SAR and QSAR in Environmental Research最新文献_第10页

HDAC1 PREDICTOR: a simple and transparent application for virtual screening of histone deacetylase 1 inhibitors. HDAC1预测器:一种简单透明的应用程序，用于组蛋白去乙酰化酶1抑制剂的虚拟筛选。

IF 3 3区环境科学与生态学

SAR and QSAR in Environmental Research Pub Date : 2022-12-01 DOI: 10.1080/1062936X.2022.2147996

O V Tinkov, V Y Grigorev, L D Grigoreva, V N Osipov

{"title":"HDAC1 PREDICTOR: a simple and transparent application for virtual screening of histone deacetylase 1 inhibitors.","authors":"O V Tinkov, V Y Grigorev, L D Grigoreva, V N Osipov","doi":"10.1080/1062936X.2022.2147996","DOIUrl":"https://doi.org/10.1080/1062936X.2022.2147996","url":null,"abstract":"Histone deacetylases play an important role in regulating gene expression by modifying histones and changing chromatin conformation. HDAC dysregulation is involved in many diseases, such as cancer, autoimmune and neurodegenerative diseases. Histone deacetylase 1 (HDAC1) inhibitors represent an important class of drugs. Quantitative Structure-Activity Relationship (QSAR) classification models were developed using 2D RDKit molecular descriptors; ECPF4 (Extended Connectivity Fingerprint) circular fingerprints; and the Random Forest, Gradient Boosting, and Support Vector Machine methods. The developed models were integrated into the HDAC1 PREDICTOR application, which is freely available at the link https://ovttiras-hdac1-inhibitors-hdac1-predictor-app-z3mrbr.streamlitapp.com. The HDAC1 PREDICTOR web application allows one to reveal the compounds for which the predicted activity to inhibit HDAC1 is higher than that of the reference Vorinostat compound (IC50 = 11.08 nM). The algorithm implemented in HDAC1 PREDICTOR for determining the contributions of molecular fragments to the inhibitory activity can be used to find the molecule segments that increase or decrease the activity, enabling the researcher to conduct a rational molecular design of new highly active HDAC1 inhibitors. The developed QSAR models and the code for their construction in the Python programming language are freely available on the GitHub platform at https://github.com/ovttiras/HDAC1-inhibitors.","PeriodicalId":21446,"journal":{"name":"SAR and QSAR in Environmental Research","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10794151","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}

引用次数: 0

Synthesis of new benzimidazole derivatives containing 1,3,4-thiadiazole: their in vitro antimicrobial, in silico molecular docking and molecular dynamic simulations studies. 含1,3,4-噻二唑的新型苯并咪唑衍生物的合成:体外抗菌、硅分子对接及分子动力学模拟研究。

IF 3 3区环境科学与生态学

SAR and QSAR in Environmental Research Pub Date : 2022-11-01 DOI: 10.1080/1062936X.2022.2149620

U Acar Çevik, A Işık, A E Evren, Ö Kapusız, Ü D Gül, Y Özkay, Z A Kaplancıklı

{"title":"Synthesis of new benzimidazole derivatives containing 1,3,4-thiadiazole: their in vitro antimicrobial, in silico molecular docking and molecular dynamic simulations studies.","authors":"U Acar Çevik, A Işık, A E Evren, Ö Kapusız, Ü D Gül, Y Özkay, Z A Kaplancıklı","doi":"10.1080/1062936X.2022.2149620","DOIUrl":"https://doi.org/10.1080/1062936X.2022.2149620","url":null,"abstract":"A series of some new benzimidazole-1,3,4-thiadiazoles was synthesized. The structures of target substances were confirmed by using 1H-NMR and 13С-NMR spectroscopy, mass spectrometry and elemental analysis. The synthesized compounds were evaluated for antimicrobial activity against six bacterial strains namely Escherichia coli (ATCC 25922), Klebsiella pneumoniae (ATCC 13883), Pseudomonas aeruginosa (ATCC 27853), Enterococcus faecalis (ATCC 2942), Bacillus subtilis (ATCC 6633), Staphylococcus aureus (ATCC 29213)and four fungal strains namely Candida albicans (ATCC 24433), Candida krusei (ATCC 6258), Candida parapsilosis (ATCC 22019) and Candida glabrata (ATCC 9). Antimicrobial data revealed that compounds 4f and 4i with MIC of < 0.97 µg/mL were found to be most effective against E. coli. Among the studied molecules, compounds 4f and 4i showed the best antifungal activity with MIC value of 1.95 µg/mL. Additionally, docking studies were performed towards the most promising compounds 4f and 4i, in the active site of DNA gyrase revealing strong interactions. A molecular dynamics (MD) simulation analysis was also used to investigate the dynamic nature, binding interaction, and protein-ligand stability.","PeriodicalId":21446,"journal":{"name":"SAR and QSAR in Environmental Research","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40481961","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}

引用次数: 3

Finding inhibitors and deciphering inhibitor-induced conformational plasticity in the Janus kinase via multiscale simulations. 通过多尺度模拟发现抑制剂并破译抑制剂诱导的Janus激酶构象可塑性。

IF 3 3区环境科学与生态学

SAR and QSAR in Environmental Research Pub Date : 2022-11-01 Epub Date: 2022-11-18 DOI: 10.1080/1062936X.2022.2145352

M F Sk, P Kar

{"title":"Finding inhibitors and deciphering inhibitor-induced conformational plasticity in the Janus kinase via multiscale simulations.","authors":"M F Sk, P Kar","doi":"10.1080/1062936X.2022.2145352","DOIUrl":"https://doi.org/10.1080/1062936X.2022.2145352","url":null,"abstract":"The Janus kinase (JAK) is a master regulator of the JAK/STAT pathway. Dysregulation of this signalling cascade causes neuroinflammation and autoimmune disorders. Therefore, JAKs have been characterized as an attractive target for developing anti-inflammatory drugs. Nowadays, designing efficient, effective, and specific targeted therapeutics without being cytotoxic has gained interest. We performed the virtual screening of natural products in combination with pharmacological analyses. Subsequently, we performed molecular dynamics simulations to study the stability of the ligand-bound complexes and ligand-induced inactive conformations. Notably, inactive kinases display remarkable conformational plasticity; however, ligand-induced molecular mechanisms of these conformations are still poorly understood. Herein, we performed a free energy landscape analysis to explore the conformational plasticity of the JAK1 kinase. Leonurine, STOCK1N-68642, STOCK1N-82656, and STOCK1N-85809 bound JAK1 exhibited a smooth transition from an active (αC-in) to a completely inactive conformation (αC-out). Ligand binding induces disorders in the αC-helix. Molecular mechanics Poisson Boltzmann surface area (MM/PBSA) calculation suggested three phytochemicals, namely STOCK1N-68642, Epicatechin, and STOCK1N-98615, have higher binding affinity compared to other ligand molecules. The ligand-induced conformational plasticity revealed by our simulations differs significantly from the available crystal structures, which might help in designing allosteric drugs.","PeriodicalId":21446,"journal":{"name":"SAR and QSAR in Environmental Research","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40696203","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}

引用次数: 1

A quantitative structural analysis of AR-42 derivatives as HDAC1 inhibitors for the identification of promising structural contributors. AR-42衍生物作为HDAC1抑制剂的定量结构分析，以确定有前途的结构贡献者。

IF 3 3区环境科学与生态学

SAR and QSAR in Environmental Research Pub Date : 2022-11-01 Epub Date: 2022-11-22 DOI: 10.1080/1062936X.2022.2145353

R Kundu, S Banerjee, S K Baidya, N Adhikari, T Jha

{"title":"A quantitative structural analysis of AR-42 derivatives as HDAC1 inhibitors for the identification of promising structural contributors.","authors":"R Kundu, S Banerjee, S K Baidya, N Adhikari, T Jha","doi":"10.1080/1062936X.2022.2145353","DOIUrl":"https://doi.org/10.1080/1062936X.2022.2145353","url":null,"abstract":"Alteration and abnormal epigenetic mechanisms can lead to the aberration of normal biological functions and the occurrence of several diseases. The histone deacetylase (HDAC) family of enzymes is one of the prime regulators of epigenetic functions modifying the histone proteins, and thus, regulating epigenetics directly. HDAC1 is one of those HDACs which have important contributions to cellular epigenetics. The abnormality of HDAC is correlated to the occurrence, progression, and poor prognosis in several disease conditions namely neurodegenerative disorders, cancer cell proliferation, metastasis, chemotherapy resistance, and survival in various cancers. Therefore, the progress of potent and effective HDAC1 inhibitors is one of the prime approaches to combat such diseases. In this study, both regression and classification-based molecular modelling studies were conducted on some AR-42 derivatives as HDAC1 inhibitors to elucidate the crucial structural aspects that are responsible for regulating their biological responses. This study revealed that the molecular polarizability, van der Waals volume, the presence of aromatic rings as well as the higher number of hydrogen bond acceptors might affect prominently their inhibitory activity and might be responsible for proper fitting and interactions at the HDAC1 active site to pertain effective inhibition.","PeriodicalId":21446,"journal":{"name":"SAR and QSAR in Environmental Research","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40489241","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}

引用次数: 1

QSAR model to predict K_p,uu,brain with a small dataset, incorporating predicted values of related parameter. 利用小数据集，结合相关参数的预测值，采用QSAR模型预测Kp、uu、brain。

IF 3 3区环境科学与生态学

SAR and QSAR in Environmental Research Pub Date : 2022-11-01 DOI: 10.1080/1062936X.2022.2149619

Y Umemori, K Handa, S Sakamoto, M Kageyama, T Iijima

{"title":"QSAR model to predict Kp,uu,brain with a small dataset, incorporating predicted values of related parameter.","authors":"Y Umemori, K Handa, S Sakamoto, M Kageyama, T Iijima","doi":"10.1080/1062936X.2022.2149619","DOIUrl":"https://doi.org/10.1080/1062936X.2022.2149619","url":null,"abstract":"The unbound brain-to-plasma concentration ratio (Kp,uu,brain) is a parameter that indicates the extent of central nervous system penetration. Pharmaceutical companies build prediction models because many experiments are required to obtain Kp,uu,brain. However, the lack of data hinders the design of an accurate prediction model. To construct a quantitative structure-activity relationship (QSAR) model with a small dataset of Kp,uu,brain, we investigated whether the prediction accuracy could be improved by incorporating software-predicted brain penetration-related parameters (BPrPs) as explanatory variables for pharmacokinetic parameter prediction. We collected 88 compounds with experimental Kp,uu,brain from various official publications. Random forest was used as the machine learning model. First, we developed prediction models using only structural descriptors. Second, we verified the predictive accuracy of each model with the predicted values of BPrPs incorporated in various combinations. Third, the Kp,uu,brain of the in-house compounds was predicted and compared with the experimental values. The prediction accuracy was improved using five-fold cross-validation (RMSE = 0.455, r2 = 0.726) by incorporating BPrPs. Additionally, this model was verified using an external in-house dataset. The result suggested that using BPrPs as explanatory variables improve the prediction accuracy of the Kp,uu,brain QSAR model when the available number of datasets is small.","PeriodicalId":21446,"journal":{"name":"SAR and QSAR in Environmental Research","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40481960","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}

引用次数: 0

Decoding drug resistant mechanism of V32I, I50V and I84V mutations of HIV-1 protease on amprenavir binding by using molecular dynamics simulations and MM-GBSA calculations. 利用分子动力学模拟和MM-GBSA计算解码HIV-1蛋白酶V32I、I50V和I84V突变对安替那韦结合的耐药机制

IF 3 3区环境科学与生态学

SAR and QSAR in Environmental Research Pub Date : 2022-10-01 Epub Date: 2022-11-02 DOI: 10.1080/1062936X.2022.2140708

Y X Yu, W Wang, H B Sun, L L Zhang, L F Wang, Y Y Yin

{"title":"Decoding drug resistant mechanism of V32I, I50V and I84V mutations of HIV-1 protease on amprenavir binding by using molecular dynamics simulations and MM-GBSA calculations.","authors":"Y X Yu, W Wang, H B Sun, L L Zhang, L F Wang, Y Y Yin","doi":"10.1080/1062936X.2022.2140708","DOIUrl":"https://doi.org/10.1080/1062936X.2022.2140708","url":null,"abstract":"Mutations V32I, I50V and I84V in the HIV-1 protease (PR) induce drug resistance towards drug amprenavir (APV). Multiple short molecular dynamics (MSMD) simulations and molecular mechanics generalized Born surface area (MM-GBSA) method were utilized to investigate drug-resistant mechanism of V32I, I50V and I84V towards APV. Dynamic information arising from MSMD simulations suggest that V32I, I50V and I84V highly affect structural flexibility, motion modes and conformational behaviours of two flaps in the PR. Binding free energies calculated by MM-GBSA method suggest that the decrease in binding enthalpy and the increase in binding entropy induced by mutations V32I, I50V and I84V are responsible for drug resistance of the mutated PRs on APV. The energetic contributions of separate residues on binding of APV to the PR show that V32I, I50V and I84V highly disturb the interactions of two flaps with APV and mostly drive the decrease in binding ability of APV to the PR. Thus, the conformational changes of two flaps in the PR caused by V32I, I50V and I84V play key roles in drug resistance of three mutated PR towards APV. This study can provide useful dynamics information for the design of potent inhibitors relieving drug resistance.","PeriodicalId":21446,"journal":{"name":"SAR and QSAR in Environmental Research","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40661111","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}

引用次数: 1

SAR based on self consistent classifier. 基于自洽分类器的SAR。

IF 3 3区环境科学与生态学

SAR and QSAR in Environmental Research Pub Date : 2022-10-01 DOI: 10.1080/1062936X.2022.2139751

L A Stolbov, D A Filimonov, V V Poroikov

{"title":"SAR based on self consistent classifier.","authors":"L A Stolbov, D A Filimonov, V V Poroikov","doi":"10.1080/1062936X.2022.2139751","DOIUrl":"https://doi.org/10.1080/1062936X.2022.2139751","url":null,"abstract":"The accuracy and performance of (Q)SAR models depend significantly on the data used for training. Datasets prepared on the basis of publicly available databases contain structures belonging to different chemical classes and have a highly imbalanced actives/inactives ratio. Currently, hundreds of structural descriptors are used in (Q)SAR studies. The abundance of structural descriptors gives rise to the problem of the constructed (Q)SAR models stability. The methods frequently used for the selection of a small fraction of the 'best' descriptors usually do not have sufficient mathematical justification. We propose a new approach to a self-consistent classifier for SAR analysis in order to overcome these problems. Logistic (SCLC) and extreme (SCEC) extensions of self-consistent regression (SCR) were implemented to enhance the classification capabilities of SCR. The approach was applied to classification models' development for inhibiting activity endpoints in HIV-1-related data and toxicity endpoints with subsequent fivefold cross-validation to estimate the models' performance. Comparison of the proposed SCLC and SCEC models with those developed using the original SCR and support vector machine demonstrated the comparable accuracy. Advantages in feature selection using our approach provide more generalizable (Q)SAR models. In particular, the crucial factors responsible for the observed value are determined unambiguously.","PeriodicalId":21446,"journal":{"name":"SAR and QSAR in Environmental Research","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40700625","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}

引用次数: 0

Hybrid consensus and k-nearest neighbours (kNN) strategies to classify dual BRD4/PLK1 inhibitors. 混合共识和k-最近邻(kNN)策略分类双重BRD4/PLK1抑制剂

IF 3 3区环境科学与生态学

SAR and QSAR in Environmental Research Pub Date : 2022-10-01 Epub Date: 2022-11-04 DOI: 10.1080/1062936X.2022.2139292

H Rezaie, M Asadollahi-Baboli, S K Hassaninejad-Darzi

{"title":"Hybrid consensus and k-nearest neighbours (kNN) strategies to classify dual BRD4/PLK1 inhibitors.","authors":"H Rezaie, M Asadollahi-Baboli, S K Hassaninejad-Darzi","doi":"10.1080/1062936X.2022.2139292","DOIUrl":"https://doi.org/10.1080/1062936X.2022.2139292","url":null,"abstract":"A novel decision-making procedure is proposed here for the first time to identify active/inactive and selective/non-selective dual inhibitors using consensus approaches and pools of k-nearest neighbours (kNN) classifications instead of individual models. Dual BRD4/PLK1 inhibition with adequate selectivity is a potential therapeutic strategy for targeting tumour cells in high-risk patients. We report the unique way to identify both active and selective dual BRD4/PLK1 inhibitors using consensus and kNN strategies together with two sources of receptor-based and ligand-based information which are the ranked binding energies of residues and important molecular features, respectively. The results of consensus approaches were compared with the results of individual kNN models. The chemical space similarity was measured using three different distance functions to increase the reliability. All activity and selectivity classification models were validated using cross-validation and y-randomization tests. The outcomes show that consensus approaches can increase the reliability and accuracy of active/inactive or selective/non-selective detections up to 90%. Consensus approaches also reached more balanced values of sensitivity and specificity compared to the individual kNN models because of the compensation in the integration of diverse sources of information.","PeriodicalId":21446,"journal":{"name":"SAR and QSAR in Environmental Research","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40444237","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}

引用次数: 2

LY3041658/ interleukin-8 complex structure as targets for IL-8 small molecule inhibitors discovery using a combination of in silico methods. LY3041658/白介素-8复合体结构作为IL-8小分子抑制剂的靶点，利用硅相结合的方法发现。

IF 3 3区环境科学与生态学

SAR and QSAR in Environmental Research Pub Date : 2022-10-01 Epub Date: 2022-11-01 DOI: 10.1080/1062936X.2022.2132536

T T N Tran, Q H Tran, Q T Nguyen, M T Le, D T T Trinh, V H Tran, K M Thai

{"title":"LY3041658/ interleukin-8 complex structure as targets for IL-8 small molecule inhibitors discovery using a combination of in silico methods.","authors":"T T N Tran, Q H Tran, Q T Nguyen, M T Le, D T T Trinh, V H Tran, K M Thai","doi":"10.1080/1062936X.2022.2132536","DOIUrl":"https://doi.org/10.1080/1062936X.2022.2132536","url":null,"abstract":"Since interleukin-8 (IL-8/CXCL8) and its receptor, CXCR1 and CXCR2, were known in the early 1990s, biological pathways related to these proteins were proven to have high clinical value in cancer and inﬂammatory/autoimmune conditions treatment. Recently, IL-8 has been identified as biomarker for severe COVID-19 patients and COVID-19 prognosis. Boyles et al. (mAbs 12 (2020), pp. 1831880) have published a high-resolution X-ray crystal structure of the LY3041658 Fab in a complex human CXCL8. They described the ability to bind to IL-8 and the blocking of IL-8/its receptors interaction by the LY3041658 monoclonal antibody. Therefore, the study has been designed to identify potential small molecules inhibiting interleukin-8 by targeting LY3041658/IL-8 complex structure using an in silico approach. A structure‑based pharmacophore and molecular docking models of the protein active site cavity were generated to identify possible candidates, followed by virtual screening with the ZINC database. ADME analysis of hit compounds was also conducted. Molecular dynamics simulations were then performed to survey the behaviour and stability of the ligand-protein complexes. Furthermore, the MM/PBSA technique has been utilized to evaluate the free binding energy. The final data confirmed that one newly obtained compound, ZINC21882765, may serve as the best potential inhibitor for IL-8.","PeriodicalId":21446,"journal":{"name":"SAR and QSAR in Environmental Research","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40460938","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}

引用次数: 1

Predicting mosquito repellents for clothing application from molecular fingerprint-based artificial neural network SAR models. 基于分子指纹的人工神经网络SAR模型预测服装驱蚊剂应用。

IF 3 3区环境科学与生态学

SAR and QSAR in Environmental Research Pub Date : 2022-09-01 DOI: 10.1080/1062936X.2022.2124014

J Devillers, V Sartor, H Devillers

引用次数: 2