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Banerjee A, Roy K. ARKA: a framework of dimensionality reduction for machine-learning classification modeling, risk assessment, and data gap-filling of sparse environmental toxicity data. ENVIRONMENTAL SCIENCE. PROCESSES & IMPACTS 2024; 26:991-1007. [PMID: 38743054 DOI: 10.1039/d4em00173g] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
Due to the lack of experimental toxicity data for environmental chemicals, there arises a need to fill data gaps by in silico approaches. One of the most commonly used in silico approaches for toxicity assessment of small datasets is the Quantitative Structure-Activity Relationship (QSAR), which generates predictive models for the efficient prediction of query compounds. However, the reliability of the predictions from QSARs derived from small datasets is often questionable from a statistical point of view. This is due to the presence of a larger number of descriptors as compared to the number of training compounds, which reduces the degree of freedom of the developed model. To reduce the overall prediction error for a particular QSAR model, we have proposed here the computation of the novel Arithmetic Residuals in K-groups Analysis (ARKA) descriptors. We have reduced the number of modeling descriptors in a supervised manner by partitioning them into K classes (K = 2 here) depending on the higher mean normalized values of the descriptors to a particular response class, thus preventing the loss of chemical information. A scatter plot of the data points using the values of two ARKA descriptors (ARKA_2 vs. ARKA_1) can potentially identify activity cliffs, less confident data points, and less modelable data points. We have used here five representative environmentally relevant endpoints (skin sensitization, earthworm toxicity, milk/plasma partitioning, algal toxicity, and rodent carcinogenicity of hazardous chemicals) with graded responses to which the ARKA framework was applied for classification modeling. On comparing the performance of the models generated using conventional QSAR descriptors and the ARKA descriptors, the prediction quality of the models derived from ARKA descriptors was found, based on multiple graded-data validation metrics-derived decision criteria, much better than the models derived from QSAR descriptors signifying the potential of ARKA descriptors in ecotoxicological classification modeling of small data sets. Additionally, this holds true for the Read-Across approach as well, since the Read-Across predictions using ARKA descriptors supersede the predictions generated from QSAR descriptors. For the ease of users, a Java-based expert system has been developed that computes the ARKA descriptors from the input of QSAR descriptors.
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Affiliation(s)
- Arkaprava Banerjee
- Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata 700 032, India.
| | - Kunal Roy
- Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata 700 032, India.
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Abstract
Screening compounds for potential carcinogenicity is of major importance for prevention of environmentally induced cancers. A large sequence of predictive models, ranging from short-term biological assays (e.g., mutagenicity tests) to theoretical models, has been attempted in this field. Theoretical approaches such as (Q)SAR are highly desirable for identifying carcinogens, since they actively promote the replacement, reduction, and refinement of animal tests. This chapter reports and describes some of the most noted (Q)SAR models based on human expert knowledge and statistical approaches, aiming at predicting the carcinogenicity of chemicals. Additionally, the performance of the selected models has been evaluated, and the results are interpreted in details by applying these predictive models to some pharmaceutical molecules.
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Affiliation(s)
- Azadi Golbamaki
- Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy
| | - Emilio Benfenati
- Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy
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Amin SA, Banerjee S, Singh S, Qureshi IA, Gayen S, Jha T. First structure-activity relationship analysis of SARS-CoV-2 virus main protease (Mpro) inhibitors: an endeavor on COVID-19 drug discovery. Mol Divers 2021; 25:1827-1838. [PMID: 33400085 PMCID: PMC7782049 DOI: 10.1007/s11030-020-10166-3] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2020] [Accepted: 11/28/2020] [Indexed: 11/10/2022]
Abstract
Main protease (Mpro) of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) intervenes in the replication and transcription processes of the virus. Hence, it is a lucrative target for anti-viral drug development. In this study, molecular modeling analyses were performed on the structure activity data of recently reported diverse SARS-CoV-2 Mpro inhibitors to understand the structural requirements for higher inhibitory activity. The classification-based quantitative structure-activity relationship (QSAR) models were generated between SARS-CoV-2 Mpro inhibitory activities and different descriptors. Identification of structural fingerprints to increase or decrease in the inhibitory activity was mapped for possible inclusion/exclusion of these fingerprints in the lead optimization process. Challenges in ADME properties of protease inhibitors were also discussed to overcome the problems of oral bioavailability. Further, depending on the modeling results, we have proposed novel as well as potent SARS-CoV-2 Mpro inhibitors.
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Affiliation(s)
- Sk Abdul Amin
- Natural Science Laboratory, Division of Medicinal and Pharmaceutical Chemistry, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, 700032, India
| | - Suvankar Banerjee
- Natural Science Laboratory, Division of Medicinal and Pharmaceutical Chemistry, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, 700032, India
| | - Samayaditya Singh
- Department of Biotechnology and Bioinformatics, School of Life Sciences, University of Hyderabad, Hyderabad, 500046, Telangana, India
| | - Insaf Ahmed Qureshi
- Department of Biotechnology and Bioinformatics, School of Life Sciences, University of Hyderabad, Hyderabad, 500046, Telangana, India
| | - Shovanlal Gayen
- Laboratory of Drug Design and Discovery, Department of Pharmaceutical Sciences, Dr. Harisingh Gour University, Sagar, 470003, MP, India.
| | - Tarun Jha
- Natural Science Laboratory, Division of Medicinal and Pharmaceutical Chemistry, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, 700032, India.
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Toma C, Manganaro A, Raitano G, Marzo M, Gadaleta D, Baderna D, Roncaglioni A, Kramer N, Benfenati E. QSAR Models for Human Carcinogenicity: An Assessment Based on Oral and Inhalation Slope Factors. Molecules 2020; 26:E127. [PMID: 33383938 PMCID: PMC7796303 DOI: 10.3390/molecules26010127] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2020] [Revised: 12/24/2020] [Accepted: 12/25/2020] [Indexed: 11/21/2022] Open
Abstract
Carcinogenicity is a crucial endpoint for the safety assessment of chemicals and products. During the last few decades, the development of quantitative structure-activity relationship ((Q)SAR) models has gained importance for regulatory use, in combination with in vitro testing or expert-based reasoning. Several classification models can now predict both human and rat carcinogenicity, but there are few models to quantitatively assess carcinogenicity in humans. To our knowledge, slope factor (SF), a parameter describing carcinogenicity potential used especially for human risk assessment of contaminated sites, has never been modeled for both inhalation and oral exposures. In this study, we developed classification and regression models for inhalation and oral SFs using data from the Risk Assessment Information System (RAIS) and different machine learning approaches. The models performed well in classification, with accuracies for the external set of 0.76 and 0.74 for oral and inhalation exposure, respectively, and r2 values of 0.57 and 0.65 in the regression models for oral and inhalation SFs in external validation. These models might therefore support regulators in (de)prioritizing substances for regulatory action and in weighing evidence in the context of chemical safety assessments. Moreover, these models are implemented on the VEGA platform and are now freely downloadable online.
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Affiliation(s)
- Cosimo Toma
- Laboratory of Chemistry and Environmental Toxicology, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156 Milan, Italy; (C.T.); (G.R.); (M.M.); (D.G.); (A.R.)
- Institute for Risk Assessment Sciences (IRAS), Utrecht University, P.O. Box 80177, 3508 TD Utrecht, The Netherlands;
| | | | - Giuseppa Raitano
- Laboratory of Chemistry and Environmental Toxicology, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156 Milan, Italy; (C.T.); (G.R.); (M.M.); (D.G.); (A.R.)
| | - Marco Marzo
- Laboratory of Chemistry and Environmental Toxicology, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156 Milan, Italy; (C.T.); (G.R.); (M.M.); (D.G.); (A.R.)
| | - Domenico Gadaleta
- Laboratory of Chemistry and Environmental Toxicology, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156 Milan, Italy; (C.T.); (G.R.); (M.M.); (D.G.); (A.R.)
| | - Diego Baderna
- Laboratory of Chemistry and Environmental Toxicology, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156 Milan, Italy; (C.T.); (G.R.); (M.M.); (D.G.); (A.R.)
| | - Alessandra Roncaglioni
- Laboratory of Chemistry and Environmental Toxicology, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156 Milan, Italy; (C.T.); (G.R.); (M.M.); (D.G.); (A.R.)
| | - Nynke Kramer
- Institute for Risk Assessment Sciences (IRAS), Utrecht University, P.O. Box 80177, 3508 TD Utrecht, The Netherlands;
| | - Emilio Benfenati
- Laboratory of Chemistry and Environmental Toxicology, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156 Milan, Italy; (C.T.); (G.R.); (M.M.); (D.G.); (A.R.)
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Zhang YM, Wang T, Yang XS. An in vitro and in silico investigation of human pregnane X receptor agonistic activity of poly- and perfluorinated compounds using the heuristic method-best subset and comparative similarity indices analysis. CHEMOSPHERE 2020; 240:124789. [PMID: 31561157 DOI: 10.1016/j.chemosphere.2019.124789] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/28/2019] [Revised: 09/01/2019] [Accepted: 09/05/2019] [Indexed: 06/10/2023]
Abstract
Poly- and perfluorinated compounds (PFCs) may induce potential endocrine-disrupting hormonal effects. However, the molecular mechanism of the toxicology of PFCs remains unclear, and the insufficient information is available on the biological activities of PFCs at present. In this study, the cell-based reporter gene assays were used to determine the agonistic activity of PFCs on the human pregnane X receptor (hPXR). The heuristic method combined with best subset modeling (HM-BSM) based on Dragon descriptors and comparative similarity indices analysis (CoMSIA) were employed to build classical quantitative structure-activity relationship (QSAR) and three-dimensional QSAR models, respectively. The applicability domain (AD) of the classical QSAR model was assessed. Both the HM-BSM and CoMSIA approaches demonstrated good robustness, predictive ability, and mechanistic interpretability. The r2 and leave-one-out cross-validation squared correlated coefficient (q2LOO) values were 0.872 and 0.759 for the HM-BSM, and 0.976 and 0.751 for the CoMSIA model, respectively. The hPXR agonistic activity of the PFCs predicted by the built HM-BSM and CoMSIA agreed well with experimental activity, with root mean square error (RMSE) values of 0.0803 and 0.117, respectively, and external validation squared correlated coefficients (q2EXT) of 0.972 and 0.932, respectively. The hPXR agonistic activity of PFCs was related to their molecular polarizability, charge and atomic mass. Hydrogen bonding and hydrophobic interactions constituted the primary intermolecular forces between PFCs and the hPXR. The developed models were used to screen the PFCs with high hPXR agonistic activity.
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Affiliation(s)
- Yi-Ming Zhang
- School of Basic Medical Sciences, Nanjing Medical University, Nanjing, 211166, China
| | - Tao Wang
- Department of Oral and Maxillofacial Surgery, The Affiliated Stomatological Hospital, Nanjing Medical University, Nanjing, 210029, China
| | - Xu-Shu Yang
- School of Pharmacy, Nanjing Medical University, Nanjing, 211166, China.
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Ojha PK, Kar S, Roy K, Leszczynski J. Toward comprehension of multiple human cells uptake of engineered nano metal oxides: quantitative inter cell line uptake specificity (QICLUS) modeling. Nanotoxicology 2018; 13:14-34. [PMID: 30354872 DOI: 10.1080/17435390.2018.1529836] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
To address the nanomaterial exposure threat, it is imperative to understand how nanomaterials are recognized, internalized, and distributed within diverse cell systems. Targeting of nanomaterials to a specific cell type is generally attained through the modification of the nanoparticle (NP) surface leading to required cellular uptake. The enhanced cellular uptake to normal cells can direct to the higher interaction of NPs with subcellular organelles resulting the provocation of various signaling pathways. The successes of NPs rely on the prospect for the synthesis of functionalized NPs with necessary properties and their enhanced potential for cellular uptake for specific targeting. In the present study, we have modeled the cellular uptake of 109 surface modifiers of metal oxide nanoparticles (MNPs) for three different cell lines: HUVEC (Human endothelial cells), U937 (human macrophage cells), and PaCa2 (cancer cell lines). Along with the quantitative structure-activity relationship (QSAR) models, for the very first time we have developed and performed quantitative inter cell line uptake specificity (QICLUS) modeling to identify the physicochemical properties, as well as majorly structural fragments responsible for cellular uptake differences between two specific cell lines. The present work provides a comprehensive understanding of the cellular uptake of MNPs and the underlying structural parameters controlling the nano-cellular interactions. This phenomenon has also been analyzed from the QSAR and QICLUS models that concluded the functional groups of surface modifiers like amine, anhydride, halogen atoms, nitro group, acids have the dominating roles for the uptake of MNPs into the cell lines. Thus, the developed models may be used for designing of novel surface modifiers of MNPs of desired characteristics for proper cell-NPs interactions, as well as in the context of virtual screening aspect. Moreover, the MNP-cell interactions can give some idea about the toxicity for target-specific drug delivery treatment as higher cellular uptake is required for specific cells to treat the disease and lower uptake to the neighboring cells for lower toxicity.
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Affiliation(s)
- Probir Kumar Ojha
- a Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology , Jadavpur University , Kolkata , India
| | - Supratik Kar
- b Interdisciplinary Nanotoxicity Center, Department of Chemistry, Physics and Atmospheric Sciences , Jackson State University , Jackson , MS , USA
| | - Kunal Roy
- a Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology , Jadavpur University , Kolkata , India
| | - Jerzy Leszczynski
- b Interdisciplinary Nanotoxicity Center, Department of Chemistry, Physics and Atmospheric Sciences , Jackson State University , Jackson , MS , USA
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Li X, Du Z, Wang J, Wu Z, Li W, Liu G, Shen X, Tang Y. In Silico Estimation of Chemical Carcinogenicity with Binary and Ternary Classification Methods. Mol Inform 2015; 34:228-35. [PMID: 27490168 DOI: 10.1002/minf.201400127] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2014] [Accepted: 01/11/2015] [Indexed: 11/07/2022]
Abstract
Carcinogenicity is one of the most concerned properties of chemicals to human health, thus it is important to identify chemical carcinogenicity as early as possible. In this study, 829 diverse compounds with rat carcinogenicity were collected from Carcinogenic Potency Database (CPDB). Using six types of fingerprints to represent the molecules, 30 binary and ternary classification models were generated to predict chemical carcinogenicity by five machine learning methods. The models were evaluated by an external validation set containing 87 chemicals from ISSCAN database. The best binary model was developed by MACCS keys and kNN algorithm with predictive accuracy at 83.91 %, while the best ternary model was also generated by MACCS keys and kNN algorithm with overall accuracy at 80.46 %. Furthermore, the best binary and ternary classification models were used to estimate carcinogenicity of tobacco smoke components containing 2251 compounds. 981 ones were predicted as carcinogens by binary classification model, while 110 compounds were predicted as strong carcinogens and 807 ones as weak carcinogens by ternary classification model. The results indicated that our models would be helpful for prediction of chemical carcinogenicity.
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Affiliation(s)
- Xiao Li
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, P. R. China phone: +86-21-6425-1052; fax: +86-21-6425-1033.,Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai 201203, P. R. China
| | - Zheng Du
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, P. R. China phone: +86-21-6425-1052; fax: +86-21-6425-1033
| | - Jie Wang
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, P. R. China phone: +86-21-6425-1052; fax: +86-21-6425-1033
| | - Zengrui Wu
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, P. R. China phone: +86-21-6425-1052; fax: +86-21-6425-1033
| | - Weihua Li
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, P. R. China phone: +86-21-6425-1052; fax: +86-21-6425-1033
| | - Guixia Liu
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, P. R. China phone: +86-21-6425-1052; fax: +86-21-6425-1033
| | - Xu Shen
- Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai 201203, P. R. China
| | - Yun Tang
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, P. R. China phone: +86-21-6425-1052; fax: +86-21-6425-1033. .,Key Laboratory of Cigarette Smoke, Technical Center, Shanghai Tobacco Group Co. Ltd. Shanghai 200082, P. R. China.
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Abstract
Computational approaches offer the attraction of being both fast and cheap to run being able to process thousands of chemical structures in a few minutes. As with all new technology, there is a tendency for these approaches to be hyped up and claims of reliability and performance may be exaggerated. So just how good are these computational methods?
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Affiliation(s)
- Nigel Greene
- Worldwide Medicinal Chemistry
- Pfizer Inc. Groton
- CT 06340, USA
| | - William Pennie
- Drug Safety Research and Evaluation
- Takeda Pharmaceuticals International Inc
- Cambridge, USA
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Mridha P, Pal P, Roy K. Chemometric modelling of triphenylmethyl derivatives as potent anticancer agents. MOLECULAR SIMULATION 2014. [DOI: 10.1080/08927022.2013.854897] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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Kar S, Roy K. Predictive toxicity modelling of benzodiazepine drugs using multiplein silicoapproaches: descriptor-based QSTR, group-based QSTR and 3D-toxicophore mapping. MOLECULAR SIMULATION 2014. [DOI: 10.1080/08927022.2014.888718] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
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Pramanik S, Roy K. Predictive modeling of chemical toxicity towards Pseudokirchneriella subcapitata using regression and classification based approaches. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2014; 101:184-190. [PMID: 24507144 DOI: 10.1016/j.ecoenv.2013.12.030] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/21/2013] [Revised: 12/28/2013] [Accepted: 12/30/2013] [Indexed: 06/03/2023]
Abstract
Biodiversity nurturing may be a valuable pathway in controlling chemical stress on the ecosystem. In the present work, in silico studies have been performed to develop regression based quantitative structure toxicity relationship (QSTR) models using a data set containing 105 organic chemicals for the prediction of 48-h chemical toxicity towards Pseudokirchneriella subcapitata. Classification based linear discriminant analysis (LDA) was also performed to distinguish chemicals into toxic and nontoxic groups using the same data set. The developed models were found to possess good predictive quality in terms of internal, external and overall validation parameters. The regression based QSTR model suggests that second order molecular connectivity index (molecular size and lipophilicity), density (aromaticity), relative shape of molecules (cyclicity/aromaticity), and specific molecular fragments of the chemicals are important properties of chemicals to exert their toxicity on P. subcapitata. The classification based LDA QSTR model suggested that fused ring aromatic systems, secondary carbon atom fragments, second order valence molecular connectivity indices (molecular size and branching) and molecular weight are the distinguishing features to differentiate chemicals into toxic and nontoxic groups.
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Affiliation(s)
- Subrata Pramanik
- Drug Theoretics and Cheminformatics Laboratory, Division of Medicinal and Pharmaceutical Chemistry, Department of Pharmaceutical Technology, Jadavpur University, Kolkata 700032, India
| | - Kunal Roy
- Drug Theoretics and Cheminformatics Laboratory, Division of Medicinal and Pharmaceutical Chemistry, Department of Pharmaceutical Technology, Jadavpur University, Kolkata 700032, India.
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Gu T, Yang X, Li M, Wu M, Su Q, Lu W, Zhang Y. Predicting the DPP-IV inhibitory activity pIC₅₀ based on their physicochemical properties. BIOMED RESEARCH INTERNATIONAL 2013; 2013:798743. [PMID: 23865065 PMCID: PMC3705804 DOI: 10.1155/2013/798743] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/29/2013] [Revised: 05/10/2013] [Accepted: 05/28/2013] [Indexed: 11/17/2022]
Abstract
The second development program developed in this work was introduced to obtain physicochemical properties of DPP-IV inhibitors. Based on the computation of molecular descriptors, a two-stage feature selection method called mRMR-BFS (minimum redundancy maximum relevance-backward feature selection) was adopted. Then, the support vector regression (SVR) was used in the establishment of the model to map DPP-IV inhibitors to their corresponding inhibitory activity possible. The squared correlation coefficient for the training set of LOOCV and the test set are 0.815 and 0.884, respectively. An online server for predicting inhibitory activity pIC50 of the DPP-IV inhibitors as described in this paper has been given in the introduction.
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Affiliation(s)
- Tianhong Gu
- School of Materials Science and Engineering, Shanghai University, 149 Yan-Chang Road, Shanghai 200072, China
| | - Xiaoyan Yang
- Department of Chemistry, College of Sciences, Shanghai University, 99 Shang-Da Road, Shanghai 200444, China
| | - Minjie Li
- Department of Chemistry, College of Sciences, Shanghai University, 99 Shang-Da Road, Shanghai 200444, China
| | - Milin Wu
- Department of Chemistry, College of Sciences, Shanghai University, 99 Shang-Da Road, Shanghai 200444, China
| | - Qiang Su
- School of Materials Science and Engineering, Shanghai University, 149 Yan-Chang Road, Shanghai 200072, China
| | - Wencong Lu
- Department of Chemistry, College of Sciences, Shanghai University, 99 Shang-Da Road, Shanghai 200444, China
| | - Yuhui Zhang
- Department of Neurosurgery, Changhai Hospital, Second Military Medical University, 168 Chang-Hai Road, Shanghai 200433, China
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