Quantitative Structure-activity Relationship Models of Monomer Reactivity

The reactivity parameters,Q and e,in the Q-e scheme reflect the reactivities of a monomer(or a radical)in free-radical copolymerizations.By applying multiple linear regression(MLR)analysis,the optimal quantitative structure-activity relationship(QSAR)model for the reactivity parameter lnQ was developed based on five descriptors(NAF,NOF,EαLUMO,EβHOMO,and EβLUMO)and 69 monomers with the root mean square(rms)error of 0.61.The optimal MLR model of the parameter e obtained from five descriptors(TOcl,NpN,NSO,EαHOMO and DH)and 68 monomers produced rms error of 0.42.Compared with previous models,the two optimal MLR models in this paper show satisfactory statistical characteristics.The feasibility of combining 2D descriptors obtained from the monomers and 3D descriptors calculated from the radical structures(formed from monomers+H )to predict parameters Q and e has been demonstrated.

Elucidating the structure-activity relationship of Cu-Ag bimetallic catalysts for electrochemical CO_(2) reduction

Developing bimetallic catalysts is an effective strategy for enhancing the activity and selectivity of electrochemical CO_(2) reduction reactions,where understanding the structure-activity relationship is essential for catalyst design.Herein,we prepared two Cu-Ag bimetallic catalysts with Ag nanoparticles attached to the top or the bottom of Cu nanowires.When tested in a flow cell,the Cu-Ag catalyst with Ag nanoparticles on the bottom achieved a faradaic efficiency of 54%for ethylene production,much higher than the catalyst with Ag nanoparticles on the top.The catalysts were further studied in the H-cell and zero-gap MEA cell.It was found that placing the two metals in the intensified reaction zone is crucial to triggering the tandem reaction of bimetallic catalysts.Our work elucidates the structure-activity relationship of bimetallic catalysts for CO_(2) reduction and demonstrates the importance of considering both catalyst structures and cell characteristics to achieve high activity and selectivity.

Review on mechanisms and structure-activity relationship of hypoglycemic effects of polysaccharides from natural resources

Diabetes mellitus(DM)is a common multifactorial disease,causing various complications,such as chronic metabolism.The current therapies for diabetes mellitus are commercial diabetic drugs that have different definite side effect.However,polysaccharides mainly extracted from natural resources,have advantages of safety,accessibility,and anti-diabetic potential.We have summarized recent research of natural polysaccharides with hypoglycemic activities,focusing on different pharmacological mechanisms in various cell and animal models.The relationships of structure-hypoglycemic effect are also discussed in detail.This review could provide a comprehensive perspective for better understanding on development and mechanism of natural polysaccharides against diabetes mellitus,which have been required by clinical studies yet.

Characterizing permeability-porosity relationships of porous rocks using a stress sensitivity model in consideration of elastic-structural deformation and tortuosity sensitivity

Clarifying the relationship between stress sensitivities of permeability and porosity is of great significance in guiding underground resource mining.More and more studies focus on how to construct stress sensitivity models to describe the relationship and obtain a comprehensive stress sensitivity of porous rock.However,the limitations of elastic deformation calculation and incompleteness of considered tortuosity sensitivity lead to the fact that the existing stress sensitivity models are still unsatisfactory in terms of accuracy and generalization.Therefore,a more accurate and generic stress sensitivity model considering elastic-structural deformation of capillary cross-section and tortuosity sensitivity is proposed in this paper.The elastic deformation is derived from the fractal scaling model and Hooke's law.Considering the effects of elastic-structural deformation on tortuosity sensitivity,an empirical formula is proposed,and the conditions for its applicability are clarified.The predictive performance of the proposed model for the permeability-porosity relationships is validated in several sets of publicly available experimental data.These experimental data are from different rocks under different pressure cycles.The mean and standard deviation of relative errors of predicted stress sensitivity with respect to experimental data are 2.63%and 1.91%.Compared with other models,the proposed model has higher accuracy and better predictive generalization performance.It is also found that the porosity sensitivity exponent a,which can describe permeability-porosity relationships,is 2 when only elastic deformation is considered.a decreases from 2 when structural deformation is also considered.In addition,a may be greater than 3 due to the increase in tortuosity sensitivity when tortuosity sensitivity is considered even if the rock is not fractured.

PCB CT Image Element Segmentation Model Optimizing the Semantic Perception of Connectivity Relationship

Computed Tomography(CT)is a commonly used technology in Printed Circuit Boards(PCB)non-destructive testing,and element segmentation of CT images is a key subsequent step.With the development of deep learning,researchers began to exploit the“pre-training and fine-tuning”training process for multi-element segmentation,reducing the time spent on manual annotation.However,the existing element segmentation model only focuses on the overall accuracy at the pixel level,ignoring whether the element connectivity relationship can be correctly identified.To this end,this paper proposes a PCB CT image element segmentation model optimizing the semantic perception of connectivity relationship(OSPC-seg).The overall training process adopts a“pre-training and fine-tuning”training process.A loss function that optimizes the semantic perception of circuit connectivity relationship(OSPC Loss)is designed from the aspect of alleviating the class imbalance problem and improving the correct connectivity rate.Also,the correct connectivity rate index(CCR)is proposed to evaluate the model’s connectivity relationship recognition capabilities.Experiments show that mIoU and CCR of OSPC-seg on our datasets are 90.1%and 97.0%,improved by 1.5%and 1.6%respectively compared with the baseline model.From visualization results,it can be seen that the segmentation performance of connection positions is significantly improved,which also demonstrates the effectiveness of OSPC-seg.

Advances in Research of Biological Activity,Action Mechanism and Structure-Activity Relationship of Lentinan

Lentinula edodes is the second largest edible mushroom in the world and is widely used as food and medicine.Modern research shows that lentinan(LNT)is the main active component of L.edodes.It has anti-cancer,treatment of diabetes,intestinal protection,anti-inflammatory,anti-oxidation,anti-aging,hepatoprotective,immune-regulating effects.In this review,the biological activity,action mechanism and structure-activity relationship of LNT in recent years are reviewed.On this basis,the existing problems were discussed,and the future research and application of LNT were prospected.Finally,it is hoped that this review will promote the in-depth study of LNT and provide a reference for its development as a drug and functional food.

Determination of structure-activity relationships between fentanyl analogs and human μ-opioid receptors based on active binding site models

Fentanyl is a potent and widely used clinical narcotic analgesic, as well as a highly selective IJ-opioid agonist. The present study established a homologous model of the human μ-opioid receptor; an intercomparison of three types of μ-opioid receptor protein sequence homologous rates was made. The secondary receptor structure was predicted, the model reliability was assessed and verified using the Ramachandran plot and ProTab analysis. The predictive ability of the CoMFA model was further validated using an external test set. Using the Surflex-Dock program, a series of fentanyl analog molecules were docked to the receptor, the calculation results from Biopolymer/SitelD showed that the receptor had a deep binding area situated in the extracellular side of the transmembrane domains （TM） among TM3, TM5, TM6, and TMT. Results suggested that there might be 5 active areas in the receptor. The important residues were Asp147, Tyr148, and Tyr149 in TM3, Trp293, and His297 in TM6, and Trp318, His319, Ile322, and Tyr326 in TM7, which were located at the 5 active areas. The best fentanyl docking orientation position was the piperidine ring, which was nearly perpendicular to the membrane surface in the 7 TM domains. Molecular dynamic simulations were applied to evaluate potential relationships between ligand conformation and fentanyl substitution.

Application of PCA and HCA to the Structure-Activity Relationship Study of Fluoroquinolones

Density functional theory （DFT） was used to calculate molecular descriptors （properties） for 12 fluoro-quinolone with anti-S.pneumoniae activity. Principal component analysis （PCA） and hierarchical cluster analysis （HCA） were employed to reduce dimensionality and investigate in which variables should be more effective for classifying fluoroquinolones according to their degree of an-S.pneumoniae activity. The PCA results showed that the variables ELUMO, Q3, Q5, QA, logP, MR, VOL and △EHL of these compounds were responsible for the anti-S.pneumoniae activity. The HCA results were similar to those obtained with PCA.The methodologies of PCA and HCA provide a reliable rule for classifying new fluoroquinolones with antiS.pneumoniae activity. By using the chemometric results, 6 synthetic compounds were analyzed through the PCA and HCA and two of them are proposed as active molecules with anti-S.pneumoniae, which is consistent with the results of clinic experiments.

Structure-activity Relationships of the Coloration and Stability of Anthocyanidins

Taking the six common anthocyanidins in nature, i.e. cyanidin, delphinidin, malvidin, pelargonidin, peonidin and petunidin, as examples, this paper summarized the main achievements about the structure-activity relationships of the coloration and stability of anthocyanidins. The coloration and stability of anthocyanidins are funda- mentally determined by the chemical and spatial structures of the anthocyanidins. The electron-deficient state, hydroxylation and methylation patterns, especially the ones on the B-ring, and coplanarity of the three rings of anthocyanidins are inde- pendently or synergetically, positively and/or negatively, influence the coloration and stability of the anthocyanidins. Thereinto, the in vivo colorations of anthocyanins are also related to the organ-selective and crystal- or anthocyanic vacuolar inclusion- related existence of the anthocyanidins. This review could provide a reference for the researches of the structure-optimizing and function-exploiting of anthocyanidins and also for the selection of the crops and cultivars containing specific anthocyani- din profiles.

Studies on the Synthesis, Anticonvulsant Activity, and the Structure-Activity Relationships of Phenyl Pyridazinones and their GABA Derivatives

In searching for effective anticonvulsant agents,fourteen 6-aryl-4.5-di- hydro-3(2H)pyridazinones.fifteen 6-aryl-3(2H)pyridazinones,and seventeen 3-GABA derivatives of 6-aryIpyridazines have been synthesized,and evaluated in mice for the ability to antagonize maximal electroshock seizure(MES).The ED_(50) values showed that 6-(2′,4′- dichlorophenyt)-3(2H)pyridazinone was the most potent anticonvulsant among these corn- pounds(ED_(50)=10.15 mg/kg).The structure-activity relationships of the aryl pyridazinones were studied.The result showed that:(1)the higher the value of the hydrophobic parameter л of the substituent on the phenyl ring.the more potent the anticonvulsant activity of the corn- pound.and(2)only the compounds with an electron withdrawing substituent on the phenyl ring exhibited appreciable anticonvulsant activity.

Toxicity of Selected Imidazolium-based Ionic Liquids on Caenorhabditis elegans： a Quantitative Structure-Activity Relationship Study

Due to the large number of ionic liquids （ILs） and their potential environmental risk, assessing the toxicity of ILs by ecotoxicological experiment only is insufficient. Quantitative structure- activity relationship （QSAR） has been proven to be a quick and effective method to estimate the viscosity, melting points, and even toxicity of ILs. In this work, the LC50 values of 30 imidazolium-based ILs were determined with Caenorhabditis elegans as a model animal. Four suitable molecular descriptors were selected on the basis of genetic function approximation algorithm to construct a QSAR model with an R^2 value of 0.938. The predicted lgLC50 in this work are in agreement with the experimental values, indicating that the model has good stability and predictive ability. Our study provides a valuable model to predict the potential toxicity of ILs with different sub-structures to the environment and human health.

Quantitative structure-activity relationship study on the biodegradation of acid dyestuffs

Quantitative structure-biodegradability relationships （QSBRs） were established to develop predictive models and mechanistic explanations for acid dyestuffs as well as biological activities. With a total of four descriptors, molecular weight （MW）, energies of the highest occupied molecular orbital （EHOMO）, the lowest unoccupied molecular orbital （ELUMO）, and the excited state （EES）, calculated using quantum chemical semi-empirical methodology, a series of models were analyzed between the dye biodegradability and each descriptor. Results showed that EHOMO and Mw were the dominant parameters controlling the biodegradability of acid dyes. A statistically robust QSBR model was developed for all studied dyes, with the combined application of EHOMO and Mw. The calculated biodegradations fitted well with the experimental data monitored in a facultative-aerobic process, indicative of the reliable prediction and mechanistic character of the developed model.

Structure-activity Relationships of Vasorelaxing Effect of Polyarginine Oligopeptides

Based on the EDRF(endothelium derived relaxing factor)-like effects for polyarginine Arg-Arg-oH was selected as the lead compound and its derivatives Arg-Arg- OCH_3.Arg Arg-Arg-OH,HO-ArgCOCH_2CH_2COArg-OH,HO-ArgCOCH_2COArg-OH,were synthesized.These substances showed on bioassay various degrees of vasorelaxant activities. With protection for the C-terminal of Arg-Arg-OH with a methyl ester.the vasorelaxing ac- tivitv was decreased.In contrast.when the N-terminal of Arg-OH was protected with mal- onic acid or butane diacid.the biological activites were lower than those of Arg-Arg-OH due to the lowered metabolic rate.With protection of N-terminal of Arg-Arg-OH with L-Arg residue.Arg-Arg-Arg-OH was obtained,which showed a vasorelaxing activity better than that of Arg-Arg-OH.The bioactivities observed on the Wister's rats for the former com- pound become the experimental basis for prodrug design of EDRF.

Quantitative Structure-activity Relationship Study on the Antioxidant Activity of Carotenoids

Carotenoids are a family of effective active oxygen scavengers, which can reduce the danger of occurrence of chronic diseases such as cardiovascular disease, cataract, cancer, and so on. The quantitative structure-activity relationship （QSAR） equation between carotenoids and antioxidant activity was established by quantum chemistry AM1, molecular mechanism （MM＋） and stepwise regression analysis methods, and the model was evaluated by leave-one-out approach. The results showed that the significant molecular descriptors related to the antioxidant activity of carotenoids were the energy difference （E_HL） between the lowest unoccupied molecular orbital （LUMO） and the highest occupied molecular orbital （HOMO） and ionization energy （Eiso）. The model showed a good predictive ability （Q^2 〉 0.5）.

New Descriptors of Amino Acids and Its Applications to Peptide Quantitative Structure-activity Relationship

A new set of descriptors, HSEHPCSV （component score vector of hydrophobic, steric, and electronic properties together with hydrogen bonding contributions）, were derived from principal component analyses of 95 physicochemical variables of 20 natural amino acids separately according to different kinds of properties described, namely, hydrophobic, steric, and electronic properties as well as hydrogen bonding contributions. HSEHPCSV scales were then employed to express structures of angiotensin-converting enzyme inhibitors, bitter tasting thresholds and bactericidal 18 peptide, and to construct QSAR models based on partial least square （PLS）. The results obtained are as follows： the multiple correlation coefficient （R2cum） of 0.846, 0.917 and 0.993, leave-one-out cross validated Q2cm of 0.835, 0.865 and 0.899, and root-mean-square error for estimated error （RMSEE） of 0.396, 0.187and 0.22, respectively. Satisfactory results showed that, as new amino acid scales, data of HSEHPCSV may be a useful structural expression methodology＇for the studies on peptide QSAR （quantitative structure-activity relationship） due to many advantages such as plentiful structural information, definite physical and chemical meaning and easy interpretation.

Solubility study of hydrogen in direct coal liquefaction solvent based on quantitative structure–property relationships model

Direct coal liquefaction(DCL)is an important and effective method of converting coal into high-valueadded chemicals and fuel oil.In DCL,heating the direct coal liquefaction solvent(DCLS)from low to high temperature and pre-hydrogenation of the DCLS are critical steps.Therefore,studying the dissolution of hydrogen in DCLS under liquefaction conditions gains importance.However,it is difficult to precisely determine hydrogen solubility only by experiments,especially under the actual DCL conditions.To address this issue,we developed a prediction model of hydrogen solubility in a single solvent based on the machine-learning quantitative structure–property relationship(ML-QSPR)methods.The results showed that the squared correlation coefficient R^(2)=0.92 and root mean square error RMSE=0.095,indicating the model’s good statistical performance.The external validation of the model also reveals excellent accuracy and predictive ability.Molecular polarization(a)is the main factor affecting the dissolution of hydrogen in DCLS.The hydrogen solubility in acyclic alkanes increases with increasing carbon number.Whereas in polycyclic aromatics,it decreases with increasing ring number,and in hydrogenated aromatics,it increases with hydrogenation degree.This work provides a new reference for the selection and proportioning of DCLS,i.e.,a solvent with higher hydrogen solubility can be added to provide active hydrogen for the reaction and thus reduce the hydrogen pressure.Besides,it brings important insight into the theoretical significance and practical value of the DCL.

Quantitative Structure-activity Relationship(QSAR) Study of Toxicity of Substituted Aromatic Compounds to Photobacterium Phosphoreum

With the artificial neural network（ANN） method combined with the multiple linear regression（MLR）,based on a series of quantum chemical descriptors and molecular connectivity indexes,quantitative structure-activity relationship（QSAR） models to predict the acute toxicity（-lgEC50） of substituted aromatic compounds to Photobacterium phosphoreum were established.Four molecular descriptors that appear in the MLR model,namely,the second order valence molecular connectivity index（2XV）,the energy of the highest occupied molecular orbital（EHOMO）,the logarithm of n-octyl alcohol/water partition coefficient（logKow） and the Connolly molecular area（MA）,were inputs of the ANN model.The root-mean-square error（RMSE） of the training and validation sets of the ANN model are 0.1359 and 0.2523,and the correlation coefficient（R） is 0.9810 and 0.8681,respectively.The leave-one-out（LOO） cross validated correlation coefficient（Q L2OO） of the MLR and ANN models is 0.6954 and 0.6708,respectively.The result showed that the two methods are complementary in the calculations.The regression method gave support to the neural network with physical explanation,and the neural network method gave a more accurate model for QSAR.In addition,some insights into the structural factors affecting the acute toxicity and toxicity mechanism of substituted aromatic compounds were discussed.

Application of PCA and HCA to the Structure-activity Relationship(SAR) Study of Fluoroquinolones

Density functional theory （DFT） was used to calculate a set of molecular descri ptors （properties） for 14 fluoroquinolones with anti-B.fragilis activity. Principal component analysis （PCA） and hierarchical cluster analysis （HCA） were employed in order to reduce dimensionality and investigate which subset of variables should be more effective for classifying fluoroquinolones according to their an-B.fragilis activity degree. The PCA shows that the variables of ELUMO, AEHL, μ, Q2, Q3, Q5, Q6, QB, LogP, MR and MP are responsible for the separation between compounds with higher and lower anti-B.fragilis activities. The HCA results are similar to those obtained with PCA. By using the chemometric results, 4 synthetic compounds were analyzed through PCA and HCA, and 2 of them are proposed as active molecules against B.fragilis, which is consistent with the results of clinic experiments. The methodologies of PCA and HCA provide a reliable rule for classifying new fluoroquinolones with anti-B.fragilis activity.

Consensus model of social network group decision-making based on trust relationship among experts and expert reliability

Due to people’s increasing dependence on social networks,it is essential to develop a consensus model considering not only their own factors but also the interaction between people.Both external trust relationship among experts and the internal reliability of experts are important factors in decision-making.This paper focuses on improving the scientificity and effectiveness of decision-making and presents a consensus model combining trust relationship among experts and expert reliability in social network group decision-making(SN-GDM).A concept named matching degree is proposed to measure expert reliability.Meanwhile,linguistic information is applied to manage the imprecise and vague information.Matching degree is expressed by a 2-tuple linguistic model,and experts’preferences are measured by a probabilistic linguistic term set(PLTS).Subsequently,a hybrid weight is explored to weigh experts’importance in a group.Then a consensus measure is introduced and a feedback mechanism is developed to produce some personalized recommendations with higher group consensus.Finally,a comparative example is provided to prove the scientificity and effectiveness of the proposed consensus model.

A Systematic Review of Uric Acid Transporter 1(URAT1) Inhibitors for the Treatment of Hyperuricemia and Gout and an Insight into the Structure-activity Relationship(SAR)

Gout is caused by the deposition of uric acid as monosodium urate（MSU）. Chronic hyperuricemia is the necessary condition for MSU deposition, which arises from over-production and/or under-excretion of uric acid. Renal under-excretion of uric acid accounts for greater than 90% of the patients with hyperuricemia, making URAT1 inhibitors, which act through uricosuric effect a promising class of urate-lowering therapy（ULT）. This review aims at the summary and discussion of the latest development of URAT1 inhibitors for the treatment of hyperuricemia and gout and providing an insight into their structure-activity relationship（SAR）, which will be helpful to the design of URAT1 inhibitors for both academic research and pharmaceutical industry. The current development pipeline of URAT1 inhibitors is promising and encouraging.