New Species and Cytotoxicity Mechanism of Halohydroxybenzonitrile Disinfection Byproducts in Drinking Water.

Recently, seven dihalohydroxybenzonitriles (diHHBNs) have been determined as concerning nitrogenous aromatic disinfection byproducts (DBPs) in drinking water. Herein, eight new monohalohydroxybenzonitriles (monoHHBNs), including 3-chloro-2-hydroxybenzonitrile, 5-chloro-2-hydroxybenzonitrile, 3-chloro-4-hydroxybenzonitrile, 3-bromo-2-hydroxybenzonitrile, 5-bromo-2-hydroxybenzonitrile, 3-bromo-4-hydroxybenzonitrile, 5-iodo-2-hydroxybenzonitrile, and 3-iodo-4-hydroxybenzonitrile, were detected and identified in drinking water for the first time. Thereafter, the relative concentration-cytotoxicity contribution of each HHBN was calculated based on the acquired occurrence level and cytotoxicity data in this study, the genome-scale cytotoxicity mechanism was explored, and a quantitative structure-activity relationship (QSAR) model was developed. Results indicated that new monoHHBNs were present in drinking water at concentrations of 0.04-1.83 ng/L and exhibited higher cytotoxicity than some other monohalogenated aromatic DBPs. Notably, monoHHBNs showed concentration-cytotoxicity contribution comparable to diHHBNs, which have been previously identified as potential toxicity drivers in drinking water. Transcriptomic analysis revealed immunotoxicity and genotoxicity as dominant cytotoxicity mechanisms for HHBNs in Chinese hamster ovary (CHO-K1) cells, with potential carcinogenic effects. The QSAR model suggested oxidative stress and cellular uptake efficiency as important factors for their cytotoxicity, highlighting the importance of potential iodinated HHBNs in drinking water, such as 3,5-diiodo-2-hydroxybenzonitrile, for future studies. These findings are meaningful for better understanding the health risk and toxicological significance of HHBNs in drinking water.

Two-Stage Machine Learning-Based Approach to Predict Points of Departure for Human Noncancer and Developmental/Reproductive Effects.

Chemical points of departure (PODs) for critical health effects are crucial for evaluating and managing human health risks and impacts from exposure. However, PODs are unavailable for most chemicals in commerce due to a lack of in vivo toxicity data. We therefore developed a two-stage machine learning (ML) framework to predict human-equivalent PODs for oral exposure to organic chemicals based on chemical structure. Utilizing ML-based predictions for structural/physical/chemical/toxicological properties from OPERA 2.9 as features (Stage 1), ML models using random forest regression were trained with human-equivalent PODs derived from in vivo data sets for general noncancer effects (n = 1,791) and reproductive/developmental effects (n = 2,228), with robust cross-validation for feature selection and estimating generalization errors (Stage 2). These two-stage models accurately predicted PODs for both effect categories with cross-validation-based root-mean-squared errors less than an order of magnitude. We then applied one or both models to 34,046 chemicals expected to be in the environment, revealing several thousand chemicals of moderate concern and several hundred chemicals of high concern for health effects at estimated median population exposure levels. Further application can expand by orders of magnitude the coverage of organic chemicals that can be evaluated for their human health risks and impacts.

An in silico approach to identify novel and potential Akt1 (protein kinase B-alpha) inhibitors as anticancer drugs.

Akt1 (protein kinase B) has become a major focus of attention due to its significant functionality in a variety of cellular processes and the inhibition of Akt1 could lead to a decrease in tumour growth effectively in cancer cells. In the present work, we discovered a set of novel Akt1 inhibitors by using multiple computational techniques, i.e. pharmacophore-based virtual screening, molecular docking, binding free energy calculations, and ADME properties. A five-point pharmacophore hypothesis was implemented and validated with AADRR38. The obtained R2 and Q2 values are in the acceptable region with the values of 0.90 and 0.64, respectively. The generated pharmacophore model was employed for virtual screening to find out the potential Akt1 inhibitors. Further, the selected hits were subjected to molecular docking, binding free energy analysis, and refined using ADME properties. Also, we designed a series of 6-methoxybenzo[b]oxazole analogues by comprising the structural characteristics of the hits acquired from the database. Molecules D1-D10 were found to have strong binding interactions and higher binding free energy values. In addition, Molecular dynamic simulation was performed to understand the conformational changes of protein-ligand complex.

Discovery of novel Akt1 inhibitors by an ensemble-based virtual screening method, molecular dynamics simulation, and in vitro biological activity testing.

Akt1, as an important member of the Akt family, plays a controlled role in cancer cell growth and survival. Inhibition of Akt1 activity can promote cancer cell apoptosis and inhibit tumor growth. Therefore, in this investigation, a multilayer virtual screening approach, including receptor-ligand interaction-based pharmacophore, 3D-QSAR, molecular docking, and deep learning methods, was utilized to construct a virtual screening platform for Akt1 inhibitors. 17 representative compounds with different scaffolds were identified as potential Akt1 inhibitors from three databases. Among these 17 compounds, the Hit9 exhibited the best inhibitory activity against Akt1 with inhibition rate of 33.08% at concentration of 1 µM. The molecular dynamics simulations revealed that Hit9 and Akt1 could form a compact and stable complex. Moreover, Hit9 interacted with some key residues by hydrophobic, electrostatic, and hydrogen bonding interactions and induced substantial conformation changes in the hinge region of the Akt1 active site. The average binding free energies for the Akt1-CQU, Akt1-Ipatasertib, and Akt1-Hit9 systems were - 34.44, - 63.37, and - 39.14 kJ mol-1, respectively. In summary, the results obtained in this investigation suggested that Hit9 with novel scaffold may be a promising lead compound for developing new Akt1 inhibitor for treatment of various cancers with Akt1 overexpressed.

Benchmarking of BMDC assay and related QSAR study for identifying sensitizing chemicals.

The Bone-Marrow derived Dendritic Cell (BMDC) test is a promising assay for identifying sensitizing chemicals based on the 3Rs (Replace, Reduce, Refine) principle. This study expanded the BMDC benchmarking to various in vitro, in chemico, and in silico assays targeting different key events (KE) in the skin sensitization pathway, using common substances datasets. Additionally, a Quantitative Structure-Activity Relationship (QSAR) model was developed to predict the BMDC test outcomes for sensitizing or non-sensitizing chemicals. The modeling workflow involved ISIDA (In Silico Design and Data Analysis) molecular fragment descriptors and the SVM (Support Vector Machine) machine-learning method. The BMDC model's performance was at least comparable to that of all ECVAM-validated models regardless of the KE considered. Compared with other tests targeting KE3, related to dendritic cell activation, BMDC assay was shown to have higher balanced accuracy and sensitivity concerning both the Local Lymph Node Assay (LLNA) and human labels, providing additional evidence for its reliability. The consensus QSAR model exhibits promising results, correlating well with observed sensitization potential. Integrated into a publicly available web service, the BMDC-based QSAR model may serve as a cost-effective and rapid alternative to lab experiments, providing preliminary screening for sensitization potential, compound prioritization, optimization and risk assessment.

Merging Counter-Propagation and Back-Propagation Algorithms: Overcoming the Limitations of Counter-Propagation Neural Network Models.

Artificial neural networks (ANNs) are nowadays applied as the most efficient methods in the majority of machine learning approaches, including data-driven modeling for assessment of the toxicity of chemicals. We developed a combined neural network methodology that can be used in the scope of new approach methodologies (NAMs) assessing chemical or drug toxicity. Here, we present QSAR models for predicting the physical and biochemical properties of molecules of three different datasets: aqueous solubility, acute fish toxicity toward fat head minnow, and bio-concentration factors. A novel neural network modeling method is developed by combining two neural network algorithms, namely, the counter-propagation modeling strategy (CP-ANN) with the back-propagation-of-errors algorithm (BPE-ANN). The advantage is a short training time, robustness, and good interpretability through the initial CP-ANN part, while the extension with BPE-ANN improves the precision of predictions in the range between minimal and maximal property values of the training data, regardless of the number of neurons in both neural networks, either CP-ANN or BPE-ANN.

Multi-process regulation of novel brominated flame retardants: Environmentally friendly substitute design, screening and environmental risk regulation.

Novel brominated flame retardants (NBFRs), one of the most widely used synthetic flame-retardant materials, have been considered as a new group of pollutants that potentially affect human health. To overcome the adverse effects of NBFRs, a systematic approach for molecular design, screening, and performance evaluation was developed to generate environmentally friendly NBFR derivatives with unaltered functionality. In the present study, the features of NBFRs (long-distance migration, biotoxicity, bioenrichment, and environmental persistence) were determined and characterized by the multifactor comprehensive characterization method with equal weight addition, and the similarity index analysis (CoMSIA) model was constructed. Based on the three-dimensional equipotential diagram of the target molecule 2-ethylhexyl tetrabromobenzoic acid (TBB), 23 TBB derivatives were designed. Of these, 22 derivatives with decreased environmental impact and unaltered functional properties (i.e., flame retardancy and stability) were selected using 3D-QSAR models and density functional theory methods. The health risks of these derivatives to humans were assessed by toxicokinetic analysis; the results narrowed down the number of candidates to three (Derivative-7, Derivative-10, and Derivative-15). The environmental impact of these candidates was further evaluated and regulated in the real-world environment by using molecular dynamics simulation assisted by the Taguchi experimental design method. The relationship between the binding effects and the nonbonding interaction resultant force (TBB derivatives-receptor proteins) was also studied, and it was found that the larger the modulus of the binding force, the stronger the binding ability of the two. This finding indicated that the environmental impact of the designed NBFR derivatives was decreased. The present study aimed to provide a new idea and method for designing NBFR substitutes and to provide theoretical support for restraining the potential environmental risks of NBFRs.

Screening of Oral Potential Angiotensin-Converting Enzyme Inhibitory Peptides from Zizyphus jujuba Proteins Based on Gastrointestinal Digestion In Vivo.

Plant proteins are a good source of active peptides, which can exert physiological effects on the body. Predicting the possible activity of plant proteins and obtaining active peptides with oral potential are challenging. In this study, the potential activity of peptides from Zizyphus jujuba proteins after in silico simulated gastrointestinal digestion was predicted using the BIOPEP-UWM™ database. The ACE-inhibitory activity needs to be further investigated. The actual peptides in mouse intestines after the oral administration of Zizyphus jujuba protein were collected and analyzed, 113 Zizyphus jujuba peptides were identified, and 3D-QSAR models of the ACE-inhibitory activity were created and validated using a training set (34 peptides) and a test set (12 peptides). Three peptides, RLPHV, TVKPGL and KALVAP, were screened using the 3D-QSAR model and were found to bind to the active sites of the ACE enzyme, and their IC50 values were determined. Their values were 6.01, 3.81, and 17.06 µM, respectively. The in vitro digestion stabilities of the RLPHV, TVKPGL, and KALVAP peptides were 82%, 90%, and 78%. This article provides an integrated method for studying bioactive peptides derived from plant proteins.

Formation and Cytotoxicity of Halophenylacetamides: A New Group of Nitrogenous Aromatic Halogenated Disinfection Byproducts in Drinking Water.

Nitrogenous aromatic halogenated disinfection byproducts (DBPs) in drinking water have received considerable attention recently owing to their relatively high toxicity. In this study, a new group of nitrogenous aromatic halogenated disinfection byproducts, halophenylacetamides (HPAcAms), were successfully identified for the first time in both the laboratory experiments and realistic drinking water. The formation mechanism of HPAcAms during chlorination of phenylalanine in the presence of Br- and I-, occurrence frequencies, and concentrations in authentic drinking water were investigated, and a quantitative structure-activity relationship (QSAR) model was developed based on the acquired cytotoxicity data. The results demonstrated that HPAcAms could be formed from phenylalanine in chlorination via electrophilic substitution, decarboxylation, hydrochloric acid elimination, and hydrolysis. The HPAcAm yields from phenylalanine were significantly affected by contact time, pH, chlorine dose, and temperature. Nine HPAcAms with concentrations in the range of 0.02-1.54 ng/L were detected in authentic drinking water samples. Most tested HPAcAms showed significantly higher cytotoxicity compared with dichloroacetamide, which is the most abundant aliphatic haloacetamide DBP. The QSAR model demonstrated that the cellular uptake efficiency and the polarized distributions of electrons of HPAcAms play essential roles in their cytotoxicity mechanisms.

QSAR model and mechanism research on color removal efficiency of dying wastewater by FeCl3 coagulation.

Coagulation is the most widely used method in the treatment of printing and dying wastewater. To better understand the relationship between the coagulation effect and dye molecular structures, quantitative structure activity relationship (QSAR) analyses were performed to elucidate the factors affecting the coagulation in ferric chloride (FeCl3) coagulation process. First, the coagulation experiments on 38 dye molecules were conducted to determine their color removal rates (Rexp) by FeCl3 under different pH conditions (i.e., pH = 4 and 10). The results showed that the average Rexp of dyes were 41.36% ± 2.40% at pH value of 4 and 55.70% ± 2.83% at pH value of 10. Subsequently, a multiple linear regression (MLR) method was used to construct QSAR models based on Rexp and 42 molecular parameters calculated by Gaussian 09, Materials Studio 7.0 and Multiwfn. The developed QSAR models exhibited excellent stability, reliability, and robustness with values of R2 = 0.7950, 0.8170, Q2INT = 0.6401, 0.7382, Q2EXT = 0.5168, 0.5441, at pH values of 4 and 10, respectively. Through analysis of quantum parameter values, electrostatic adsorption and hydrogen bonding adsorption were primarily responsible for the coagulation process. Therefore, this study could be useful in providing critical information for evaluating the removal efficiency and a feasible way to predict the removal rate of dyes by FeCl3 when no coagulation experiments were conducted.

Application of QSAR for investigation on coagulation mechanisms of textile wastewater.

Coagulation is an effective preliminary treatment process for textile wastewater. In order to evaluate the effectiveness of the coagulation process, we performed quantitative structure activity relationship (QSAR) analyses with total organic carbon (TOC) removal rates (Rexp) as an index by three different coagulants (AlCl3, FeCl3, and MgCl2). The experimental results showed that the average Rexp of the three coagulants was 39.12% ± 2.60%, 51.60% ± 2.88%, and 49.95% ± 3.17%. Subsequently, 42 molecular descriptors of dye molecules were calculated by quantitative calculation softwares Gaussian 09, Material Studio 7.0, and Multiwfn 3.7, and then QSAR models were constructed by a multiple linear regression (MLR) method for the three coagulation systems. The developed QSAR models demonstrated excellent stability, robustness, and predictability with values of R2 = 0.7677, 0.8015, and 0.7035, Q2INT = 0.6067, 0.7026, and 0.5898, Q2EXT = 0.5505, 0.5076, and 0.5697, respectively. Based on the analysis of quantum parameters, the coagulation mechanism for AlCl3, FeCl3 is primarily electrostatic adsorption as well as hydrogen bonding, while MgCl2 coagulates dyes mainly by electrostatic adsorption. This study provides an assessment of the removal rules and a feasible method for predicting dye removal rates in AlCl3, FeCl3, and MgCl2 coagulation process.

4-Phthalimidobenzenesulfonamide Derivatives as Acetylcholinesterase and Butyrylcholinesterase Inhibitors: DFTs, 3D-QSAR, ADMET, and Molecular Dynamics Simulation.

INTRODUCTION: Alzheimer's disease is a form of dementia which affects majority of the people. It is characterized by memory loss and other cognitive function disabilities and is one of the most challenging neurodegenerative disorders to treat because of its progressive nature. The disease affects millions of people all around the world, and the number of those affected is expanding every day. In the previous study, the 4-phthalimidobenzenesulfonamide derivatives were synthesized as AChE and BChE inhibitors, and here, we were aiming to further reporting in silico studies of these compounds for efficient drug discovery process and to find out the potential lead compounds. METHODS: In silico characterization included density functional theory (DFT) studies, 3D-QSAR, ADMET properties, molecular docking, and molecular dynamic simulations. The geometries of all derivatives were optimized using B3LYP method and 6-311G basis set. RESULTS: The findings of the current study revealed that 4-phthalimidobenzenesulfonamide derivatives exhibited a reactive electronic property which is essential for anticholinesterase activity. Moreover, optimized structures were subjected to molecular docking studies with targeted protein. The compounds 2c and 2g showed excellent binding score of -37.44 and -33.67 kJ/mol with BChE and AChE, respectively, and exhibited strong binding affinity. The potent derivatives produced stable complex with amino acid residues of active pocket of both BChE and AChE. The stability of protein-ligand complexes was determined by molecular dynamic simulation studies, and results were found in correlation with molecular docking findings. CONCLUSION: Findings of the current study suggested that these derivatives are potent inhibitors of cholinesterase enzyme.

Fast Identification of Adverse Drug Reactions (ADRs) of Digestive and Nervous Systems of Organic Drugs by In Silico Models.

This study aimed to discover concurrences of adverse drug reactions (ADRs) and derive models of the most frequent items of ADRs based on the SIDER database, which included 1430 marketed drugs and 5868 ADRs. First, common ADRs of organic drugs were manually reclassified according to side effects in the human system and followed by an association rule analysis, which found ADRs of digestive and nervous systems often occurred at the same time with a good association rule. Then, three algorithms, linear discriminant analysis (LDA), support vector machine (SVM) and deep learning, were used to derive models of ADRs of digestive and nervous systems based on 497 organic monomer drugs and to identify key structural features in defining these ADRs. The statistical results indicated that these kinds of QSAR models were good tools for screening ADRs of digestive and nervous systems, which gave the ROC AUC values of 81.5%, 98.9%, 91.5%, 69.5%, 78.4% and 78.8%, respectively. Then, these models were applied to investigate ADRs of 1536 organic compounds with four phase and zero rule-of-five (RO5) violations from the ChEMBL database. Based on the consensus ADRs' predictions of models, 58.1% and 42.6% of compounds were predicted to cause these two ADRs, respectively, indicating the significance of initial assessment of ADRs in early drug discovery.

Identifying Novel ATX Inhibitors via Combinatory Virtual Screening Using Crystallography-Derived Pharmacophore Modelling, Docking Study, and QSAR Analysis.

Autotaxin (ATX) is considered as an interesting drug target for the therapy of several diseases. The goal of the research was to detect new ATX inhibitors which have novel scaffolds by using virtual screening. First, based on two diverse receptor-ligand complexes, 14 pharmacophore models were developed, and the 14 models were verified through a big test database. Those pharmacophore models were utilized to accomplish virtual screening. Next, for the purpose of predicting the probable binding poses of compounds and then carrying out further virtual screening, docking-based virtual screening was performed. Moreover, an excellent 3D QSAR model was established, and 3D QSAR-based virtual screening was applied for predicting the activity values of compounds which got through the above two-round screenings. A correlation coefficient r2, which equals 0.988, was supplied by the 3D QSAR model for the training set, and the correlation coefficient r2 equaling 0.808 for the test set means that the developed 3D QSAR model is an excellent model. After the filtering was done by the combinatory virtual screening, which is based on the pharmacophore modelling, docking study, and 3D QSAR modelling, we chose nine potent inhibitors with novel scaffolds finally. Furthermore, two potent compounds have been particularly discussed.

Cinnamic Derivatives as Antitubercular Agents: Characterization by Quantitative Structure-Activity Relationship Studies.

Tuberculosis, caused by Mycobacterium tuberculosis (Mtb), remains one of the top ten causes of death worldwide and the main cause of mortality from a single infectious agent. The upsurge of multi- and extensively-drug resistant tuberculosis cases calls for an urgent need to develop new and more effective antitubercular drugs. As the cinnamoyl scaffold is a privileged and important pharmacophore in medicinal chemistry, some studies were conducted to find novel cinnamic acid derivatives (CAD) potentially active against tuberculosis. In this context, we have engaged in the setting up of a quantitative structure-activity relationships (QSAR) strategy to: (i) derive through multiple linear regression analysis a statistically significant model to describe the antitubercular activity of CAD towards wild-type Mtb; and (ii) identify the most relevant properties with an impact on the antitubercular behavior of those derivatives. The best-found model involved only geometrical and electronic CAD related properties and was successfully challenged through strict internal and external validation procedures. The physicochemical information encoded by the identified descriptors can be used to propose specific structural modifications to design better CAD antitubercular compounds.

Design, synthesis and biological evaluation of amide-pyridine derivatives as novel dual-target (SE, CYP51) antifungal inhibitors.

Based on the analysis of the squalene cyclooxygenase (SE) and 14α-demethylase (CYP51) inhibitors pharmacophore feature and the dual-target active sites, a series of compounds with amide-pyridine scaffolds have been designed and synthesized to treat the increasing incidence of drug-resistant fungal infections. In vitro evaluation showed that these compounds have a certain degree of antifungal activity. The most potent compounds 11a, 11b with MIC values in the range of 0.125-2 µg/ml had a broad-spectrum antifungal activity and exhibited excellent inhibitory activity against drug-resistant pathogenic fungi. Preliminary mechanism studies revealed that the compound 11b might play an antifungal role by inhibiting the activity of SE and CYP51. Notably compounds did not show the genotoxicity through plasmid binding assay. Finally, this study of molecular docking, ADME/T prediction and the construction of 3D QSAR model were performed. These results can point out the direction for further optimization of the lead compound.

Virtual design of novel Plasmodium falciparum cysteine protease falcipain-2 hybrid lactone-chalcone and isatin-chalcone inhibitors probing the S2 active site pocket.

We report computer-aided design of new lactone-chalcone and isatin-chalcone (HLCIC) inhibitors of the falcipain-2 (PfFP-2). 3D models of 15 FP-2:HLCIC1-15 complexes with known observed activity (IC50exp) were prepared to establish a quantitative structure-activity (QSAR) model and linear correlation between relative Gibbs free energy of enzyme:inhibitor complex formation (ΔΔGcom) and IC50exp: pIC50exp = -0.0236 × ΔΔGcom+5.082(#); R2 = 0.93. A 3D pharmacophore model (PH4) derived from the QSAR directed our effort to design novel HLCIC analogues. During the design, an initial virtual library of 2621440 HLCIC was focused down to 18288 drug-like compounds and finally, PH4 screened to identify 81 promising compounds. Thirty-three others were added from an intuitive substitution approach intended to fill better the enzyme S2 pocket. One hundred and fourteen theoretical IC50 (IC50pre) values were predicted by means of (#) and their pharmacokinetics (ADME) profiles. More than 30 putative HLCICs display IC50pre 100 times superior to that of the published most active training set inhibitor HLCIC1.

In silico study for inhibiting thyroid hormone sulfotransferase activity by halogenated phenolic chemicals.

Thyroid hormones (THs) are essential to proper growth and development of human bodies. Inhibiting the sulfation metabolism of THs has been demonstrated to be an important way for some environmental pollutants, such as halogenated phenolic compounds, to interfere THs homeostasis, thereby causing health problems. However, the important property characteristics that govern the sulfation inhibition of these chemicals are not well understood, and the experimental data on inhibition potential is limited. In this work, an in silico approach was developed to investigate the structure-activity relationship for their sulfotransferases (SULTs) inhibition. A series of quantum chemical descriptors that quantify the electronic and energy properties of 22 halogenated phenolic compounds have been calculated to establish a predictive model and analyzed their corresponding contributions to SULTs inhibition. Density functional theory (DFT) B3LYP/6-31G** has been employed to optimize molecular geometries to obtain a total of 15 descriptors for every compound. The implementation of linear regression shows three descriptors that represent molecular mass, positive charges on hydrogen atoms, and energy of frontier orbitals strongly correlate with SULTs inhibition potential. This indicates molecular size, hydrogen-bond strength, and nucleophilic-electrophilic reactivity may play important roles in SULTs inhibition. The derived regression model has good statistical performance (r2 = 0.84, rms = 0.35), and different validation strategies indicate it can serve as an efficient predictive tool for other chemicals in application domain but with no experimental data, consequently assisting in their THs sulfation inhibition and health risk assessment.

An integrated approach with new strategies for QSAR models and lead optimization.

BACKGROUND: Computational drug design approaches are important for shortening the time and reducing the cost for drug discovery and development. Among these methods, molecular docking and quantitative structure activity relationship (QSAR) play key roles for lead discovery and optimization. Here, we propose an integrated approach with core strategies to identify the protein-ligand hot spots for QSAR models and lead optimization. These core strategies are: 1) to generate both residue-based and atom-based interactions as the features; 2) to identify compound common and specific skeletons; and 3) to infer consensus features for QSAR models. RESULTS: We evaluated our methods and new strategies on building QSAR models of human acetylcholinesterase (huAChE). The leave-one-out cross validation values q 2 and r 2 of our huAChE QSAR model are 0.82 and 0.78, respectively. The experimental results show that the selected features (resides/atoms) are important for enzymatic functions and stabling the protein structure by forming key interactions (e.g., stack forces and hydrogen bonds) between huAChE and its inhibitors. Finally, we applied our methods to arthrobacter globiformis histamine oxidase (AGHO) which is correlated to heart failure and diabetic. CONCLUSIONS: Based on our AGHO QSAR model, we identified a new substrate verified by bioassay experiments for AGHO. These results show that our methods and new strategies can yield stable and high accuracy QSAR models. We believe that our methods and strategies are useful for discovering new leads and guiding lead optimization in drug discovery.

Design, synthesis and evaluation of 3-arylidene azetidin-2-ones as potential antifungal agents against Alternaria solani Sorauer.

A new concise and facile method was explored to synthesize a collection of new 3-arylidene azetidin-2-ones, which could be regarded as the derivatives of the hybrid scaffold of bioactive natural cinnamamide and heterocycle azetidi-2-one. The structures of the synthesized compounds were characterized by 1H, 13C NMR, and MS; and their antifungal activity were evaluated against Alternaria solani Sorauer. These antifungal data were subjected to a quantitative structure-activity relationship (QSAR) analysis using Codessa software on the basis of the results from B3LYP/6-31G(d,p) quantum calculations. The best regressive model revealed that potentially more active compounds should have low dipole moments and QC-min (minimal net atomic charge for a C atom), and high QO-max (maximal net atomic charge for an O atom) and QN-min (minimal net atomic charge for an N atom). The most potent compound 7k could lead to intracellular accumulation of reactive oxygen species, dissipation of mitochondrial transmembrane potential, and an autophagy-like cell death process in A. solani Sorauer. Taken together, these results laid the foundation for further design of improved crop-protection agents based on this hybrid scaffold.