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Titaley IA, Ogba OM, Chibwe L, Hoh E, Cheong PHY, Simonich SLM. Automating data analysis for two-dimensional gas chromatography/time-of-flight mass spectrometry non-targeted analysis of comparative samples. J Chromatogr A 2018; 1541:57-62. [PMID: 29448996 PMCID: PMC5909067 DOI: 10.1016/j.chroma.2018.02.016] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2017] [Revised: 02/03/2018] [Accepted: 02/06/2018] [Indexed: 12/19/2022]
Abstract
Non-targeted analysis of environmental samples, using comprehensive two-dimensional gas chromatography coupled with time-of-flight mass spectrometry (GC × GC/ToF-MS), poses significant data analysis challenges due to the large number of possible analytes. Non-targeted data analysis of complex mixtures is prone to human bias and is laborious, particularly for comparative environmental samples such as contaminated soil pre- and post-bioremediation. To address this research bottleneck, we developed OCTpy, a Python™ script that acts as a data reduction filter to automate GC × GC/ToF-MS data analysis from LECO® ChromaTOF® software and facilitates selection of analytes of interest based on peak area comparison between comparative samples. We used data from polycyclic aromatic hydrocarbon (PAH) contaminated soil, pre- and post-bioremediation, to assess the effectiveness of OCTpy in facilitating the selection of analytes that have formed or degraded following treatment. Using datasets from the soil extracts pre- and post-bioremediation, OCTpy selected, on average, 18% of the initial suggested analytes generated by the LECO® ChromaTOF® software Statistical Compare feature. Based on this list, 63-100% of the candidate analytes identified by a highly trained individual were also selected by OCTpy. This process was accomplished in several minutes per sample, whereas manual data analysis took several hours per sample. OCTpy automates the analysis of complex mixtures of comparative samples, reduces the potential for human error during heavy data handling and decreases data analysis time by at least tenfold.
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Affiliation(s)
- Ivan A Titaley
- Department of Chemistry, Oregon State University, Corvallis, OR, 97331, USA
| | - O Maduka Ogba
- Department of Chemistry, Oregon State University, Corvallis, OR, 97331, USA; Department of Chemistry, Pomona College, Claremont, CA, 91711, USA
| | - Leah Chibwe
- Department of Chemistry, Oregon State University, Corvallis, OR, 97331, USA
| | - Eunha Hoh
- Graduate School of Public Health, San Diego State University, San Diego, CA, 92182, USA
| | - Paul H-Y Cheong
- Department of Chemistry, Oregon State University, Corvallis, OR, 97331, USA.
| | - Staci L Massey Simonich
- Department of Chemistry, Oregon State University, Corvallis, OR, 97331, USA; Department of Environmental and Molecular Toxicology, Oregon State University, Corvallis, OR, 97331, USA.
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Moriwaki H, Tian YS, Kawashita N, Takagi T. Mordred: a molecular descriptor calculator. J Cheminform 2018; 10:4. [PMID: 29411163 PMCID: PMC5801138 DOI: 10.1186/s13321-018-0258-y] [Citation(s) in RCA: 473] [Impact Index Per Article: 78.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2017] [Accepted: 01/23/2018] [Indexed: 01/05/2023] Open
Abstract
Molecular descriptors are widely employed to present molecular characteristics in cheminformatics. Various molecular-descriptor-calculation software programs have been developed. However, users of those programs must contend with several issues, including software bugs, insufficient update frequencies, and software licensing constraints. To address these issues, we propose Mordred, a developed descriptor-calculation software application that can calculate more than 1800 two- and three-dimensional descriptors. It is freely available via GitHub. Mordred can be easily installed and used in the command line interface, as a web application, or as a high-flexibility Python package on all major platforms (Windows, Linux, and macOS). Performance benchmark results show that Mordred is at least twice as fast as the well-known PaDEL-Descriptor and it can calculate descriptors for large molecules, which cannot be accomplished by other software. Owing to its good performance, convenience, number of descriptors, and a lax licensing constraint, Mordred is a promising choice of molecular descriptor calculation software that can be utilized for cheminformatics studies, such as those on quantitative structure–property relationships.![]()
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Affiliation(s)
- Hirotomo Moriwaki
- Graduate School of Pharmaceutical Sciences, Osaka University, 1-6 Yamadaoka, Suita City, Osaka, 565-0871, Japan.
| | - Yu-Shi Tian
- Graduate School of Pharmaceutical Sciences, Osaka University, 1-6 Yamadaoka, Suita City, Osaka, 565-0871, Japan
| | - Norihito Kawashita
- Faculty of Sciences and Engineering, Kindai University, 3-4-1 Kowakae, Higashiosaka City, Osaka, 577-8502, Japan
| | - Tatsuya Takagi
- Graduate School of Pharmaceutical Sciences, Osaka University, 1-6 Yamadaoka, Suita City, Osaka, 565-0871, Japan
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Ezzat A, Wu M, Li XL, Kwoh CK. Computational prediction of drug–target interactions using chemogenomic approaches: an empirical survey. Brief Bioinform 2018; 20:1337-1357. [DOI: 10.1093/bib/bby002] [Citation(s) in RCA: 117] [Impact Index Per Article: 19.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2017] [Revised: 12/21/2017] [Indexed: 01/18/2023] Open
Abstract
Abstract
Computational prediction of drug–target interactions (DTIs) has become an essential task in the drug discovery process. It narrows down the search space for interactions by suggesting potential interaction candidates for validation via wet-lab experiments that are well known to be expensive and time-consuming. In this article, we aim to provide a comprehensive overview and empirical evaluation on the computational DTI prediction techniques, to act as a guide and reference for our fellow researchers. Specifically, we first describe the data used in such computational DTI prediction efforts. We then categorize and elaborate the state-of-the-art methods for predicting DTIs. Next, an empirical comparison is performed to demonstrate the prediction performance of some representative methods under different scenarios. We also present interesting findings from our evaluation study, discussing the advantages and disadvantages of each method. Finally, we highlight potential avenues for further enhancement of DTI prediction performance as well as related research directions.
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54
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Phanus-umporn C, Shoombuatong W, Prachayasittikul V, Anuwongcharoen N, Nantasenamat C. Privileged substructures for anti-sickling activity via cheminformatic analysis. RSC Adv 2018; 8:5920-5935. [PMID: 35539618 PMCID: PMC9078244 DOI: 10.1039/c7ra12079f] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2017] [Revised: 02/21/2018] [Accepted: 01/12/2018] [Indexed: 11/21/2022] Open
Abstract
Sickle cell disease (SCD), an autosomal recessive genetic disorder, has been recognized by the World Health Organization (WHO) as a major public health problem as it affects 300 000 individuals worldwide. Complications arising from SCD include anemia, microvascular occlusion, severe pain, stokes, renal dysfunction and infections. A lucrative therapeutic strategy is to employ anti-sickling agents that can disrupt the formation of the HbS polymer. This study therefore employed cheminformatic approaches, encompassing classification structure–activity relationship (CSAR) modeling, to deduce the privileged substructures giving rise to the anti-sickling activity of an investigated set of 115 compounds, followed by substructure analysis. Briefly, the compiled compounds were described by fingerprint descriptors and used in the construction of CSAR models via several machine learning algorithms. The modelability of the data set, as exemplified by the MODI index, was determined to be in the range of 0.70–0.84. The predictive performance was deduced by the accuracy, sensitivity, specificity and Matthews correlation coefficient, which was found to be statistically robust, whereby the former three parameters afforded values in excess of 0.7 while the latter statistical parameter provided a value greater than 0.5. An analysis of the top 20 important substructure descriptors for anti-sickling activity revealed that 10 important features were significant in the differentiation of actives from inactives, as illustrated by aromaticity/conjugation (e.g. SubFPC287, SubFPC171 and SubFPC5), carbonyl groups (e.g. SubFPC137, SubFPC139, SubFPC49 and SubFPC135) and miscellaneous groups (e.g. SubFPC303, SubFPC302 and SubFPC275). Furthermore, an analysis of the structure–activity relationship revealed that the length of alkyl chains, choice of functional moiety and position of substitution on the benzene ring may affect the anti-sickling activity of these compounds. Thus, this knowledge is anticipated to be useful for guiding the design of robust compounds against the gelling activity of HbS, as preliminarily demonstrated in the data-driven compound design presented herein. Cheminformatic approaches (classification structure–activity relationship models based on 12 fingerprint classes) were employed for deducing privileged substructures giving rise to the anti-sickling activity of an investigated set of 115 compounds.![]()
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Affiliation(s)
- Chuleeporn Phanus-umporn
- Center of Data Mining and Biomedical Informatics
- Faculty of Medical Technology
- Mahidol University
- Bangkok 10700
- Thailand
| | - Watshara Shoombuatong
- Center of Data Mining and Biomedical Informatics
- Faculty of Medical Technology
- Mahidol University
- Bangkok 10700
- Thailand
| | - Veda Prachayasittikul
- Center of Data Mining and Biomedical Informatics
- Faculty of Medical Technology
- Mahidol University
- Bangkok 10700
- Thailand
| | - Nuttapat Anuwongcharoen
- Center of Data Mining and Biomedical Informatics
- Faculty of Medical Technology
- Mahidol University
- Bangkok 10700
- Thailand
| | - Chanin Nantasenamat
- Center of Data Mining and Biomedical Informatics
- Faculty of Medical Technology
- Mahidol University
- Bangkok 10700
- Thailand
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55
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Shi C, Borchardt TB. JRgui: A Python Program of Joback and Reid Method. ACS OMEGA 2017; 2:8682-8688. [PMID: 31457399 PMCID: PMC6645593 DOI: 10.1021/acsomega.7b01464] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/01/2017] [Accepted: 11/16/2017] [Indexed: 05/12/2023]
Abstract
Using the modern object-oriented programing language Python (e.g., tkinter and pandas modules) and a chemoinformatics open-source library (RDKit), the classic Joback and Reid group contribution method was revisited and written into a graphical user interface program, JRgui. The underlying algorithm behind the program is explained, herein, with the users being able to operate the program in either a manual or automatic mode. In the manual mode, the users are required to determine the type and occurrence of functional groups in the compound of interest and manually enter into the program. In the automatic mode, both of these parameters can be detected automatically via user input of the compound simplified molecular input line entry specification (SMILES) string. An additional advantage of the automatic mode is that a large number of molecules can be processed simultaneously by parsing their individual SMILES strings into a text file, which is read by the program. The resulting predicted physical properties along with approximately 200 molecular descriptors are saved in a spreadsheet file for subsequent analysis. The program is available for free at https://github.com/curieshicy/JRgui for Windows, Linux, and macOS 64-bit operating systems. It is hoped that the current work may facilitate the creation of other user-friendly programs in the chemoinformatics community using Python.
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56
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Dimitri GM, Lió P. DrugClust: A machine learning approach for drugs side effects prediction. Comput Biol Chem 2017; 68:204-210. [DOI: 10.1016/j.compbiolchem.2017.03.008] [Citation(s) in RCA: 42] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2016] [Revised: 02/11/2017] [Accepted: 03/27/2017] [Indexed: 01/05/2023]
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57
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Dong J, Yao ZJ, Zhu MF, Wang NN, Lu B, Chen AF, Lu AP, Miao H, Zeng WB, Cao DS. ChemSAR: an online pipelining platform for molecular SAR modeling. J Cheminform 2017; 9:27. [PMID: 29086046 PMCID: PMC5418185 DOI: 10.1186/s13321-017-0215-1] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2016] [Accepted: 04/24/2017] [Indexed: 12/31/2022] Open
Abstract
Background In recent years, predictive models based on machine learning techniques have proven to be feasible and effective in drug discovery. However, to develop such a model, researchers usually have to combine multiple tools and undergo several different steps (e.g., RDKit or ChemoPy package for molecular descriptor calculation, ChemAxon Standardizer for structure preprocessing, scikit-learn package for model building, and ggplot2 package for statistical analysis and visualization, etc.). In addition, it may require strong programming skills to accomplish these jobs, which poses severe challenges for users without advanced training in computer programming. Therefore, an online pipelining platform that integrates a number of selected tools is a valuable and efficient solution that can meet the needs of related researchers. Results This work presents a web-based pipelining platform, called ChemSAR, for generating SAR classification models of small molecules. The capabilities of ChemSAR include the validation and standardization of chemical structure representation, the computation of 783 1D/2D molecular descriptors and ten types of widely-used fingerprints for small molecules, the filtering methods for feature selection, the generation of predictive models via a step-by-step job submission process, model interpretation in terms of feature importance and tree visualization, as well as a helpful report generation system. The results can be visualized as high-quality plots and downloaded as local files. Conclusion ChemSAR provides an integrated web-based platform for generating SAR classification models that will benefit cheminformatics and other biomedical users. It is freely available at: http://chemsar.scbdd.com.. ![]() Electronic supplementary material The online version of this article (doi:10.1186/s13321-017-0215-1) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Jie Dong
- Xiangya School of Pharmaceutical Sciences, Central South University, No. 172, Tongzipo Road, Yuelu District, Changsha, People's Republic of China
| | - Zhi-Jiang Yao
- Xiangya School of Pharmaceutical Sciences, Central South University, No. 172, Tongzipo Road, Yuelu District, Changsha, People's Republic of China.,The Third Xiangya Hospital, Central South University, Changsha, People's Republic of China
| | - Min-Feng Zhu
- Xiangya School of Pharmaceutical Sciences, Central South University, No. 172, Tongzipo Road, Yuelu District, Changsha, People's Republic of China.,The Third Xiangya Hospital, Central South University, Changsha, People's Republic of China
| | - Ning-Ning Wang
- Xiangya School of Pharmaceutical Sciences, Central South University, No. 172, Tongzipo Road, Yuelu District, Changsha, People's Republic of China
| | - Ben Lu
- The Third Xiangya Hospital, Central South University, Changsha, People's Republic of China
| | - Alex F Chen
- Xiangya School of Pharmaceutical Sciences, Central South University, No. 172, Tongzipo Road, Yuelu District, Changsha, People's Republic of China.,The Third Xiangya Hospital, Central South University, Changsha, People's Republic of China
| | - Ai-Ping Lu
- Institute for Advancing Translational Medicine in Bone and Joint Diseases, School of Chinese Medicine, Hong Kong Baptist University, Kowloon Tong, Hong Kong SAR, People's Republic of China
| | - Hongyu Miao
- Department of Biostatistics, School of Public Health, University of Texas Health Science Center, Houston, TX, 77030, USA
| | - Wen-Bin Zeng
- Xiangya School of Pharmaceutical Sciences, Central South University, No. 172, Tongzipo Road, Yuelu District, Changsha, People's Republic of China
| | - Dong-Sheng Cao
- Xiangya School of Pharmaceutical Sciences, Central South University, No. 172, Tongzipo Road, Yuelu District, Changsha, People's Republic of China. .,Institute for Advancing Translational Medicine in Bone and Joint Diseases, School of Chinese Medicine, Hong Kong Baptist University, Kowloon Tong, Hong Kong SAR, People's Republic of China.
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58
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Shaikh N, Sharma M, Garg P. Selective Fusion of Heterogeneous Classifiers for Predicting Substrates of Membrane Transporters. J Chem Inf Model 2017; 57:594-607. [PMID: 28228010 DOI: 10.1021/acs.jcim.6b00508] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Membrane transporters play a crucial role in determining fate of administered drugs in a biological system. Early identification of plausible transporters for a drug molecule can provide insights into its therapeutic, pharmacokinetic, and toxicological profiles. In the present study, predictive models for classifying small molecules into substrates and nonsubstrates of various pharmaceutically important membrane transporters were developed using quantitative structure-activity relationship (QSAR) and proteochemometric (PCM) approaches. For this purpose, 4575 substrate interactions for these transporters were collected from the Metabolism and Transport Database (Metrabase) and the literature. The transporters selected for this study include (i) six efflux transporters, viz., breast cancer resistance protein (BCRP/ABCG2), P-glycoprotein (P-gp/MDR1), and multidrug resistance proteins (MRP1, MRP2, MRP3, and MRP4), and (ii) seven influx transporters, viz., organic cation transporter (OCT1/SO22A1), peptide transporter (PEPT1/SO15A1), apical sodium-bile acid transporter (ASBT/NTCP2), and organic anion transporting peptides (OATP1A2/SO1A2, OATP1B/SO1B1, OATP1B3/SO1B3, and OATP2B1/SO2B1). Various types of descriptors and machine learning methods (classifiers) were evaluated for the development of robust predictive models. Additionally, ensemble models were developed by bagging of homogeneous classifiers and selective fusion of heterogeneous classifiers. It was observed that the latter approach improves the accuracy of substrate/nonsubstrate prediction for transporters (average correct classification rate of more than 0.80 for external validation). Moreover, structural fragments important in determining the substrate specificity across the various transporters were identified. To demonstrate these fragments on the query molecule, contour maps were generated. The prediction efficacy of the developed models was illustrated by a good correlation between the reported logBB value of a molecule and its predicted substrate propensity for blood-brain barrier transporters. Conclusively, this comprehensive modeling analysis can be efficiently employed for the prediction of membrane transporters of a drug, thereby providing insights into its pharmacological profile.
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Affiliation(s)
- Naeem Shaikh
- Department of Pharmacoinformatics, National Institute of Pharmaceutical Education and Research (NIPER) , S. A. S. Nagar, Mohali, Punjab-160062, India
| | - Mahesh Sharma
- Department of Pharmacoinformatics, National Institute of Pharmaceutical Education and Research (NIPER) , S. A. S. Nagar, Mohali, Punjab-160062, India
| | - Prabha Garg
- Department of Pharmacoinformatics, National Institute of Pharmaceutical Education and Research (NIPER) , S. A. S. Nagar, Mohali, Punjab-160062, India
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Turku A, Borrel A, Leino TO, Karhu L, Kukkonen JP, Xhaard H. Pharmacophore Model To Discover OX1 and OX2 Orexin Receptor Ligands. J Med Chem 2016; 59:8263-75. [DOI: 10.1021/acs.jmedchem.6b00333] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
Affiliation(s)
- Ainoleena Turku
- Faculty of Pharmacy,
Division of Pharmaceutical Chemistry and Technology, University of Helsinki, P.O. Box 56, FIN-00014 Helsinki, Finland
- Faculty of Veterinary Medicine, Department of Veterinary Biosciences, University of Helsinki, P.O. Box 66, FIN-00014 Helsinki, Finland
| | - Alexandre Borrel
- Faculty of Pharmacy,
Division of Pharmaceutical Chemistry and Technology, University of Helsinki, P.O. Box 56, FIN-00014 Helsinki, Finland
| | - Teppo O. Leino
- Faculty of Pharmacy,
Division of Pharmaceutical Chemistry and Technology, University of Helsinki, P.O. Box 56, FIN-00014 Helsinki, Finland
| | - Lasse Karhu
- Faculty of Pharmacy,
Division of Pharmaceutical Chemistry and Technology, University of Helsinki, P.O. Box 56, FIN-00014 Helsinki, Finland
| | - Jyrki P. Kukkonen
- Faculty of Veterinary Medicine, Department of Veterinary Biosciences, University of Helsinki, P.O. Box 66, FIN-00014 Helsinki, Finland
| | - Henri Xhaard
- Faculty of Pharmacy,
Division of Pharmaceutical Chemistry and Technology, University of Helsinki, P.O. Box 56, FIN-00014 Helsinki, Finland
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60
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Yao ZJ, Dong J, Che YJ, Zhu MF, Wen M, Wang NN, Wang S, Lu AP, Cao DS. TargetNet: a web service for predicting potential drug-target interaction profiling via multi-target SAR models. J Comput Aided Mol Des 2016; 30:413-24. [PMID: 27167132 DOI: 10.1007/s10822-016-9915-2] [Citation(s) in RCA: 214] [Impact Index Per Article: 26.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2016] [Accepted: 05/06/2016] [Indexed: 02/01/2023]
Abstract
Drug-target interactions (DTIs) are central to current drug discovery processes and public health fields. Analyzing the DTI profiling of the drugs helps to infer drug indications, adverse drug reactions, drug-drug interactions, and drug mode of actions. Therefore, it is of high importance to reliably and fast predict DTI profiling of the drugs on a genome-scale level. Here, we develop the TargetNet server, which can make real-time DTI predictions based only on molecular structures, following the spirit of multi-target SAR methodology. Naïve Bayes models together with various molecular fingerprints were employed to construct prediction models. Ensemble learning from these fingerprints was also provided to improve the prediction ability. When the user submits a molecule, the server will predict the activity of the user's molecule across 623 human proteins by the established high quality SAR model, thus generating a DTI profiling that can be used as a feature vector of chemicals for wide applications. The 623 SAR models related to 623 human proteins were strictly evaluated and validated by several model validation strategies, resulting in the AUC scores of 75-100 %. We applied the generated DTI profiling to successfully predict potential targets, toxicity classification, drug-drug interactions, and drug mode of action, which sufficiently demonstrated the wide application value of the potential DTI profiling. The TargetNet webserver is designed based on the Django framework in Python, and is freely accessible at http://targetnet.scbdd.com .
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Affiliation(s)
- Zhi-Jiang Yao
- School of Pharmaceutical Sciences, Central South University, Changsha, 410013, People's Republic of China
- College of Chemistry and Chemical Engineering, Central South University, Changsha, 410083, People's Republic of China
| | - Jie Dong
- School of Pharmaceutical Sciences, Central South University, Changsha, 410013, People's Republic of China
| | - Yu-Jing Che
- School of Mathematics and Statistics, Central South University, Changsha, 410083, People's Republic of China
| | - Min-Feng Zhu
- School of Mathematics and Statistics, Central South University, Changsha, 410083, People's Republic of China
| | - Ming Wen
- College of Chemistry and Chemical Engineering, Central South University, Changsha, 410083, People's Republic of China
| | - Ning-Ning Wang
- School of Pharmaceutical Sciences, Central South University, Changsha, 410013, People's Republic of China
| | - Shan Wang
- College of Chemistry and Chemical Engineering, Central South University, Changsha, 410083, People's Republic of China
| | - Ai-Ping Lu
- Institute of Advancing Translational Medicine in Bone and Joint Diseases, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong, SAR, People's Republic of China
| | - Dong-Sheng Cao
- School of Pharmaceutical Sciences, Central South University, Changsha, 410013, People's Republic of China.
- Institute of Advancing Translational Medicine in Bone and Joint Diseases, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong, SAR, People's Republic of China.
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61
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Dong J, Cao DS, Miao HY, Liu S, Deng BC, Yun YH, Wang NN, Lu AP, Zeng WB, Chen AF. ChemDes: an integrated web-based platform for molecular descriptor and fingerprint computation. J Cheminform 2015; 7:60. [PMID: 26664458 PMCID: PMC4674923 DOI: 10.1186/s13321-015-0109-z] [Citation(s) in RCA: 178] [Impact Index Per Article: 19.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2015] [Accepted: 11/26/2015] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Molecular descriptors and fingerprints have been routinely used in QSAR/SAR analysis, virtual drug screening, compound search/ranking, drug ADME/T prediction and other drug discovery processes. Since the calculation of such quantitative representations of molecules may require substantial computational skills and efforts, several tools have been previously developed to make an attempt to ease the process. However, there are still several hurdles for users to overcome to fully harness the power of these tools. First, most of the tools are distributed as standalone software or packages that require necessary configuration or programming efforts of users. Second, many of the tools can only calculate a subset of molecular descriptors, and the results from multiple tools need to be manually merged to generate a comprehensive set of descriptors. Third, some packages only provide application programming interfaces and are implemented in different computer languages, which pose additional challenges to the integration of these tools. RESULTS A freely available web-based platform, named ChemDes, is developed in this study. It integrates multiple state-of-the-art packages (i.e., Pybel, CDK, RDKit, BlueDesc, Chemopy, PaDEL and jCompoundMapper) for computing molecular descriptors and fingerprints. ChemDes not only provides friendly web interfaces to relieve users from burdensome programming work, but also offers three useful and convenient auxiliary tools for format converting, MOPAC optimization and fingerprint similarity calculation. Currently, ChemDes has the capability of computing 3679 molecular descriptors and 59 types of molecular fingerprints. CONCLUSION ChemDes provides users an integrated and friendly tool to calculate various molecular descriptors and fingerprints. It is freely available at http://www.scbdd.com/chemdes. The source code of the project is also available as a supplementary file. Graphical abstract:An overview of ChemDes. A platform for computing various molecular descriptors and fingerprints.
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Affiliation(s)
- Jie Dong
- School of Pharmaceutical Sciences, Central South University, Changsha, 410013 Hunan People's Republic of China
| | - Dong-Sheng Cao
- School of Pharmaceutical Sciences, Central South University, Changsha, 410013 Hunan People's Republic of China
| | - Hong-Yu Miao
- Department of Biostatistics, School of Public Health, University of Texas Health Science Center at Houston, Houston, USA
| | - Shao Liu
- Xiangya Hospital, Central South University, Changsha, 410008 Hunan People's Republic of China
| | - Bai-Chuan Deng
- College of Chemistry and Chemical Engineering, Central South University, Changsha, 410083 People's Republic of China
| | - Yong-Huan Yun
- College of Chemistry and Chemical Engineering, Central South University, Changsha, 410083 People's Republic of China
| | - Ning-Ning Wang
- School of Pharmaceutical Sciences, Central South University, Changsha, 410013 Hunan People's Republic of China
| | - Ai-Ping Lu
- Institute of Advancing Translational Medicine in Bone & Joint Diseases, School of Chinese Medicine, Hong Kong Baptist University, Kowloon Tong, Hong Kong SAR People's Republic of China
| | - Wen-Bin Zeng
- School of Pharmaceutical Sciences, Central South University, Changsha, 410013 Hunan People's Republic of China
| | - Alex F Chen
- School of Pharmaceutical Sciences, Central South University, Changsha, 410013 Hunan People's Republic of China
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Xiao N, Cao DS, Zhu MF, Xu QS. protr/ProtrWeb: R package and web server for generating various numerical representation schemes of protein sequences. Bioinformatics 2015; 31:1857-9. [DOI: 10.1093/bioinformatics/btv042] [Citation(s) in RCA: 187] [Impact Index Per Article: 20.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2014] [Accepted: 01/18/2015] [Indexed: 11/13/2022] Open
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63
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Medina-Franco JL, Méndez-Lucio O, Martinez-Mayorga K. The Interplay Between Molecular Modeling and Chemoinformatics to Characterize Protein–Ligand and Protein–Protein Interactions Landscapes for Drug Discovery. ADVANCES IN PROTEIN CHEMISTRY AND STRUCTURAL BIOLOGY 2014; 96:1-37. [DOI: 10.1016/bs.apcsb.2014.06.001] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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