SAR and QSAR in Environmental Research最新文献

{"title":"Ligand and structure-based discovery of phosphorus-containing compounds as potential metalloproteinase inhibitors.","authors":"Y Cañizares-Carmenate, Y Perera-Sardiña, Y Marrero-Ponce, R Díaz-Amador, F Torrens, J A Castillo-Garit","doi":"10.1080/1062936X.2024.2314103","DOIUrl":"10.1080/1062936X.2024.2314103","url":null,"abstract":"<p><p>In this study, a methodology is proposed, combining ligand- and structure-based virtual screening tools, for the identification of phosphorus-containing compounds as inhibitors of zinc metalloproteases. First, we use Dragon molecular descriptors to develop a Linear Discriminant Analysis classification model, which is widely validated according to the OECD principles. This model is simple, robust, stable and has good discriminating power. Furthermore, it has a defined applicability domain and it is used for virtual screening of the DrugBank database. Second, docking experiments are carried out on the identified compounds that showed good binding energies to the enzyme thermolysin. Considering the potential toxicity of phosphorus-containing compounds, their toxicological profile is evaluated according to Protox II. Of the five molecules evaluated, two show carcinogenic and mutagenic potential at small LD₅₀, not recommended as drugs, while three of them are classified as non-toxic, and could constitute a starting point for the development of new vasoactive metalloprotease inhibitor drugs. According to molecular dynamics simulation, two of them show stable interactions with the active site maintaining coordination with the metal. A high agreement is evident between QSAR, docking and molecular dynamics results, demonstrating the potentialities of the combination of these tools.</p>","PeriodicalId":21446,"journal":{"name":"SAR and QSAR in Environmental Research","volume":" ","pages":"219-240"},"PeriodicalIF":3.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139913425","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"First report on pesticide sub-chronic and chronic toxicities against dogs using QSAR and chemical read-across.","authors":"A Kumar, P K Ojha, K Roy","doi":"10.1080/1062936X.2024.2320143","DOIUrl":"10.1080/1062936X.2024.2320143","url":null,"abstract":"<p><p>Excessive use of chemicals is the outcome of the industrialization of agricultural sectors which leads to disturbance of ecological balance. Various agrochemicals are widely used in agricultural fields, urban green areas, and to protect from various pest-associated diseases. Due to their long-term health and environmental hazards, chronic toxicity assessment is crucial. Since in vivo and in vitro toxicity assessments are costly, lengthy, and require a large number of animal experiments, in silico toxicity approaches are better alternatives to save time, cost, and animal experimentation. We have developed the first regression-based 2D-QSAR models using different sub-chronic and chronic toxicity data of pesticides against dogs employing 2D descriptors. From the statistical results (<math><mi>n</mi><mrow><mrow><mi>train</mi></mrow></mrow><mo>=</mo><mn>53</mn><mo>-</mo><mn>62</mn><mo>,</mo><mrow><mrow><mi> </mi></mrow></mrow><mrow><msup><mi>r</mi><mn>2</mn></msup></mrow></math> = 0.614 to 0.754, <math><msubsup><mi>Q</mi><mrow><mrow><mrow><mi>L</mi><mi>O</mi><mi>O</mi></mrow></mrow></mrow><mn>2</mn></msubsup></math> = 0.501 to 0.703 and <math><mrow><mrow><mi> </mi></mrow></mrow><msubsup><mi>Q</mi><mrow><mfenced><mrow><mrow><mrow><mi>F</mi></mrow></mrow><mn>1</mn></mrow></mfenced></mrow><mn>2</mn></msubsup></math> = 0.531 to 0.718, <math><msubsup><mi>Q</mi><mrow><mfenced><mrow><mrow><mrow><mi>F</mi></mrow></mrow><mn>2</mn></mrow></mfenced></mrow><mn>2</mn></msubsup><mo>=</mo><mn>0.523</mn><mo>-</mo><mn>0.713</mn></math>), it was concluded that the models are robust, reliable, interpretable, and predictive. Similarity-based read-across algorithm was also used to improve the predictivity (<math><msubsup><mi>Q</mi><mrow><mfenced><mrow><mrow><mrow><mi>F</mi></mrow></mrow><mn>1</mn></mrow></mfenced></mrow><mn>2</mn></msubsup><mo>=</mo><mn>0.595</mn><mo>-</mo><mn>0.813</mn><mo>,</mo><msubsup><mi>Q</mi><mrow><mfenced><mrow><mrow><mrow><mi>F</mi></mrow></mrow><mn>2</mn></mrow></mfenced></mrow><mn>2</mn></msubsup><mo>=</mo><mn>0.573</mn><mo>-</mo><mn>0.809</mn></math>) of the models. 5132 chemicals obtained from the CPDat and 1694 pesticides obtained from the PPDB database were also screened using the developed models, and their predictivity and reliability were checked. Thus, these models will be helpful for eco-toxicological data-gap filling, toxicity prediction of untested pesticides, and development of novel, safer & eco-friendly pesticides.</p>","PeriodicalId":21446,"journal":{"name":"SAR and QSAR in Environmental Research","volume":"35 3","pages":"241-263"},"PeriodicalIF":3.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139932725","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring crucial structural attributes of quinolinyl methoxyphenyl sulphonyl-based hydroxamate derivatives as ADAM17 inhibitors through classification-dependent molecular modelling approaches.","authors":"T B Samoi, S Banerjee, B Ghosh, T Jha, N Adhikari","doi":"10.1080/1062936X.2024.2311689","DOIUrl":"10.1080/1062936X.2024.2311689","url":null,"abstract":"<p><p>A Disintegrin and Metalloproteinase 17 (ADAM17), a Zn²⁺-dependent metalloenzyme of the adamalysin family of the metzincin superfamily, is associated with various pathophysiological conditions including rheumatoid arthritis and cancer. However, no specific inhibitors have been marketed yet for ADAM17-related disorders. In this study, 94 quinolinyl methoxyphenyl sulphonyl-based hydroxamates as ADAM17 inhibitors were subjected to classification-based molecular modelling and binding pattern analysis to identify the significant structural attributes contributing to ADAM17 inhibition. The statistically validated classification-based models identified the importance of the P1' substituents such as the quinolinyl methoxyphenyl sulphonyl group of these compounds for occupying the S1' - S3' pocket of the enzyme. The quinolinyl function of these compounds was found to explore stable binding of the P1' substituents at the S1' - S3' pocket whereas the importance of the sulphonyl and the orientation of the P1' moiety also revealed stable binding. Based on the outcomes of the current study, four novel compounds of different classes were designed as promising ADAM17 inhibitors. These findings regarding the crucial structural aspects and binding patterns of ADAM17 inhibitors will aid the design and discovery of novel and effective ADAM17 inhibitors for therapeutic advancements of related diseases.</p>","PeriodicalId":21446,"journal":{"name":"SAR and QSAR in Environmental Research","volume":" ","pages":"157-179"},"PeriodicalIF":3.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139723954","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development of terpenoid repellents against <i>Aedes albopictus</i>: a combined study of biological activity evaluation and computational modelling.","authors":"J Wang, X Feng, W Yuan, J Zhang, S Zhu, L Xu, H Li, J Song, X Rao, S Liao, Z Wang, H Si","doi":"10.1080/1062936X.2024.2306327","DOIUrl":"10.1080/1062936X.2024.2306327","url":null,"abstract":"<p><p>To explore novel terpenoid repellents, 22 candidate terpenoid derivatives were synthesized and tested for their electroantennogram (EAG) responses and repellent activities against <i>Aedes albopictus</i>. The results from the EAG experiments revealed that 5-(2-hydroxypropan-2-yl)-2-methylcyclohex-2-en-1-yl formate (compound 1) induced distinct EAG responses in female <i>Aedes albopictus</i>. At concentrations of 0.1, 1, 10, 100, and 1000 mg/L, the EAG response values for compound 1 were 179.59, 183.99, 190.38, 193.80, and 196.66 mV, demonstrating comparable or superior effectiveness to DEET. Repellent activity analysis indicated significant repellent activity for compound 1, closest to the positive control DEET. The in silico assessment of the ADMET profile of compound 1 indicates that it successfully passed the ADMET evaluation. Molecular docking studies exhibited favourable binding of compound 1 to the active site of the odorant binding protein (OBP) of <i>Aedes albopictus</i>, involving hydrophobic forces and hydrogen bond interactions with residues in the OBP pocket. The QSAR model highlighted the influential role of hydrogen-bonding receptors, positively charged surface area of weighted atoms, polarity parameters of molecules, and maximum nuclear-nuclear repulsion force of carbon-carbon bonds on the relative EAG response values of the tested compounds. This study holds substantial significance for the advancement of new terpenoid repellents.</p>","PeriodicalId":21446,"journal":{"name":"SAR and QSAR in Environmental Research","volume":" ","pages":"71-89"},"PeriodicalIF":3.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139698150","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Docking and other computing tools in drug design against SARS-CoV-2.","authors":"A V Sulimov, I S Ilin, A S Tashchilova, O A Kondakova, D C Kutov, V B Sulimov","doi":"10.1080/1062936X.2024.2306336","DOIUrl":"10.1080/1062936X.2024.2306336","url":null,"abstract":"<p><p>The use of computer simulation methods has become an indispensable component in identifying drugs against the SARS-CoV-2 coronavirus. There is a huge body of literature on application of molecular modelling to predict inhibitors against target proteins of SARS-CoV-2. To keep our review clear and readable, we limited ourselves primarily to works that use computational methods to find inhibitors and test the predicted compounds experimentally either in target protein assays or in cell culture with live SARS-CoV-2. Some works containing results of experimental discovery of corresponding inhibitors without using computer modelling are included as examples of a success. Also, some computational works without experimental confirmations are also included if they attract our attention either by simulation methods or by databases used. This review collects studies that use various molecular modelling methods: docking, molecular dynamics, quantum mechanics, machine learning, and others. Most of these studies are based on docking, and other methods are used mainly for post-processing to select the best compounds among those found through docking. Simulation methods are presented concisely, information is also provided on databases of organic compounds that can be useful for virtual screening, and the review itself is structured in accordance with coronavirus target proteins.</p>","PeriodicalId":21446,"journal":{"name":"SAR and QSAR in Environmental Research","volume":"35 2","pages":"91-136"},"PeriodicalIF":3.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139730355","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction.","authors":"","doi":"10.1080/1062936X.2024.2312758","DOIUrl":"10.1080/1062936X.2024.2312758","url":null,"abstract":"","PeriodicalId":21446,"journal":{"name":"SAR and QSAR in Environmental Research","volume":" ","pages":"181-182"},"PeriodicalIF":3.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139692818","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Steroidal hydrazones as antimicrobial agents: biological evaluation and molecular docking studies.","authors":"M Merlani, N Nadaraia, N Barbakadze, L Amiranashvili, M Kakhabrishvili, A Petrou, T Carević, J Glamočlija, A Geronikaki","doi":"10.1080/1062936X.2024.2309183","DOIUrl":"10.1080/1062936X.2024.2309183","url":null,"abstract":"<p><p>Most of pharmaceutical agents display several or even many biological activities. It is obvious that testing even one compound for thousands of biological activities is a practically not reasonable task. Therefore, computer-aided prediction is the method of choice for the selection of the most promising bioassays for particular compounds. Using PASS Online software, we determined the probable antimicrobial activity of the 31 steroid derivatives. Experimental testing of the antimicrobial activity of the tested compounds by microdilution method confirmed the computational predictions. Furthermore, <i>P. aeruginosa</i> and <i>C. albicans</i> biofilm formation was investigated. Compound 11 showed a biofilm reduction by 42.26% at the MIC of the tested compound. The percentages are lower than ketoconazole, but very close to its activity.</p>","PeriodicalId":21446,"journal":{"name":"SAR and QSAR in Environmental Research","volume":" ","pages":"137-155"},"PeriodicalIF":3.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139681500","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"BC CLC-Pred: a freely available web-application for quantitative and qualitative predictions of substance cytotoxicity in relation to human breast cancer cell lines.","authors":"A A Lagunin, A S Sezganova, E S Muraviova, A V Rudik, D A Filimonov","doi":"10.1080/1062936X.2023.2289050","DOIUrl":"10.1080/1062936X.2023.2289050","url":null,"abstract":"<p><p>In silico prediction of cell line cytotoxicity considerably decreases time and financial costs during drug development of new antineoplastic agents. (Q)SAR models for the prediction of drug-like compound cytotoxicity in relation to nine breast cancer cell lines (T47D, ZR-75-1, MX1, Hs-578T, MCF7-DOX, MCF7, Bcap37, MCF7R, BT-20) were created by GUSAR software based on the data from ChEMBL database (v. 30). The separate datasets related with IC₅₀ and IG₅₀ values were used for the creation of (Q)SAR models for each cell line. Based on leave-one-out and 5F CV procedures, 24 reasonable (Q)SAR models were selected for the creation of a freely available web-application (BC CLC-Pred: https://www.way2drug.com/bc/) to predict substance cytotoxicity in relation to human breast cancer cell lines. The mean accuracies of prediction <i>r</i>², RMSE, Balance Accuracy for the selected (Q)SAR models calculated by 5F CV were 0.599, 0.679 and 0.875, respectively. As a result, BC CLC-Pred provides simultaneous quantitative and qualitative predictions of IC₅₀ and IG₅₀ values for most of the nine breast cancer cell lines, which may be helpful in selecting promising compounds and optimizing lead compounds during the development of new antineoplastic agents against breast cancer.</p>","PeriodicalId":21446,"journal":{"name":"SAR and QSAR in Environmental Research","volume":" ","pages":"1-9"},"PeriodicalIF":3.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138809194","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Descriptor generation from Morgan fingerprint using persistent homology.","authors":"T Ehiro","doi":"10.1080/1062936X.2023.2301327","DOIUrl":"10.1080/1062936X.2023.2301327","url":null,"abstract":"<p><p>In cheminformatics, molecular fingerprints (FPs) are used in various tasks such as regression and classification. However, predictive models often underutilize Morgan FP for regression and related tasks in machine learning. This study introduced descriptors derived from reshaped Morgan FPs using persistent homology for the predictive accuracy improvement. In the solvation free energy (FreeSolv) and water solubility (ESOL) datasets, persistent homology was found to enhance predictive accuracy compared to the use of only Morgan FPs. Notably, using the first-order persistence diagram (PD1) for descriptor generation resulted in more significant improvements than using the zeroth-order persistence diagram (PD0). Combining 4096 bits Morgan FPs with PD1-generated descriptors increased the average coefficient of determination in the Gaussian process regression from 0.597 to 0.667 for FreeSolv and from 0.629 to 0.654 for ESOL. Adjusting the grid size parameter during PD-based descriptor generation is crucial, as finer grids, especially with PD0, generate more descriptors but reduce predictive accuracy. Coarsening the grid or applying principal component analysis (PCA) mitigates overfitting and enhances accuracy. When descriptors were generated from Morgan FPs with randomly shuffled bit positions, coarsening the grid and/or applying PCA achieved similar accuracy improvements as when the persistent homology of the original Morgan FPs was used.</p>","PeriodicalId":21446,"journal":{"name":"SAR and QSAR in Environmental Research","volume":" ","pages":"31-51"},"PeriodicalIF":3.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139484534","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"q-RASTR modelling for prediction of diverse toxic chemicals towards <i>T. pyriformis</i>.","authors":"V Ghosh, A Bhattacharjee, A Kumar, P K Ojha","doi":"10.1080/1062936X.2023.2298452","DOIUrl":"10.1080/1062936X.2023.2298452","url":null,"abstract":"<p><p>A series of diverse organic compounds impose serious detrimental effects on the health of living organisms and the environment. Determination of the structural aspects of compounds that impart toxicity and evaluation of the same is crucial before public usage. The present study aims to determine the structural characteristics of compounds for <i>Tetrahymena pyriformis</i> toxicity using the q-RASTR (Quantitative Read Across Structure-Toxicity Relationship) model. It was developed using RASTR and 2-D descriptors for a dataset of 1792 compounds with defined endpoint (pIGC₅₀) against a model organism, <i>T. pyriformis</i>. For the current study, the whole dataset was divided based on activity/property into the training and test sets, and the q-RASTR model was developed employing six descriptors (three latent variables) having <i>r</i>², <i>Q</i>²_F1 and <i>Q</i>² values of 0.739, 0.767, and 0.735, respectively. The generated model was thoroughly validated using internationally recognized internal and external validation criteria to assess the model's dependability and predictability. It was highlighted that high molecular weight, aromatic hydroxyls, nitrogen, double bonds, and hydrophobicity increase the toxicity of organic compounds. The current study demonstrates the applicability of the RASTR algorithm in QSTR model development for the prediction of toxic chemicals (pIGC₅₀) towards <i>T. pyriformis</i>.</p>","PeriodicalId":21446,"journal":{"name":"SAR and QSAR in Environmental Research","volume":" ","pages":"11-30"},"PeriodicalIF":3.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139404231","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}