for molecular structural information. The findings showed higher soil C stocks under the diversified organic rotation with 3 years of grass-clover ley period at both soil depths, regardless of the fertilisation source or sampling year. However, the organic rotation seemed to deliver stable soil C stocks only in the subsoil layer. Compost fertilisation, in turn, increased topsoil C stocks between the two sample dates under both rotations, and it appears to be stable. These results suggested that combining a diversified organic rotation with 3 years grass-clover ley with compost fertilisation could be one way for agricultural systems to deliver stable soil C sequestration.

Multi-view feature representation and fusion for drug-drug interactions prediction

BackgroundDrug-drug interactions (DDIs) prediction is vital for pharmacology and clinical application to avoid adverse drug reactions on patients. It is challenging because DDIs are related to multiple factors, such as genes, drug molecular structure, diseases, biological processes, side effects, etc. It is a crucial technology for Knowledge graph to present multi-relation among entities. Recently some existing graph-based computation models have been proposed for DDIs prediction and get good performance. However, there are still some challenges in the knowledge graph representation, which can extract rich latent features from drug knowledge graph (KG).ResultsIn this work, we propose a novel multi-view feature representation and fusion (MuFRF) architecture to realize DDIs prediction. It consists of two views of feature representation and a multi-level latent feature fusion. For the feature representation from the graph view and KG view, we use graph isomorphism network to map drug molecular structures and use RotatE to implement the vector representation on bio-medical knowledge graph, respectively. We design concatenate-level and scalar-level strategies in the multi-level latent feature fusion to capture latent features from drug molecular structure information and semantic features from bio-medical KG. And the multi-head attention mechanism achieves the optimization of features on binary and multi-class classification tasks. We evaluate our proposed method based on two open datasets in the experiments. Experiments indicate that MuFRF outperforms the classic and state-of-the-art models.ConclusionsOur proposed model can fully exploit and integrate the latent feature from the drug molecular structure graph (graph view) and rich bio-medical knowledge graph (KG view). We find that a multi-view feature representation and fusion model can accurately predict DDIs. It may contribute to providing with some guidance for research and validation for discovering novel DDIs.

Crystal structure, DFT calculation, molecular docking, in vitro biological activity evaluation and in silico drug-likeness prediction of (E)-N-(4-bromophenyl)-4-(2-(2-hydroxybenzylidene) hydrazine-1-carbonyl) benzenesulfonamide

Herein, a novel Mtb LeuRS inhibitor compound 1A was synthesized and crystallized. And compound 1A was found to be active targeting Mtb LeuRS with IC50 values =21.37 ± 0.2 μM and against Mtb H37Ra with MIC values =18.85 ± 0.2 μM. Therefore, compound 1A was selected to conduct deeply research on molecular structure information and interaction with Mtb LeuRS. Firstly, quantum chemical calculation was used to predict spectral properties of the compound 1A, such as, structure optimization, IR, Raman, 1HNMR and 13CNMR . What's more, Hirshfeld surface analysis was used to explain multiple interactions of compound 1A, and 2D fingerprint was used to quantify intermolecular interactions accurately. Molecular electrostatic potential (MEP), reduced density gradient (RDG) and Electron localization function (ELF) were used to describe the relevant sites that can form electrostatic interaction. FMOs were used to determine chemical stability of compound 1A. At last, molecular docking were conducted to elaborate the interactions between Mtb LeuRS and compound 1A, and ADMET prediction indicates compound 1A has perfect druggability and pharmacokinetic properties.

A rapid extraction process monitoring of Swertia mussotii Franch. With near infrared spectroscopy

Swertia mussotii Franch. (SMF), a traditional Tibetan medicine, which has miraculous effect on treating hepatitis diseases. However, there is no research on its entire production process, and invisible production process has seriously hindered the SMF modern development. In this study, principal component analysis (PCA), subtractive spectroscopy, and two-dimensional correlation spectroscopy (2D-COS) were used to explain changes of characteristic groups in the extraction process. Four main characteristic peaks at 1884 nm, 1944 nm, 2246 nm and 2308 nm were identified to describe the changes of molecular structure information of total active components in SMF extraction process. In addition, multi critical quality attributes (CQAs) models were established by near-infrared spectroscopy (NIRS) combined with the total quantum statistical moment (TQSM). The coefficients of determination (R2eval and R2ival) were both greater than 0.99. The ratios of the standard deviation of validation to the standard error of the prediction (RPDe and RPDi) were greater than five. The quantitative model of AUCT could save time on primary data measurement by not requiring determination of indicator components compared with others. In conclusion, these results demonstrated that it was feasible to understand the SMF extraction process through AUCT and characteristic groups. These could realize the visual digital characterization and quality stability of the SMF extraction process.

Prediction of the Solubility of Acid Gas Hydrogen Sulfide in Green Solvent Ionic Liquids via Quantitative Structure–Property Relationship Models Based on the Molecular Structure

Ionic liquids (ILs) can be used as capturing acidic gases that damage the environment. By establishing a quantitative structure–property relationship (QSPR) model of the IL structure, temperature, pressure, and H2S solubility, it can be used to screen ILs with excellent properties. In this study, molecular descriptors (MD), molecular identifiers (MI), and combinations of MD and MI (MD_MI) are used to represent the structure of ILs, combining pressure and temperature as the input of the model; the QSPR model is built by coupling the two models of the deep neural network (DNN) and random forest (RF). We find that the model constructed by using MI to represent ILs and DNN has the best performance. The Shapley additive explanation (SHAP) method is used to analyze features to obtain the most valuable molecular structure information for prediction. The results show that the contribution degree and direction of different MD and MI in the prediction of H2S solubility are different and correctly identify the impact of environmental factors (temperature and pressure). Finally, the electrostatic potential (ESP) between H2S and ILs was calculated to study the influence of different carbon chain lengths on the solubility of H2S.

Pulsed Electron-Nuclear Double Resonance in the Fourier Regime.

Nuclear magnetic resonance (NMR) spectroscopy provides atomic-level molecular structural information. However, in molecules containing unpaired electron spins, NMR signals are difficult to measure directly. In such cases, data is obtained using the electron-nuclear double resonance (ENDOR) method, where nuclei are detected through their interaction with nearby unpaired electron spins. Unfortunately, electron spins spread the ENDOR signals, which challenges current acquisition techniques, often resulting in low spectral resolution that provides limited structural details. Here, we show that by using miniature microwave resonators to detect a small number of electron spins, integrated with miniature NMR coils, one can excite and detect a wide bandwidth of ENDOR data in a single pulse. This facilitates the measurement of ENDOR spectra with narrow lines spread over a large frequency range at much better spectral resolution than conventional approaches, which helps reveal details of the paramagnetic molecules' chemical structure that were not accessible before.

Novel structural feature-descriptor platform for machine learning to accelerate the development of organic photovoltaics

Although the evolution of organic photovoltaics (OPVs) has been remarkable, the discovery of new combinations of materials for ideal bulk heterojunction (BHJ) blend systems remains challenging because of their numerous considerations. Aiming to eliminate the typically labor-intensive trial-and-error approaches, machine learning (ML) models coupled with quantitative structure-property relationships are constructed to efficiently predict the photovoltaic parameters. To this end, a unique structural-feature descriptor that fragments a molecular structure into functional group (FG) units is designed. These units are indexed and the indices are combined to translate the molecular structure into a concise matrix of integers. Our novel descriptor allows the ML models to trace the FGs with ease and assign larger weights to those FGs that significantly contribute to the photovoltaic performance. Furthermore, the descriptor enables the expansion of OPV ML to ternary or multicomponent BHJ systems because of its concise expression. The proposed ML model achieves a high correlation coefficient of 0.86 between experimental and predictive power conversion efficiency of OPVs, despite using only the molecular structure information without any additional input data. Combined with the superior prediction capabilities, our novel approach promises to perfectly screen numerous BHJ cadidates, resulting in accelerating the development of the OPV.

Study on Inspection Mechanism of Organic Gems Based on Laser Raman Spectroscopy

Raman spectroscopy is a fast and nondestructive analysis and detection technology. Raman spectroscopy contains rich molecular structure information of substances. Based on the application status of Raman spectroscopy in the field of gemstones, this paper further explores its application technology in the qualitative identification of organic gemstones. In this paper, natural Longpai, Koba resin and rosin were tested based on laser Raman spectroscopy. The identification difficulties of high imitations of natural resins in Longpai are solved respectively. Therefore, the application of Raman spectroscopy to the identification and analysis of high imitations of organic gem amber and its natural resin has great advantages and convenience.

Cross-category prediction of corrosion inhibitor performance based on molecular graph structures via a three-level message passing neural network model

Current experimental verification, computational modeling, and machine learning methods for predicting corrosion inhibition efficiency (IE) are limited to specific inhibitor categories with high cost and poor generalization. In this study, a cross-category corrosion inhibitor dataset is constructed and a three-level direct message passing neural network (3 L–DMPNN) model using molecular structure information that integrates atomic-level, chemical bond-level, and molecular-level features to predict the IEs of compounds in a specific environment is established. This work demonstrates that the 3 L–DMPNN model can predict IEs of cross-category corrosion inhibitors from other independent literature and experimental dataset effectively and quickly.

Elements and Chemical Bonds Contribution Estimation of Activity Coefficients in Nonideal Liquid Mixtures

Based on the contribution of elements and chemical bonds, the UNICAC (Universal Quasi-Chemical elements and chemical bonds Activity Coefficient) method was proposed to estimate the activity coefficients of nonelectrolyte liquid mixtures. The UNICAC method defined 10 elements and 33 chemical bonds as contribution groups. The calculation of activity coefficients was divided into the combination term and the residual term. The combination term represents molecular size differences, and the residual term describes the interaction between molecules. The interaction energy parameters of 43 groups were regressed simultaneously with the experimental data of the vapor–liquid equilibrium of 1085 binary systems. According to the molecular structural information of compounds, the UNICAC method can accurately predict the activity coefficients of nonelectrolyte liquid mixtures. The vapor–liquid equilibrium of 16 groups of the binary system, which were not included in the parameters regress, was predicted using UNICAC. The average relative error of vapor composition was 1.53%. Compared with UNIFAC (2003), UNIFAC (Lyngby), UNIFAC (Dortmund), and ASOG (2011), the UNICAC model employs fewer parameters, provides a broader scope of application, and receives more precise predicted results of the vapor–liquid equilibrium. The UNICAC method would play an important reference role in the design of the chemical separation process.

Imaging Orientation of a Single Molecular Hierarchical Self-Assembled Sheet: The Combined Power of a Vibrational Sum Frequency Generation Microscopy and Neural Network.

In this work, we determined the tilt angles of molecular units in hierarchical self-assembled materials on a single-sheet level, which were not available previously. This was achieved by developing a fast line-scanning vibrational sum frequency generation (VSFG) hyperspectral imaging technique in combination with neural network analysis. Rapid VSFG imaging enabled polarization resolved images on a single sheet level to be measured quickly, circumventing technical challenges due to long-term optical instability. The polarization resolved hyperspectral images were then used to extract the supramolecular tilt angle of a self-assembly through a set of spectra-tilt angle relationships which were solved through neural network analysis. This unique combination of both novel techniques offers a new pathway to resolve molecular level structural information on self-assembled materials. Understanding these properties can further drive self-assembly design from a bottom-up approach for applications in biomimetic and drug delivery research.

NMR Relaxometry Using Outer Field of Single-Sided HTS Bulk Magnet Activated by Pulsed Field

We have succeeded in getting <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> H NMR signals at 47.8 MHz (1.12 T) from a single-sided HTS bulk magnet activated by pulse-field magnetization. Diffusion NMR using an inhomogeneous outer field was also demonstrated on the surface. This kind of NMR relaxometry can not get the information of molecular structures, which conventional NMR using a homogeneous magnetic field can acquire. However, the relaxation of nuclear spin magnetization is still observable. A higher magnetic field gradient generated by the small HTS bulk magnet, 67 T/m at a 5 mm distance from the surface, can be effectively used to observe the self-diffusion coefficient of liquids in higher spatial resolution, 44 μm. The easy accessibility to the strong field gradient is one of the unique properties ofcompact HTS bulk magnets. We believe that single-sided (unilateral) diffusion NMR will become the new application of HTS bulk magnets.