Modified Electrostatic Complementary Score Function and Its Application Boundary Exploration in Drug Design.

In recent years, machine learning (ML) models have been found to quickly predict various molecular properties with accuracy comparable to high-level quantum chemistry methods. One such example is the calculation of electrostatic potential (ESP). Different ESP prediction ML models were proposed to generate surface molecular charge distribution. Electrostatic complementarity (EC) can apply ESP data to quantify the complementarity between a ligand and its binding pocket, leading to the potential to increase the efficiency of drug design. However, there is not much research discussing EC score functions and their applicability domain. We propose a new EC score function modified from the one originally developed by Bauer and Mackey, and confirm its effectiveness against the available Pearson's R correlation coefficient. Additionally, the applicability domain of the EC score and two indices used to define the EC score application scope will be discussed.

Detecting different pesticide residues on Hami melon surface using hyperspectral imaging combined with 1D-CNN and information fusion.

Efficient, rapid, and non-destructive detection of pesticide residues in fruits and vegetables is essential for food safety. The visible/near infrared (VNIR) and short-wave infrared (SWIR) hyperspectral imaging (HSI) systems were used to detect different types of pesticide residues on the surface of Hami melon. Taking four pesticides commonly used in Hami melon as the object, the effectiveness of single-band spectral range and information fusion in the classification of different pesticides was compared. The results showed that the classification effect of pesticide residues was better by using the spectral range after information fusion. Then, a custom multi-branch one-dimensional convolutional neural network (1D-CNN) model with the attention mechanism was proposed and compared with the traditional machine learning classification model K-nearest neighbor (KNN) algorithm and random forest (RF). The traditional machine learning classification model accuracy of both models was over 80.00%. However, the classification results using the proposed 1D-CNN were more satisfactory. After the full spectrum data was fused, it was input into the 1D-CNN model, and its accuracy, precision, recall, and F1-score value were 94.00%, 94.06%, 94.00%, and 0.9396, respectively. This study showed that both VNIR and SWIR hyperspectral imaging combined with a classification model could non-destructively detect different pesticide residues on the surface of Hami melon. The classification result using the SWIR spectrum was better than that using the VNIR spectrum, and the classification result using the information fusion spectrum was better than that using SWIR. This study can provide a valuable reference for the non-destructive detection of pesticide residues on the surface of other large, thick-skinned fruits.

Non-Destructive Detection of Different Pesticide Residues on the Surface of Hami Melon Classification Based on tHBA-ELM Algorithm and SWIR Hyperspectral Imaging.

In the field of safety detection of fruits and vegetables, how to conduct non-destructive detection of pesticide residues is still a pressing problem to be solved. In response to the high cost and destructive nature of existing chemical detection methods, this study explored the potential of identifying different pesticide residues on Hami melon by short-wave infrared (SWIR) (spectral range of 1000-2500 nm) hyperspectral imaging (HSI) technology combined with machine learning. Firstly, the classification effects of classical classification models, namely extreme learning machine (ELM), support vector machine (SVM), and partial least squares discriminant analysis (PLS-DA) on pesticide residues on Hami melon were compared, ELM was selected as the benchmark model for subsequent optimization. Then, the effects of different preprocessing treatments on ELM were compared and analyzed to determine the most suitable spectral preprocessing treatment. The ELM model optimized by Honey Badger Algorithm (HBA) with adaptive t-distribution mutation strategy (tHBA-ELM) was proposed to improve the detection accuracy for the detection of pesticide residues on Hami melon. The primitive HBA algorithm was optimized by using adaptive t-distribution, which improved the structure of the population and increased the convergence speed. Compared the classification results of tHBA-ELM with HBA-ELM and ELM model optimized by genetic algorithm (GA-ELM), the tHBA-ELM model can accurately identify whether there were pesticide residues and different types of pesticides. The accuracy, precision, sensitivity, and F1-score of the test set was 93.50%, 93.73%, 93.50%, and 0.9355, respectively. Metaheuristic optimization algorithms can improve the classification performance of classical machine learning classification models. Among all the models, the performance of tHBA-ELM was satisfactory. The results indicated that SWIR-HSI coupled with tHBA-ELM can be used for the non-destructive detection of pesticide residues on Hami melon, which provided the theoretical basis and technical reference for the detection of pesticide residues in other fruits and vegetables.

Effects of Different Drying Methods on Drying Characteristics and Quality of Glycyrrhiza uralensis (Licorice).

Large amounts of waste result from licorice mold rot; moreover, prompt drying directly influences product quality and value. This study compared various glycyrrhiza drying methods (Hot air drying (HAD), infrared combined hot air drying (IR-HAD), vacuum freeze drying (VFD), microwave vacuum drying (MVD), and vacuum pulsation drying (VPD)) that are used in the processing of traditional Chinese medicine. To investigate the effects of various drying methods on the drying characteristics and internal quality of licorice slices, their color, browning, total phenol, total flavonoid, and active components (liquiritin and glycyrrhizic acid) were chosen as qualitative and quantitative evaluation indices. Our results revealed that VFD had the longest drying time, but it could effectively maintain the contents of total phenol, total flavonoid, and liquiritin and glycyrrhizic acid. The results also showed that VFD samples had the best color and the lowest degree of browning, followed by HAD, IR-HAD, and VPD. We think that VFD is the best approach to ensure that licorice is dry.

Multiscale Deepspectra Network: Detection of Pyrethroid Pesticide Residues on the Hami Melon.

The problem of pyrethroid residues has become a topical issue, posing a potential food safety concern. Pyrethroid pesticides are widely used to prevent and combat pests in Hami melon cultivation. Due to its high sensitivity and accuracy, gas chromatography (GC) is used most frequently for detecting pyrethroid pesticide residues. However, GC has a high cost and complex operation. This study proposed a deep-learning approach based on the one-dimensional convolutional neural network (1D-CNN), named Deepspectra network, to detect pesticide residues on the Hami melon based on visible/near-infrared (380-1140 nm) spectroscopy. Three combinations of convolution kernels were compared in the single-scale Deepspectra network. The convolution group of "5 × 1" and "3 × 1" kernels obtained a better overall performance. The multiscale Deepspectra network was compared to three single-scale Deepspectra networks on the preprocessing spectral data and obtained better results. The coefficient of determination (R2) for lambda-cyhalothrin and beta-cypermethrin was 0.758 and 0.835, respectively. The residual predictive deviation (RPD) for lambda-cyhalothrin and beta-cypermethrin was 2.033 and 2.460, respectively. The Deepspectra networks were compared with two conventional regression models: partial least square regression (PLSR) and support vector regression (SVR). The results showed that the multiscale Deepspectra network outperformed the other models. It was found that the multiscale Deepspectra network could be a novel approach for the quantitative estimation of pyrethroid pesticide residues on the Hami melon. These findings can also provide an effective strategy for spectral analysis.

Synthesis, characterization, and electrochemical properties of mono-, di-, and trinuclear transition Metal [60]fullerene complexes containing diphosphine cis-Ph2PCH=CHPPh2 ligand.

New transition metal fullerene complexes containing cis-Ph2PCH=CHPPh2 (dppet) ligand have been investigated. The mononuclear complexes (etau2-C60)M(cis-dppet) (1, 2; M = Pd, Pt) were prepared by reaction of C60 with M(dba)2 (dba = dibenzylideneacetone) followed by treatment with cis-dppet, while the in situ prepared 1 and 2 reacted with M1(PPh3)4 to afford dinuclear complexes (eta2 : eta2-C60)M(cis-dppet)M1 (PPh3)2 (3-6; M, M1 = Pd, Pt). Similarly, trinuclear complexes (eta2 : eta2-C60) M(cis-dppet)M1 (dppr) (7-10; M, M1 = Pd, Pt; dppr = (eta5-Ph2PC5H4)2Ru) could be synthesized by reaction of the in situ prepared 3-6 with dppr. 1-10 were characterized by elemental analysis and spectroscopy. Cyclic voltammetric studies on 2 (M = Pt), 3 (M = Pd, M1 = Pd) and 9 (M = Pt, M1 = Pd) provided further evidence for the eta2-coordination of C60 to one metal fragment or two metal fragments in these complexes.

Synthesis, characterization and electrocatalysis of diiron propanediselenolate derivatives as the active site models of [FeFe]-hydrogenases.

As an extension of our study on the H-cluster model compounds, a series of diiron propanediselenolate (PDS)-type models have been successfully synthesized. Reaction of diselenol HSe(CH(2))(3)SeH with Fe(3)(CO)(12) in THF (tetrahydrofuran) at reflux gave the parent model compound [micro-Se(CH(2))(3)Se-micro]Fe(2)(CO)(6) (1) in 48% yield. Further reaction of 1 with PPh(3) or PPh(2)H in the presence of Me(3)NO in MeCN at room temperature afforded the phosphine-monosubstituted model compounds [micro-Se(CH(2))(3)Se-micro]Fe(2)(CO)(5)(L) (2, L=PPh(3); 3, L=PPh(2)H) in 76% and 68% yields, respectively. Similarly, the N-heterocyclic carbene I(Mes)-monosubstituted model compound [micro-Se(CH(2))(3)Se-micro]Fe(2)(CO)(5)(I(Mes)) (4) could be prepared in 46% yield by reaction of imidazolium salt I(Mes).HCl with n-BuLi followed by treatment of the resulting I(Mes) ligand with 1 in THF at room temperature. Compounds 1-4 were fully characterized by elemental analysis and various spectroscopic methods. While the structures of 1-4 were further confirmed by X-ray crystallography, the comparative study of 1 and its analog [micro-S(CH(2))(3)S-micro]Fe(2)(CO)(6) demonstrates that 1 is a better catalyst for TsOH proton reduction to hydrogen under electrochemical conditions.

Synthesis and structural characterization of the mono- and diphosphine-containing diiron propanedithiolate complexes related to [FeFe]-hydrogenases. Biomimetic H2 evolution catalyzed by (mu-PDT)Fe2(CO)4[(Ph2P)2N(n-Pr)].

As the new H-cluster models, six diiron propanedithiolate (PDT) complexes with mono- and diphosphine ligands have been prepared and structurally characterized. The monophosphine model complex (mu-PDT)Fe(2)(CO)(5)[Ph(2)PNH(t-Bu)] (1) was prepared by reaction of parent complex (mu-PDT)Fe(2)(CO)(6) (A) with 1 equiv of Ph(2)PNH(t-Bu) in refluxing xylene, whereas A reacted with 1 equiv of Me(3)NO.2H(2)O in MeCN at room temperature followed by 1 equiv of Ph(2)PH to give the corresponding monophosphine model complex (mu-PDT)Fe(2)(CO)(5)(Ph(2)PH) (2). Further treatment of 2 with 1 equiv of n-BuLi in THF at -78 degrees C followed by 1 equiv of CpFe(CO)(2)I from -78 degrees C to room temperature afforded monophosphine model complex (mu-PDT)Fe(2)(CO)(5)[Ph(2)PFe(CO)(2)Cp] (3), whereas the diphosphine model complexes (mu-PDT)Fe(2)(CO)(4)(Ph(2)PC(2)H(4)PPh(2)) (4), (mu-PDT)Fe(2)(CO)(4)[(Ph(2)P)(2)N(n-Pr)] (5) and (mu-PDT)Fe(2)(CO)(4)[(Ph(2)P)(2)N(n-Bu)] (6) were obtained by reactions of A with ca.1 equiv of the corresponding diphosphines in refluxing xylene. All the new model complexes were characterized by elemental analysis, spectroscopy and particularly for 1 and 3-6 by X-ray crystallography. On the basis of electrochemical and spectroelectrochemical studies, model 5 was found to be a catalyst for HOAc proton reduction to H(2), and for this electrocatalytic reaction an ECCE mechanism was proposed.