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Jiménez-Luna J, Grisoni F, Weskamp N, Schneider G. Artificial intelligence in drug discovery: recent advances and future perspectives. Expert Opin Drug Discov 2021; 16:949-959. [PMID: 33779453 DOI: 10.1080/17460441.2021.1909567] [Citation(s) in RCA: 83] [Impact Index Per Article: 27.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Introduction: Artificial intelligence (AI) has inspired computer-aided drug discovery. The widespread adoption of machine learning, in particular deep learning, in multiple scientific disciplines, and the advances in computing hardware and software, among other factors, continue to fuel this development. Much of the initial skepticism regarding applications of AI in pharmaceutical discovery has started to vanish, consequently benefitting medicinal chemistry.Areas covered: The current status of AI in chemoinformatics is reviewed. The topics discussed herein include quantitative structure-activity/property relationship and structure-based modeling, de novo molecular design, and chemical synthesis prediction. Advantages and limitations of current deep learning applications are highlighted, together with a perspective on next-generation AI for drug discovery.Expert opinion: Deep learning-based approaches have only begun to address some fundamental problems in drug discovery. Certain methodological advances, such as message-passing models, spatial-symmetry-preserving networks, hybrid de novo design, and other innovative machine learning paradigms, will likely become commonplace and help address some of the most challenging questions. Open data sharing and model development will play a central role in the advancement of drug discovery with AI.
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Affiliation(s)
- José Jiménez-Luna
- Department of Chemistry and Applied Biosciences, ETH Zurich, Zurich, Switzerland
| | - Francesca Grisoni
- Department of Chemistry and Applied Biosciences, ETH Zurich, Zurich, Switzerland
| | - Nils Weskamp
- Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach an Der Riss, Germany
| | - Gisbert Schneider
- Department of Chemistry and Applied Biosciences, ETH Zurich, Zurich, Switzerland
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Yadav J, Pawar AP, Nagare YK, Iype E, Rangan K, Ohshita J, Kumar D, Kumar I. Direct Amine-Catalyzed Enantioselective Synthesis of Pentacyclic Dibenzo[ b, f][1,4]oxazepine/Thiazepine-Fused Isoquinuclidines along with DFT Calculations. J Org Chem 2020; 85:14094-14108. [PMID: 33030896 DOI: 10.1021/acs.joc.0c02141] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
A direct protocol for the asymmetric synthesis of dibenzoxazepine/thiazepine-fused [2.2.2] isoquinuclidines is developed. The reaction proceeds through a proline-catalyzed direct Mannich reaction followed by an intramolecular aza-Michael cascade sequence between 2-cyclohexene-1-one and various tricyclic imines, like dibenzoxazepines/thiazepines, as an overall [4 + 2] aza-Diels-Alder reaction. A series of pentacyclic isoquinuclidines have been prepared, with complete endo-selectivity, in good to high yields and excellent enantioselectivity (>99:1). Density functional theory (DFT) calculations further support the observed high stereochemical outcome of the reaction.
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Affiliation(s)
- Jyothi Yadav
- Department of Chemistry, Birla Institute of Technology and Science, Pilani, Pilani 333031, Rajasthan, India
| | - Amol Prakash Pawar
- Department of Chemistry, Birla Institute of Technology and Science, Pilani, Pilani 333031, Rajasthan, India
| | - Yadav Kacharu Nagare
- Department of Chemistry, Birla Institute of Technology and Science, Pilani, Pilani 333031, Rajasthan, India
| | - Eldhose Iype
- Department of Chemical Engineering, Birla Institute of Technology and Science, Pilani, Dubai Campus, Dubai 345055, UAE
| | - Krishnan Rangan
- Department of Chemistry, Birla Institute of Technology and Science, Pilani, Hyderabad Campus, Hyderabad 500078, Telangana, India
| | - Joji Ohshita
- Applied Chemistry Program, Graduate School of Advanced Science and Engineering, Hiroshima University, Higashi-Hiroshima 739-8527, Japan
| | - Dalip Kumar
- Department of Chemistry, Birla Institute of Technology and Science, Pilani, Pilani 333031, Rajasthan, India
| | - Indresh Kumar
- Department of Chemistry, Birla Institute of Technology and Science, Pilani, Pilani 333031, Rajasthan, India
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Vásquez-Domínguez E, Armijos-Jaramillo VD, Tejera E, González-Díaz H. Multioutput Perturbation-Theory Machine Learning (PTML) Model of ChEMBL Data for Antiretroviral Compounds. Mol Pharm 2019; 16:4200-4212. [PMID: 31426639 DOI: 10.1021/acs.molpharmaceut.9b00538] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Retroviral infections, such as HIV, are, until now, diseases with no cure. Medicine and pharmaceutical chemistry need and consider it a huge goal to define target proteins of new antiretroviral compounds. ChEMBL manages Big Data features with a complex data set, which is hard to organize. This makes information difficult to analyze due to a big number of characteristics described in order to predict new drug candidates for retroviral infections. For this reason, we propose to develop a new predictive model combining perturbation theory (PT) bases and machine learning (ML) modeling to create a new tool that can take advantage of all the available information. The PTML model proposed in this work for the ChEMBL data set preclinical experimental assays for antiretroviral compounds consists of a linear equation with four variables. The PT operators used are founded on multicondition moving averages, combining different features and simplifying the difficulty to manage all data. More than 140 000 preclinical assays for 56 105 compounds with different characteristics or experimental conditions have been carried out and can be found in ChEMBL database, covering combinations with 359 biological activity parameters (c0), 55 protein accessions (c1), 83 cell lines (c2), 64 organisms of assay (c3), and 773 subtypes or strains. We have included 150 148 preclinical experimental assays for HIV virus, 1188 for HTLV virus, 84 for simian immunodeficiency virus, 370 for murine leukemia virus, 119 for Rous sarcoma virus, 1581 for MMTV, etc. We also included 5277 assays for hepatitis B virus. The developed PTML model reached considerable values in sensibility (73.05% for training and 73.10% for validation), specificity (86.61% for training and 87.17% for validation), and accuracy (75.84% for training and 75.98% for validation). We also compared alternative PTML models with different PT operators such as covariance, moments, and exponential terms. Finally, we made a comparison between literature ML models with our PTML model and also artificial neural network (ANN) nonlinear models. We conclude that this PTML model is the first one to consider multiple characteristics of preclinical experimental antiretroviral assays combined, generating a simple, useful, and adaptable instrument, which could reduce time and costs in antiretroviral drugs research.
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Affiliation(s)
- Emilia Vásquez-Domínguez
- Department of Organic Chemistry II , University of Basque Country UPV/EHU , 48940 Leioa , Spain.,Faculty of Engineering and Applied Sciences-Biotechnology , Universidad de Las Américas (UDLA) , 170125 Quito , Ecuador
| | - Vinicio Danilo Armijos-Jaramillo
- Faculty of Engineering and Applied Sciences-Biotechnology , Universidad de Las Américas (UDLA) , 170125 Quito , Ecuador.,Bio-chemioinformatics group , Universidad de Las Américas (UDLA) , 170125 Quito , Ecuador
| | - Eduardo Tejera
- Faculty of Engineering and Applied Sciences-Biotechnology , Universidad de Las Américas (UDLA) , 170125 Quito , Ecuador.,Bio-chemioinformatics group , Universidad de Las Américas (UDLA) , 170125 Quito , Ecuador
| | - Humbert González-Díaz
- Department of Organic Chemistry II , University of Basque Country UPV/EHU , 48940 Leioa , Spain.,IKERBASQUE, Basque Foundation for Science , 48011 Bilbao , Spain
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Chicco D, Rovelli C. Computational prediction of diagnosis and feature selection on mesothelioma patient health records. PLoS One 2019; 14:e0208737. [PMID: 30629589 PMCID: PMC6328132 DOI: 10.1371/journal.pone.0208737] [Citation(s) in RCA: 38] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2018] [Accepted: 11/22/2018] [Indexed: 01/19/2023] Open
Abstract
BACKGROUND Mesothelioma is a lung cancer that kills thousands of people worldwide annually, especially those with exposure to asbestos. Diagnosis of mesothelioma in patients often requires time-consuming imaging techniques and biopsies. Machine learning can provide for a more effective, cheaper, and faster patient diagnosis and feature selection from clinical data in patient records. METHODS AND FINDINGS We analyzed a dataset of health records of 324 patients having mesothelioma symptoms from Turkey. The patients had prior asbestos exposure and displayed symptoms consistent with mesothelioma. We compared probabilistic neural network, perceptron-based neural network, random forest, one rule, and decision tree classifiers to predict diagnosis of the patient records. We measured classifiers' performance through standard confusion matrix scores such as Matthews correlation coefficient (MCC). Random forest outperformed all models tried, obtaining MCC = +0.37 on the complete imbalanced dataset and MCC = +0.64 on the under-sampled balanced dataset. We then employed random forest feature selection to identify the two most relevant dataset traits associated with mesothelioma: lung side and platelet count. These two risk factors resulted so predictive, that decision tree focusing on them achieved the second top accuracy on the complete dataset diagnosis prediction (MCC = +0.28), outperforming all other methods and even decision tree itself applied to all features. CONCLUSIONS Our results show that machine learning can predict diagnoses of patients having mesothelioma symptoms with high accuracy, sensitivity, and specificity, in few minutes. Additionally, random forest can efficiently select the most important features of this clinical dataset (lung side and platelet count) in few seconds. The importance of pleural plaques in lung sides and blood platelets in mesothelioma diagnosis indicates that physicians should focus on these two features when reading records of patients with mesothelioma symptoms. Moreover, doctors can exploit our machinery to predict patient diagnosis when only lung side and platelet data are available.
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Affiliation(s)
- Davide Chicco
- Peter Munk Cardiac Centre, Toronto, Ontario, Canada
- Princess Margaret Cancer Centre, Toronto, Ontario, Canada
| | - Cristina Rovelli
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
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Choudhary S, Singh A, Yadav J, Mir NA, Anthal S, Kant R, Kumar I. A simple route to tetracyclic oxazepine-fused pyrroles via metal-free [3+2] annulation between dibenzo[b,f][1,4]oxazepines and aqueous succinaldehyde. NEW J CHEM 2019. [DOI: 10.1039/c8nj04861d] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
Abstract
A direct method for the synthesis of tetracyclic oxazepine-fused pyrroles has been developed through [3+2] annulation between aqueous succinaldehyde and dibenzo[b,f][1,4]oxazepines under metal-free conditions.
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Affiliation(s)
- Sachin Choudhary
- Department of Chemistry
- Birla Institute of Technology and Science
- Pilani 333 031
- India
| | - Anoop Singh
- Department of Chemistry
- Birla Institute of Technology and Science
- Pilani 333 031
- India
| | - Jyothi Yadav
- Department of Chemistry
- Birla Institute of Technology and Science
- Pilani 333 031
- India
| | - Nisar A. Mir
- Department of Chemistry
- Birla Institute of Technology and Science
- Pilani 333 031
- India
| | - Sumati Anthal
- X-ray Crystallography Laboratory
- Post-Graduate Department of Physics & Electronics
- University of Jammu
- Jammu 180 006
- India
| | - Rajni Kant
- X-ray Crystallography Laboratory
- Post-Graduate Department of Physics & Electronics
- University of Jammu
- Jammu 180 006
- India
| | - Indresh Kumar
- Department of Chemistry
- Birla Institute of Technology and Science
- Pilani 333 031
- India
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Ferreira da Costa J, Silva D, Caamaño O, Brea JM, Loza MI, Munteanu CR, Pazos A, García-Mera X, González-Díaz H. Perturbation Theory/Machine Learning Model of ChEMBL Data for Dopamine Targets: Docking, Synthesis, and Assay of New l-Prolyl-l-leucyl-glycinamide Peptidomimetics. ACS Chem Neurosci 2018; 9:2572-2587. [PMID: 29791132 DOI: 10.1021/acschemneuro.8b00083] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
Predicting drug-protein interactions (DPIs) for target proteins involved in dopamine pathways is a very important goal in medicinal chemistry. We can tackle this problem using Molecular Docking or Machine Learning (ML) models for one specific protein. Unfortunately, these models fail to account for large and complex big data sets of preclinical assays reported in public databases. This includes multiple conditions of assays, such as different experimental parameters, biological assays, target proteins, cell lines, organism of the target, or organism of assay. On the other hand, perturbation theory (PT) models allow us to predict the properties of a query compound or molecular system in experimental assays with multiple boundary conditions based on a previously known case of reference. In this work, we report the first PTML (PT + ML) study of a large ChEMBL data set of preclinical assays of compounds targeting dopamine pathway proteins. The best PTML model found predicts 50000 cases with accuracy of 70-91% in training and external validation series. We also compared the linear PTML model with alternative PTML models trained with multiple nonlinear methods (artificial neural network (ANN), Random Forest, Deep Learning, etc.). Some of the nonlinear methods outperform the linear model but at the cost of a notable increment of the complexity of the model. We illustrated the practical use of the new model with a proof-of-concept theoretical-experimental study. We reported for the first time the organic synthesis, chemical characterization, and pharmacological assay of a new series of l-prolyl-l-leucyl-glycinamide (PLG) peptidomimetic compounds. In addition, we performed a molecular docking study for some of these compounds with the software Vina AutoDock. The work ends with a PTML model predictive study of the outcomes of the new compounds in a large number of assays. Therefore, this study offers a new computational methodology for predicting the outcome for any compound in new assays. This PTML method focuses on the prediction with a simple linear model of multiple pharmacological parameters (IC50, EC50, Ki, etc.) for compounds in assays involving different cell lines used, organisms of the protein target, or organism of assay for proteins in the dopamine pathway.
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Affiliation(s)
- Joana Ferreira da Costa
- Department of Organic Chemistry, University of Santiago de Compostela, 15782 Santiago de Compostela, Spain
| | - David Silva
- Department of Organic Chemistry, University of Santiago de Compostela, 15782 Santiago de Compostela, Spain
| | - Olga Caamaño
- Department of Organic Chemistry, University of Santiago de Compostela, 15782 Santiago de Compostela, Spain
| | - José M. Brea
- CIMUS, University of Santiago de Compostela, 15782 Santiago de Compostela, Spain
- Department of Pharmacology, Pharmacy and Pharmaceutical Technology, University of Santiago de Compostela, 15782 Santiago de Compostela, Spain
| | - Maria Isabel Loza
- CIMUS, University of Santiago de Compostela, 15782 Santiago de Compostela, Spain
- Department of Pharmacology, Pharmacy and Pharmaceutical Technology, University of Santiago de Compostela, 15782 Santiago de Compostela, Spain
| | - Cristian R. Munteanu
- Instituto de Investigacion Biomedica de A Coruña (INIBIC), Complexo Hospitalario Universitario de A Coruña (CHUAC), A Coruña, 15006, Spain
| | - Alejandro Pazos
- Instituto de Investigacion Biomedica de A Coruña (INIBIC), Complexo Hospitalario Universitario de A Coruña (CHUAC), A Coruña, 15006, Spain
- Computer Science Department, Faculty of Computer Science, University of A Coruna, 15071 A Coruña, Spain
| | - Xerardo García-Mera
- Department of Organic Chemistry, University of Santiago de Compostela, 15782 Santiago de Compostela, Spain
| | - Humbert González-Díaz
- Department of Organic Chemistry II, University of Basque Country UPV/EHU, 48940 Leioa, Spain
- IKERBASQUE, Basque Foundation for Science, 48011 Bilbao, Spain
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7
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Bediaga H, Arrasate S, González-Díaz H. PTML Combinatorial Model of ChEMBL Compounds Assays for Multiple Types of Cancer. ACS COMBINATORIAL SCIENCE 2018; 20:621-632. [PMID: 30240186 DOI: 10.1021/acscombsci.8b00090] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Determining the target proteins of new anticancer compounds is a very important task in Medicinal Chemistry. In this sense, chemists carry out preclinical assays with a high number of combinations of experimental conditions (c j). In fact, ChEMBL database contains outcomes of 65 534 different anticancer activity preclinical assays for 35 565 different chemical compounds (1.84 assays per compound). These assays cover different combinations of c j formed from >70 different biological activity parameters ( c0), >300 different drug targets ( c1), >230 cell lines ( c2), and 5 organisms of assay ( c3) or organisms of the target ( c4). It include a total of 45 833 assays in leukemia, 6227 assays in breast cancer, 2499 assays in ovarian cancer, 3499 in colon cancer, 3159 in lung cancer, 2750 in prostate cancer, 601 in melanoma, etc. This is a very complex data set with multiple Big Data features. This data is hard to be rationalized by researchers to extract useful relationships and predict new compounds. In this context, we propose to combine perturbation theory (PT) ideas and machine learning (ML) modeling to solve this combinatorial-like problem. In this work, we report a PTML (PT + ML) model for ChEMBL data set of preclinical assays of anticancer compounds. This is a simple linear model with only three variables. The model presented values of area under receiver operating curve = AUROC = 0.872, specificity = Sp(%) = 90.2, sensitivity = Sn(%) = 70.6, and overall accuracy = Ac(%) = 87.7 in training series. The model also have Sp(%) = 90.1, Sn(%) = 71.4, and Ac(%) = 87.8 in external validation series. The model use PT operators based on multicondition moving averages to capture all the complexity of the data set. We also compared the model with nonlinear artificial neural network (ANN) models obtaining similar results. This confirms the hypothesis of a linear relationship between the PT operators and the classification as anticancer compounds in different combinations of assay conditions. Last, we compared the model with other PTML models reported in the literature concluding that this is the only one PTML model able to predict activity against multiple types of cancer. This model is a simple but versatile tool for the prediction of the targets of anticancer compounds taking into consideration multiple combinations of experimental conditions in preclinical assays.
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Affiliation(s)
- Harbil Bediaga
- Department of Organic Chemistry II, University of Basque Country UPV/EHU, 48940, Leioa, Spain
| | - Sonia Arrasate
- Department of Organic Chemistry II, University of Basque Country UPV/EHU, 48940, Leioa, Spain
| | - Humbert González-Díaz
- Department of Organic Chemistry II, University of Basque Country UPV/EHU, 48940, Leioa, Spain
- IKERBASQUE, Basque Foundation for Science, 48011, Bilbao, Spain
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Choudhary S, Pawar AP, Yadav J, Sharma DK, Kant R, Kumar I. One-Pot Synthesis of Chiral Tetracyclic Dibenzo[ b, f][1,4]oxazepine-Fused 1,2-Dihydropyridines (DHPs) under Metal-Free Conditions. J Org Chem 2018; 83:9231-9239. [PMID: 29906390 DOI: 10.1021/acs.joc.8b01232] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
An efficient protocol for the catalytic asymmetric synthesis of new dibenzo[ b, f][1,4]-oxazepine-fused 1,2-dihydropyridines (DHPs) has been described under metal-free conditions. This reaction proceeds through proline-catalyzed direct Mannich/cyclization between seven-membered dibenzo[ b, f][1,4]-oxazepine-imines and aqueous glutaraldehyde, followed by IBX-mediated site-selective dehydrogenative oxidation in one-pot operation with high yields (up to 92%) and excellent enantioselectivity (up to >99:1 er).
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Affiliation(s)
- Sachin Choudhary
- Department of Chemistry , Birla Institute of Technology & Science , Pilani 333 031 , India
| | - Amol Prakash Pawar
- Department of Chemistry , Birla Institute of Technology & Science , Pilani 333 031 , India
| | - Jyothi Yadav
- Department of Chemistry , Birla Institute of Technology & Science , Pilani 333 031 , India
| | - Devinder Kumar Sharma
- X-ray Crystallography Laboratory, Post-Graduate Department of Physics & Electronics , University of Jammu , Jammu and Kashmir 180 006 , India
| | - Rajni Kant
- X-ray Crystallography Laboratory, Post-Graduate Department of Physics & Electronics , University of Jammu , Jammu and Kashmir 180 006 , India
| | - Indresh Kumar
- Department of Chemistry , Birla Institute of Technology & Science , Pilani 333 031 , India
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Li Y, Meng JP, Lei J, Chen ZZ, Tang DY, Zhu J, Zhang J, Xu ZG. Efficient Synthesis of Fused Oxazepino-isoquinoline Scaffolds via an Ugi, Followed by an Intramolecular Cyclization. ACS COMBINATORIAL SCIENCE 2017; 19:324-330. [PMID: 28271876 DOI: 10.1021/acscombsci.7b00002] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
A mild and efficient protocol was developed for the synthesis of oxazepino-isoquinolines via a one-pot Ugi four-component reaction, followed by the intramolecular addition of the resulting alcohol to an alkyne moiety under microwave irradiation conditions. Notably, this process only required one purification step, providing facile access to two series of complex and potentially interesting biologically active scaffolds.
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Affiliation(s)
- Yong Li
- Chongqing
Engineering Laboratory of Targeted and Innovative Therapeutics, Chongqing
Key Laboratory of Kinase Modulators as Innovative Medicine, IATTI, Chongqing University of Arts and Sciences, 319 Honghe Avenue, Yongchuan, Chongqing 402160, China
- Key
Laboratory for Asymmetric Synthesis and Chiral Technology of Sichuan
Province, Chengdu Institute of Organic Chemistry, Chinese Academy of Sciences. Chengdu 610041, China
| | - Jiang-Ping Meng
- Chongqing
Engineering Laboratory of Targeted and Innovative Therapeutics, Chongqing
Key Laboratory of Kinase Modulators as Innovative Medicine, IATTI, Chongqing University of Arts and Sciences, 319 Honghe Avenue, Yongchuan, Chongqing 402160, China
| | - Jie Lei
- Chongqing
Engineering Laboratory of Targeted and Innovative Therapeutics, Chongqing
Key Laboratory of Kinase Modulators as Innovative Medicine, IATTI, Chongqing University of Arts and Sciences, 319 Honghe Avenue, Yongchuan, Chongqing 402160, China
- Key
Laboratory for Asymmetric Synthesis and Chiral Technology of Sichuan
Province, Chengdu Institute of Organic Chemistry, Chinese Academy of Sciences. Chengdu 610041, China
| | - Zhong-Zhu Chen
- Chongqing
Engineering Laboratory of Targeted and Innovative Therapeutics, Chongqing
Key Laboratory of Kinase Modulators as Innovative Medicine, IATTI, Chongqing University of Arts and Sciences, 319 Honghe Avenue, Yongchuan, Chongqing 402160, China
| | - Dian-Yong Tang
- Chongqing
Engineering Laboratory of Targeted and Innovative Therapeutics, Chongqing
Key Laboratory of Kinase Modulators as Innovative Medicine, IATTI, Chongqing University of Arts and Sciences, 319 Honghe Avenue, Yongchuan, Chongqing 402160, China
| | - Jin Zhu
- Chongqing
Engineering Laboratory of Targeted and Innovative Therapeutics, Chongqing
Key Laboratory of Kinase Modulators as Innovative Medicine, IATTI, Chongqing University of Arts and Sciences, 319 Honghe Avenue, Yongchuan, Chongqing 402160, China
- Key
Laboratory for Asymmetric Synthesis and Chiral Technology of Sichuan
Province, Chengdu Institute of Organic Chemistry, Chinese Academy of Sciences. Chengdu 610041, China
| | - Jin Zhang
- Chongqing
Engineering Laboratory of Targeted and Innovative Therapeutics, Chongqing
Key Laboratory of Kinase Modulators as Innovative Medicine, IATTI, Chongqing University of Arts and Sciences, 319 Honghe Avenue, Yongchuan, Chongqing 402160, China
| | - Zhi-Gang Xu
- Chongqing
Engineering Laboratory of Targeted and Innovative Therapeutics, Chongqing
Key Laboratory of Kinase Modulators as Innovative Medicine, IATTI, Chongqing University of Arts and Sciences, 319 Honghe Avenue, Yongchuan, Chongqing 402160, China
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10
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Vilar S, Quezada E, Uriarte E, Costanzi S, Borges F, Viña D, Hripcsak G. Computational Drug Target Screening through Protein Interaction Profiles. Sci Rep 2016; 6:36969. [PMID: 27845365 PMCID: PMC5109486 DOI: 10.1038/srep36969] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2016] [Accepted: 10/24/2016] [Indexed: 11/11/2022] Open
Abstract
The development of computational methods to discover novel drug-target interactions on a large scale is of great interest. We propose a new method for virtual screening based on protein interaction profile similarity to discover new targets for molecules, including existing drugs. We calculated Target Interaction Profile Fingerprints (TIPFs) based on ChEMBL database to evaluate drug similarity and generated new putative compound-target candidates from the non-intersecting targets in each pair of compounds. A set of drugs was further studied in monoamine oxidase B (MAO-B) and cyclooxygenase-1 (COX-1) enzyme through molecular docking and experimental assays. The drug ethoxzolamide and the natural compound piperlongumine, present in Piper longum L, showed hMAO-B activity with IC50 values of 25 and 65 μM respectively. Five candidates, including lapatinib, SB-202190, RO-316233, GW786460X and indirubin-3′-monoxime were tested against human COX-1. Compounds SB-202190 and RO-316233 showed a IC50 in hCOX-1 of 24 and 25 μM respectively (similar range as potent inhibitors such as diclofenac and indomethacin in the same experimental conditions). Lapatinib and indirubin-3′-monoxime showed moderate hCOX-1 activity (19.5% and 28% of enzyme inhibition at 25 μM respectively). Our modeling constitutes a multi-target predictor for large scale virtual screening with potential in lead discovery, repositioning and drug safety.
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Affiliation(s)
- Santiago Vilar
- Department of Biomedical Informatics, Columbia University Medical Center, New York, NY 10032, USA.,Department of Organic Chemistry, Faculty of Pharmacy, University of Santiago de Compostela, 15782, Santiago de Compostela, Spain
| | - Elías Quezada
- CIQUP, Department of Chemistry &Biochemistry, Faculty of Sciences, University of Porto, 4169-007, Porto, Portugal
| | - Eugenio Uriarte
- Department of Organic Chemistry, Faculty of Pharmacy, University of Santiago de Compostela, 15782, Santiago de Compostela, Spain
| | - Stefano Costanzi
- Department of Chemistry, American University, 20016 Washington, DC, USA
| | - Fernanda Borges
- CIQUP, Department of Chemistry &Biochemistry, Faculty of Sciences, University of Porto, 4169-007, Porto, Portugal
| | - Dolores Viña
- Department of Pharmacology, CIMUS, University of Santiago de Compostela, 15782, Santiago de Compostela, Spain
| | - George Hripcsak
- Department of Biomedical Informatics, Columbia University Medical Center, New York, NY 10032, USA
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11
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Zhou B, Sun Q, Kong DX. Predicting cancer-relevant proteins using an improved molecular similarity ensemble approach. Oncotarget 2016; 7:32394-407. [PMID: 27083051 PMCID: PMC5078021 DOI: 10.18632/oncotarget.8716] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2015] [Accepted: 03/28/2016] [Indexed: 12/15/2022] Open
Abstract
In this study, we proposed an improved algorithm for identifying proteins relevant to cancer. The algorithm was named two-layer molecular similarity ensemble approach (TL-SEA). We applied TL-SEA to analyzing the correlation between anticancer compounds (against cell lines K562, MCF7 and A549) and active compounds against separate target proteins listed in BindingDB. Several associations between cancer types and related proteins were revealed using this chemoinformatics approach. An analysis of the literature showed that 26 of 35 predicted proteins were correlated with cancer cell proliferation, apoptosis or differentiation. Additionally, interactions between proteins in BindingDB and anticancer chemicals were also predicted. We discuss the roles of the most important predicted proteins in cancer biology and conclude that TL-SEA could be a useful tool for inferring novel proteins involved in cancer and revealing underlying molecular mechanisms.
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Affiliation(s)
- Bin Zhou
- State Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, Wuhan 430070, China
- Agricultural Bioinformatics Key Laboratory of Hubei Province, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
| | - Qi Sun
- State Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, Wuhan 430070, China
- Agricultural Bioinformatics Key Laboratory of Hubei Province, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
| | - De-Xin Kong
- State Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, Wuhan 430070, China
- Agricultural Bioinformatics Key Laboratory of Hubei Province, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
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12
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Zhang Z, Dai Z, Ma X, Liu Y, Ma X, Li W, Ma C. Cu-catalyzed one-pot synthesis of fused oxazepinone derivatives via sp2 C–H and O–H cross-dehydrogenative coupling. Org Chem Front 2016. [DOI: 10.1039/c6qo00040a] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
An efficient Cu-catalyzed cascade reaction protocol was developed for the synthesis of fused oxazepinone derivatives via sp2 C–H and O–H cross-dehydrogenative coupling.
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Affiliation(s)
- Zeyuan Zhang
- School of Chemistry and Chemical Engineering
- Shandong University
- Jinan
- P R China
| | - Zhen Dai
- School of Chemistry and Chemical Engineering
- Shandong University
- Jinan
- P R China
| | - Xinkun Ma
- School of Chemistry and Chemical Engineering
- Shandong University
- Jinan
- P R China
| | - Yihan Liu
- School of Chemistry and Chemical Engineering
- Shandong University
- Jinan
- P R China
| | - Xiaojun Ma
- School of Chemistry and Chemical Engineering
- Shandong University
- Jinan
- P R China
| | - Wanli Li
- School of Chemistry and Chemical Engineering
- Shandong University
- Jinan
- P R China
| | - Chen Ma
- School of Chemistry and Chemical Engineering
- Shandong University
- Jinan
- P R China
- State Key Laboratory of Natural and Biomimetic Drugs
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13
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Ligand biological activity predictions using fingerprint-based artificial neural networks (FANN-QSAR). Methods Mol Biol 2015; 1260:149-64. [PMID: 25502380 DOI: 10.1007/978-1-4939-2239-0_9] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
This chapter focuses on the fingerprint-based artificial neural networks QSAR (FANN-QSAR) approach to predict biological activities of structurally diverse compounds. Three types of fingerprints, namely ECFP6, FP2, and MACCS, were used as inputs to train the FANN-QSAR models. The results were benchmarked against known 2D and 3D QSAR methods, and the derived models were used to predict cannabinoid (CB) ligand binding activities as a case study. In addition, the FANN-QSAR model was used as a virtual screening tool to search a large NCI compound database for lead cannabinoid compounds. We discovered several compounds with good CB2 binding affinities ranging from 6.70 nM to 3.75 μM. The studies proved that the FANN-QSAR method is a useful approach to predict bioactivities or properties of ligands and to find novel lead compounds for drug discovery research.
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14
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Refat HM. Synthesis and Antimicrobial Evaluation of Some New Spiroindolinone Derivatives. J Heterocycl Chem 2014. [DOI: 10.1002/jhet.2253] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Affiliation(s)
- Hala M. Refat
- Chemistry Department, Faculty of Education; Suez Canal University; Al-Arish Egypt
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15
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Zhou S, Li GB, Huang LY, Xie HZ, Zhao YL, Chen YZ, Li LL, Yang SY. A prediction model of drug-induced ototoxicity developed by an optimal support vector machine (SVM) method. Comput Biol Med 2014; 51:122-7. [PMID: 24907415 DOI: 10.1016/j.compbiomed.2014.05.005] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2013] [Revised: 05/07/2014] [Accepted: 05/09/2014] [Indexed: 02/05/2023]
Abstract
Drug-induced ototoxicity, as a toxic side effect, is an important issue needed to be considered in drug discovery. Nevertheless, current experimental methods used to evaluate drug-induced ototoxicity are often time-consuming and expensive, indicating that they are not suitable for a large-scale evaluation of drug-induced ototoxicity in the early stage of drug discovery. We thus, in this investigation, established an effective computational prediction model of drug-induced ototoxicity using an optimal support vector machine (SVM) method, GA-CG-SVM. Three GA-CG-SVM models were developed based on three training sets containing agents bearing different risk levels of drug-induced ototoxicity. For comparison, models based on naïve Bayesian (NB) and recursive partitioning (RP) methods were also used on the same training sets. Among all the prediction models, the GA-CG-SVM model II showed the best performance, which offered prediction accuracies of 85.33% and 83.05% for two independent test sets, respectively. Overall, the good performance of the GA-CG-SVM model II indicates that it could be used for the prediction of drug-induced ototoxicity in the early stage of drug discovery.
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Affiliation(s)
- Shu Zhou
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, West China Medical School, Sichuan University, Sichuan 610041, PR China
| | - Guo-Bo Li
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, West China Medical School, Sichuan University, Sichuan 610041, PR China
| | - Lu-Yi Huang
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, West China Medical School, Sichuan University, Sichuan 610041, PR China
| | - Huan-Zhang Xie
- West China School of Pharmacy, Sichuan University, Sichuan 610041, PR China
| | - Ying-Lan Zhao
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, West China Medical School, Sichuan University, Sichuan 610041, PR China
| | - Yu-Zong Chen
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, West China Medical School, Sichuan University, Sichuan 610041, PR China
| | - Lin-Li Li
- West China School of Pharmacy, Sichuan University, Sichuan 610041, PR China.
| | - Sheng-Yong Yang
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, West China Medical School, Sichuan University, Sichuan 610041, PR China.
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16
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Ganguly NC, Mondal P, Roy S, Mitra P. Ligand-free copper-catalyzed efficient one-pot access of benzo[b]pyrido[3,2-f][1,4]oxazepinones through O-heteroarylation-Smiles rearrangement-cyclization cascade. RSC Adv 2014. [DOI: 10.1039/c4ra11128a] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
Abstract
An optimized protocol towards synthesis of benzopyrido[1,4]oxazepinones of potential biological relevance is achieved by coupling of N-substituted-o-chloronicotinamides and o-halogenated naphthols/phenols.
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Affiliation(s)
- Nemai C. Ganguly
- Department of Chemistry
- University of Kalyani
- Kalyani 741235, India
| | - Pallab Mondal
- Department of Chemistry
- University of Kalyani
- Kalyani 741235, India
| | - Sushmita Roy
- Department of Chemistry
- University of Kalyani
- Kalyani 741235, India
| | - Partha Mitra
- Department of Inorganic Chemistry
- Indian Association for the Cultivation of Science
- Kolkata 700032, India
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17
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18
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Ferino G, Cadoni E, Matos MJ, Quezada E, Uriarte E, Santana L, Vilar S, Tatonetti NP, Yáñez M, Viña D, Picciau C, Serra S, Delogu G. MAO Inhibitory Activity of 2-Arylbenzofurans versus 3-Arylcoumarins: Synthesis, in vitro Study, and Docking Calculations. ChemMedChem 2013; 8:956-66. [DOI: 10.1002/cmdc.201300048] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2013] [Revised: 04/02/2013] [Indexed: 01/03/2023]
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19
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Vilar S, Uriarte E, Santana L, Tatonetti NP, Friedman C. Detection of drug-drug interactions by modeling interaction profile fingerprints. PLoS One 2013; 8:e58321. [PMID: 23520498 PMCID: PMC3592896 DOI: 10.1371/journal.pone.0058321] [Citation(s) in RCA: 68] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2012] [Accepted: 02/01/2013] [Indexed: 11/19/2022] Open
Abstract
Drug-drug interactions (DDIs) constitute an important problem in postmarketing pharmacovigilance and in the development of new drugs. The effectiveness or toxicity of a medication could be affected by the co-administration of other drugs that share pharmacokinetic or pharmacodynamic pathways. For this reason, a great effort is being made to develop new methodologies to detect and assess DDIs. In this article, we present a novel method based on drug interaction profile fingerprints (IPFs) with successful application to DDI detection. IPFs were generated based on the DrugBank database, which provided 9,454 well-established DDIs as a primary source of interaction data. The model uses IPFs to measure the similarity of pairs of drugs and generates new putative DDIs from the non-intersecting interactions of a pair. We described as part of our analysis the pharmacological and biological effects associated with the putative interactions; for example, the interaction between haloperidol and dicyclomine can cause increased risk of psychosis and tardive dyskinesia. First, we evaluated the method through hold-out validation and then by using four independent test sets that did not overlap with DrugBank. Precision for the test sets ranged from 0.4–0.5 with more than two fold enrichment factor enhancement. In conclusion, we demonstrated the usefulness of the method in pharmacovigilance as a DDI predictor, and created a dataset of potential DDIs, highlighting the etiology or pharmacological effect of the DDI, and providing an exploratory tool to facilitate decision support in DDI detection and patient safety.
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Affiliation(s)
- Santiago Vilar
- Department of Biomedical Informatics, Columbia University Medical Center, New York, New York, United States of America.
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20
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Smith RA, Stokes EC, Langdon-Jones EE, Platts JA, Kariuki BM, Hallett AJ, Pope SJA. Cyclometalated cinchophen ligands on iridium(iii): towards water-soluble complexes with visible luminescence. Dalton Trans 2013; 42:10347-57. [DOI: 10.1039/c3dt51098k] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
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21
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Myint KZ, Wang L, Tong Q, Xie XQ. Molecular fingerprint-based artificial neural networks QSAR for ligand biological activity predictions. Mol Pharm 2012; 9:2912-23. [PMID: 22937990 PMCID: PMC3462244 DOI: 10.1021/mp300237z] [Citation(s) in RCA: 71] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
Abstract
In this manuscript, we have reported a novel 2D fingerprint-based artificial neural network QSAR (FANN-QSAR) method in order to effectively predict biological activities of structurally diverse chemical ligands. Three different types of fingerprints, namely, ECFP6, FP2 and MACCS, were used in FANN-QSAR algorithm development, and FANN-QSAR models were compared to known 3D and 2D QSAR methods using five data sets previously reported. In addition, the derived models were used to predict GPCR cannabinoid ligand binding affinities using our manually curated cannabinoid ligand database containing 1699 structurally diverse compounds with reported cannabinoid receptor subtype CB(2) activities. To demonstrate its useful applications, the established FANN-QSAR algorithm was used as a virtual screening tool to search a large NCI compound database for lead cannabinoid compounds, and we have discovered several compounds with good CB(2) binding affinities ranging from 6.70 nM to 3.75 μM. To the best of our knowledge, this is the first report for a fingerprint-based neural network approach validated with a successful virtual screening application in identifying lead compounds. The studies proved that the FANN-QSAR method is a useful approach to predict bioactivities or properties of ligands and to find novel lead compounds for drug discovery research.
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Affiliation(s)
- Kyaw-Zeyar Myint
- Department of Computational Biology, Joint Carnegie Mellon University-University of Pittsburgh Ph.D. Program, School of Medicine; Pittsburgh, Pennsylvania 15260
- Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, School of Pharmacy; Pittsburgh, Pennsylvania 15260
- Drug Discovery Institute; University of Pittsburgh, Pittsburgh, Pennsylvania 15260
| | - Lirong Wang
- Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, School of Pharmacy; Pittsburgh, Pennsylvania 15260
- Drug Discovery Institute; University of Pittsburgh, Pittsburgh, Pennsylvania 15260
- Pittsburgh Chemical Methods and Library Development (CMLD) Center; University of Pittsburgh, Pittsburgh, Pennsylvania 15260
| | - Qin Tong
- Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, School of Pharmacy; Pittsburgh, Pennsylvania 15260
| | - Xiang-Qun Xie
- Department of Computational Biology, Joint Carnegie Mellon University-University of Pittsburgh Ph.D. Program, School of Medicine; Pittsburgh, Pennsylvania 15260
- Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, School of Pharmacy; Pittsburgh, Pennsylvania 15260
- Drug Discovery Institute; University of Pittsburgh, Pittsburgh, Pennsylvania 15260
- Pittsburgh Chemical Methods and Library Development (CMLD) Center; University of Pittsburgh, Pittsburgh, Pennsylvania 15260
- Department of Structural Biology, University of Pittsburgh, Pittsburgh, Pennsylvania 15260
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22
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Vilar S, Harpaz R, Uriarte E, Santana L, Rabadan R, Friedman C. Drug-drug interaction through molecular structure similarity analysis. J Am Med Inform Assoc 2012; 19:1066-74. [PMID: 22647690 DOI: 10.1136/amiajnl-2012-000935] [Citation(s) in RCA: 124] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022] Open
Abstract
BACKGROUND Drug-drug interactions (DDIs) are responsible for many serious adverse events; their detection is crucial for patient safety but is very challenging. Currently, the US Food and Drug Administration and pharmaceutical companies are showing great interest in the development of improved tools for identifying DDIs. METHODS We present a new methodology applicable on a large scale that identifies novel DDIs based on molecular structural similarity to drugs involved in established DDIs. The underlying assumption is that if drug A and drug B interact to produce a specific biological effect, then drugs similar to drug A (or drug B) are likely to interact with drug B (or drug A) to produce the same effect. DrugBank was used as a resource for collecting 9454 established DDIs. The structural similarity of all pairs of drugs in DrugBank was computed to identify DDI candidates. RESULTS The methodology was evaluated using as a gold standard the interactions retrieved from the initial DrugBank database. Results demonstrated an overall sensitivity of 0.68, specificity of 0.96, and precision of 0.26. Additionally, the methodology was also evaluated in an independent test using the Micromedex/Drugdex database. CONCLUSION The proposed methodology is simple, efficient, allows the investigation of large numbers of drugs, and helps highlight the etiology of DDI. A database of 58 403 predicted DDIs with structural evidence is provided as an open resource for investigators seeking to analyze DDIs.
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Affiliation(s)
- Santiago Vilar
- Department of Biomedical Informatics, Columbia University Medical Center, New York, NY, USA.
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23
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Kitching MO, Hurst TE, Snieckus V. Copper-Catalyzed Cross-Coupling Interrupted by an Opportunistic Smiles Rearrangement: An Efficient Domino Approach to Dibenzoxazepinones. Angew Chem Int Ed Engl 2012. [DOI: 10.1002/ange.201106786] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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24
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Kitching MO, Hurst TE, Snieckus V. Copper-catalyzed cross-coupling interrupted by an opportunistic Smiles rearrangement: an efficient domino approach to dibenzoxazepinones. Angew Chem Int Ed Engl 2012; 51:2925-9. [PMID: 22311826 DOI: 10.1002/anie.201106786] [Citation(s) in RCA: 61] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2011] [Indexed: 11/10/2022]
Affiliation(s)
- Matthew O Kitching
- Department of Chemistry, Queen's University, Kingston, Ontario, K7L 3N6 Canada
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25
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MANOLOV I, MAICHLE-MOESSMER C. Synthesis and Structure of 3,3'-[(4-Benzyloxyphenyl)methylene]bis-(4-hydroxy-2H-chromen-2-one). X-RAY STRUCTURE ANALYSIS ONLINE 2012. [DOI: 10.2116/xraystruct.28.7] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Affiliation(s)
- Ilia MANOLOV
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Medical University
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26
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Vilar S, Harpaz R, Chase HS, Costanzi S, Rabadan R, Friedman C. Facilitating adverse drug event detection in pharmacovigilance databases using molecular structure similarity: application to rhabdomyolysis. J Am Med Inform Assoc 2011; 18 Suppl 1:i73-80. [PMID: 21946238 DOI: 10.1136/amiajnl-2011-000417] [Citation(s) in RCA: 46] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022] Open
Abstract
BACKGROUND Adverse drug events (ADE) cause considerable harm to patients, and consequently their detection is critical for patient safety. The US Food and Drug Administration maintains an adverse event reporting system (AERS) to facilitate the detection of ADE in drugs. Various data mining approaches have been developed that use AERS to detect signals identifying associations between drugs and ADE. The signals must then be monitored further by domain experts, which is a time-consuming task. OBJECTIVE To develop a new methodology that combines existing data mining algorithms with chemical information by analysis of molecular fingerprints to enhance initial ADE signals generated from AERS, and to provide a decision support mechanism to facilitate the identification of novel adverse events. RESULTS The method achieved a significant improvement in precision in identifying known ADE, and a more than twofold signal enhancement when applied to the ADE rhabdomyolysis. The simplicity of the method assists in highlighting the etiology of the ADE by identifying structurally similar drugs. A set of drugs with strong evidence from both AERS and molecular fingerprint-based modeling is constructed for further analysis. CONCLUSION The results demonstrate that the proposed methodology could be used as a pharmacovigilance decision support tool to facilitate ADE detection.
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Affiliation(s)
- Santiago Vilar
- Department of Biomedical Informatics, Columbia University Medical Center, New York, New York 10032, USA
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27
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Liu Y, Chu C, Huang A, Zhan C, Ma Y, Ma C. Regioselective synthesis of fused oxazepinone scaffolds through one-pot Smiles rearrangement tandem reaction. ACS COMBINATORIAL SCIENCE 2011; 13:547-53. [PMID: 21766861 DOI: 10.1021/co2001058] [Citation(s) in RCA: 45] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
The paper describes a convenient and facile methodology for the regioselective synthesis of fused oxazepinone scaffolds. This process is an efficient construction of the oxazepinone scaffold by a one-pot coupling/Smiles rearrangement/cyclization approach. This transition metal-free process has potential applications in the synthesis of biologically and medicinally relevant compounds.
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Affiliation(s)
- Yanli Liu
- School of Chemistry and Chemical Engineering, Shandong University, Jinan, 250100, P.R. China
| | - Chunxiao Chu
- School of Chemistry and Chemical Engineering, Shandong University, Jinan, 250100, P.R. China
| | - Aiping Huang
- School of Chemistry and Chemical Engineering, Shandong University, Jinan, 250100, P.R. China
| | - Chunjing Zhan
- School of Chemistry and Chemical Engineering, Shandong University, Jinan, 250100, P.R. China
| | - Ying Ma
- School of Chemistry and Chemical Engineering, Shandong University, Jinan, 250100, P.R. China
| | - Chen Ma
- School of Chemistry and Chemical Engineering, Shandong University, Jinan, 250100, P.R. China
- State Key Laboratory of Natural and Biomimetic Drugs, Peking University, Beijing, 100191, P.R. China
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28
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González-Díaz H, Prado-Prado F, Sobarzo-Sánchez E, Haddad M, Maurel Chevalley S, Valentin A, Quetin-Leclercq J, Dea-Ayuela MA, Teresa Gomez-Muños M, Munteanu CR, José Torres-Labandeira J, García-Mera X, Tapia RA, Ubeira FM. NL MIND-BEST: A web server for ligands and proteins discovery—Theoretic-experimental study of proteins of Giardia lamblia and new compounds active against Plasmodium falciparum. J Theor Biol 2011; 276:229-49. [DOI: 10.1016/j.jtbi.2011.01.010] [Citation(s) in RCA: 24] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2010] [Revised: 12/02/2010] [Accepted: 01/10/2011] [Indexed: 10/18/2022]
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29
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Patra JC, Chua BH. Artificial neural network-based drug design for diabetes mellitus using flavonoids. J Comput Chem 2010; 32:555-67. [DOI: 10.1002/jcc.21641] [Citation(s) in RCA: 30] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2010] [Revised: 06/03/2010] [Accepted: 06/28/2010] [Indexed: 11/07/2022]
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Abstract
Computer-aided drug design (CADD) methodologies have made great advances and contributed significantly to the discovery and/or optimization of many clinically used drugs in recent years. CADD tools have likewise been applied to the discovery of inhibitors of HIV-1 integrase, a difficult and worthwhile target for the development of efficient anti-HIV drugs. This article reviews the application of CADD tools, including pharmacophore search, quantitative structure-activity relationships, model building of integrase complexed with viral DNA and quantum-chemical studies in the discovery of HIV-1 integrase inhibitors. Different structurally diverse integrase inhibitors have been identified by, or with significant help from, various CADD tools.
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Affiliation(s)
- Chenzhong Liao
- Chemical Biology Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, DHHS, NCI-Frederick, 376 Boyles Street, Frederick, MD 21702, USA
| | - Marc C Nicklaus
- Chemical Biology Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, DHHS, NCI-Frederick, 376 Boyles Street, Frederick, MD 21702, USA
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31
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Multi-target spectral moment QSAR versus ANN for antiparasitic drugs against different parasite species. Bioorg Med Chem 2010; 18:2225-2231. [PMID: 20185316 DOI: 10.1016/j.bmc.2010.01.068] [Citation(s) in RCA: 65] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2009] [Revised: 01/22/2010] [Accepted: 01/29/2010] [Indexed: 11/23/2022]
Abstract
There are many of pathogen parasite species with different susceptibility profile to antiparasitic drugs. Unfortunately, almost QSAR models predict the biological activity of drugs against only one parasite species. Consequently, predicting the probability with which a drug is active against different species with a single unify model is a goal of the major importance. In so doing, we use Markov Chains theory to calculate new multi-target spectral moments to fit a QSAR model that predict by the first time a mt-QSAR model for 500 drugs tested in the literature against 16 parasite species and other 207 drugs no tested in the literature using spectral moments. The data was processed by linear discriminant analysis (LDA) classifying drugs as active or non-active against the different tested parasite species. The model correctly classifies 311 out of 358 active compounds (86.9%) and 2328 out of 2577 non-active compounds (90.3%) in training series. Overall training performance was 89.9%. Validation of the model was carried out by means of external predicting series. In these series the model classified correctly 157 out 190, 82.6% of antiparasitic compounds and 1151 out of 1277 non-active compounds (90.1%). Overall predictability performance was 89.2%. In addition we developed four types of non Linear Artificial neural networks (ANN) and we compared with the mt-QSAR model. The improved ANN model had an overall training performance was 87%. The present work report the first attempts to calculate within a unify framework probabilities of antiparasitic action of drugs against different parasite species based on spectral moment analysis.
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Rodriguez-Soca Y, Munteanu CR, Dorado J, Pazos A, Prado-Prado FJ, González-Díaz H. Trypano-PPI: A Web Server for Prediction of Unique Targets in Trypanosome Proteome by using Electrostatic Parameters of Protein−protein Interactions. J Proteome Res 2009; 9:1182-90. [DOI: 10.1021/pr900827b] [Citation(s) in RCA: 34] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Affiliation(s)
- Yamilet Rodriguez-Soca
- Department of Microbiology & Parasitology, Faculty of Pharmacy, University of Santiago de Compostela, 15782, Santiago de Compostela, Spain, and Department of Information and Communication Technologies, Computer Science Faculty, University of A Coruña, Campus de Elviña, 15071, A Coruña, Spain
| | - Cristian R. Munteanu
- Department of Microbiology & Parasitology, Faculty of Pharmacy, University of Santiago de Compostela, 15782, Santiago de Compostela, Spain, and Department of Information and Communication Technologies, Computer Science Faculty, University of A Coruña, Campus de Elviña, 15071, A Coruña, Spain
| | - Julián Dorado
- Department of Microbiology & Parasitology, Faculty of Pharmacy, University of Santiago de Compostela, 15782, Santiago de Compostela, Spain, and Department of Information and Communication Technologies, Computer Science Faculty, University of A Coruña, Campus de Elviña, 15071, A Coruña, Spain
| | - Alejandro Pazos
- Department of Microbiology & Parasitology, Faculty of Pharmacy, University of Santiago de Compostela, 15782, Santiago de Compostela, Spain, and Department of Information and Communication Technologies, Computer Science Faculty, University of A Coruña, Campus de Elviña, 15071, A Coruña, Spain
| | - Francisco J. Prado-Prado
- Department of Microbiology & Parasitology, Faculty of Pharmacy, University of Santiago de Compostela, 15782, Santiago de Compostela, Spain, and Department of Information and Communication Technologies, Computer Science Faculty, University of A Coruña, Campus de Elviña, 15071, A Coruña, Spain
| | - Humberto González-Díaz
- Department of Microbiology & Parasitology, Faculty of Pharmacy, University of Santiago de Compostela, 15782, Santiago de Compostela, Spain, and Department of Information and Communication Technologies, Computer Science Faculty, University of A Coruña, Campus de Elviña, 15071, A Coruña, Spain
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33
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A network-QSAR model for prediction of genetic-component biomarkers in human colorectal cancer. J Theor Biol 2009; 261:449-58. [DOI: 10.1016/j.jtbi.2009.07.031] [Citation(s) in RCA: 39] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2009] [Revised: 07/20/2009] [Accepted: 07/25/2009] [Indexed: 11/23/2022]
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34
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Patra JC, Singh O. Artificial neural networks-based approach to design ARIs using QSAR for diabetes mellitus. J Comput Chem 2009; 30:2494-508. [DOI: 10.1002/jcc.21240] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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Multi-target spectral moment: QSAR for antiviral drugs vs. different viral species. Anal Chim Acta 2009; 651:159-64. [PMID: 19782806 DOI: 10.1016/j.aca.2009.08.022] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2009] [Revised: 08/05/2009] [Accepted: 08/18/2009] [Indexed: 11/23/2022]
Abstract
The antiviral QSAR models have an important limitation today. They predict the biological activity of drugs against only one viral species. This is determined by the fact that most of the current reported molecular descriptors encode only information about the molecular structure. As a result, predicting the probability with which a drug is active against different viral species with a single unifying model is a goal of major importance. In this work, we use Markov Chain theory to calculate new multi-target spectral moments to fit a QSAR model for drugs active against 40 viral species. The model is based on 500 drugs (including active and non-active compounds) tested as antiviral agents in the recent literature; not all drugs were predicted against all viruses, but only those with experimental values. The database also contains 207 well-known compounds (not as recent as the previous ones) reported in the Merck Index with other activities that do not include antiviral action against any virus species. We used Linear Discriminant Analysis (LDA) to classify all these drugs into two classes as active or non-active against the different viral species tested, whose data we processed. The model correctly classifies 5129 out of 5594 non-active compounds (91.69%) and 412 out of 422 active compounds (97.63%). Overall training predictability was 92.34%. The validation of the model was carried out by means of external predicting series, the model classifying, thus, 2568 out of 2779 non-active compounds and 224 out of 229 active compounds. Overall training predictability was 92.82%. The present work reports the first attempts to calculate within a unified framework the probabilities of antiviral drugs against different virus species based on a spectral moment analysis.
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Concu R, Podda G, Uriarte E, González-Díaz H. Computational chemistry study of 3D-structure-function relationships for enzymes based on Markov models for protein electrostatic, HINT, and van der Waals potentials. J Comput Chem 2009; 30:1510-20. [DOI: 10.1002/jcc.21170] [Citation(s) in RCA: 33] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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Ji L, Wang X, Qin L, Luo S, Wang L. Predicting the Androgenicity of Structurally Diverse Compounds from Molecular Structure Using Different Classifiers. ACTA ACUST UNITED AC 2009. [DOI: 10.1002/qsar.200860090] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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García I, Munteanu CR, Fall Y, Gómez G, Uriarte E, González-Díaz H. QSAR and complex network study of the chiral HMGR inhibitor structural diversity. Bioorg Med Chem 2009; 17:165-75. [DOI: 10.1016/j.bmc.2008.11.007] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2008] [Revised: 10/31/2008] [Accepted: 11/06/2008] [Indexed: 10/21/2022]
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Vilar S, González-Díaz H, Santana L, Uriarte E. QSAR model for alignment-free prediction of human breast cancer biomarkers based on electrostatic potentials of protein pseudofolding HP-lattice networks. J Comput Chem 2008; 29:2613-22. [PMID: 18478581 DOI: 10.1002/jcc.21016] [Citation(s) in RCA: 35] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Network theory allows relationships to be established between numerical parameters that describe the molecular structure of genes and proteins and their biological properties. These models can be considered as quantitative structure-activity relationships (QSAR) for biopolymers. The work described here concerns the first QSAR model for 122 proteins that are associated with human breast cancer (HBC), as identified experimentally by Sjöblom et al. (Science 2006, 314, 268) from over 10,000 human proteins. In this study, the 122 proteins related to HBC (HBCp) and a control group of 200 proteins that are not related to HBC (non-HBCp) were forced to fold in an HP lattice network. From these networks a series of electrostatic potential parameters (xi(k)) was calculated to describe each protein numerically. The use of xi(k) as an entry point to linear discriminant analysis led to a QSAR model to discriminate between HBCp and non-HBCp, and this model could help to predict the involvement of a certain gene and/or protein in HBC. In addition, validation procedures were carried out on the model and these included an external prediction series and evaluation of an additional series of 1000 non-HBCp. In all cases good levels of classification were obtained with values above 80%. This study represents the first example of a QSAR model for the computational chemistry inspired search of potential HBC protein biomarkers.
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Affiliation(s)
- Santiago Vilar
- Unit of Bioinformatics and Connectivity Analysis, Institute of Industrial Pharmacy, and Department of Organic Chemistry, Faculty of Pharmacy, University of Santiago de Compostela, Santiago de Compostela 15782, Spain
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Prado-Prado FJ, Martinez de la Vega O, Uriarte E, Ubeira FM, Chou KC, González-Díaz H. Unified QSAR approach to antimicrobials. 4. Multi-target QSAR modeling and comparative multi-distance study of the giant components of antiviral drug-drug complex networks. Bioorg Med Chem 2008; 17:569-75. [PMID: 19112024 DOI: 10.1016/j.bmc.2008.11.075] [Citation(s) in RCA: 60] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2008] [Revised: 11/24/2008] [Accepted: 11/28/2008] [Indexed: 11/18/2022]
Abstract
One limitation of almost all antiviral Quantitative Structure-Activity Relationships (QSAR) models is that they predict the biological activity of drugs against only one species of virus. Consequently, the development of multi-tasking QSAR models (mt-QSAR) to predict drugs activity against different species of virus is of the major vitally important. These mt-QSARs offer also a good opportunity to construct drug-drug Complex Networks (CNs) that can be used to explore large and complex drug-viral species databases. It is known that in very large CNs we can use the Giant Component (GC) as a representative sub-set of nodes (drugs) and but the drug-drug similarity function selected may strongly determines the final network obtained. In the three previous works of the present series we reported mt-QSAR models to predict the antimicrobial activity against different fungi [Gonzalez-Diaz, H.; Prado-Prado, F. J.; Santana, L.; Uriarte, E. Bioorg.Med.Chem.2006, 14, 5973], bacteria [Prado-Prado, F. J.; Gonzalez-Diaz, H.; Santana, L.; Uriarte E. Bioorg.Med.Chem.2007, 15, 897] or parasite species [Prado-Prado, F.J.; González-Díaz, H.; Martinez de la Vega, O.; Ubeira, F.M.; Chou K.C. Bioorg.Med.Chem.2008, 16, 5871]. However, including these works, we do not found any report of mt-QSAR models for antivirals drug, or a comparative study of the different GC extracted from drug-drug CNs based on different similarity functions. In this work, we used Linear Discriminant Analysis (LDA) to fit a mt-QSAR model that classify 600 drugs as active or non-active against the 41 different tested species of virus. The model correctly classifies 143 of 169 active compounds (specificity=84.62%) and 119 of 139 non-active compounds (sensitivity=85.61%) and presents overall training accuracy of 85.1% (262 of 308 cases). Validation of the model was carried out by means of external predicting series, classifying the model 466 of 514, 90.7% of compounds. In order to illustrate the performance of the model in practice, we develop a virtual screening recognizing the model as active 92.7%, 102 of 110 antivirus compounds. These compounds were never use in training or predicting series. Next, we obtained and compared the topology of the CNs and their respective GCs based on Euclidean, Manhattan, Chebychey, Pearson and other similarity measures. The GC of the Manhattan network showed the more interesting features for drug-drug similarity search. We also give the procedure for the construction of Back-Projection Maps for the contribution of each drug sub-structure to the antiviral activity against different species.
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Affiliation(s)
- Francisco J Prado-Prado
- Department of Microbiology and Parasitology, Faculty of Pharmacy, University of Santiago de Compostela, Santiago de Compostela 15782, Spain
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Quantitative Proteome–Property Relationships (QPPRs). Part 1: Finding biomarkers of organic drugs with mean Markov connectivity indices of spiral networks of blood mass spectra. Bioorg Med Chem 2008; 16:9684-93. [DOI: 10.1016/j.bmc.2008.10.004] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2008] [Revised: 09/29/2008] [Accepted: 10/02/2008] [Indexed: 11/22/2022]
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Perez-Bello A, Munteanu CR, Ubeira FM, De Magalhães AL, Uriarte E, González-Díaz H. Alignment-free prediction of mycobacterial DNA promoters based on pseudo-folding lattice network or star-graph topological indices. J Theor Biol 2008; 256:458-66. [PMID: 18992259 PMCID: PMC7126577 DOI: 10.1016/j.jtbi.2008.09.035] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2008] [Revised: 09/23/2008] [Accepted: 09/25/2008] [Indexed: 12/01/2022]
Abstract
The importance of the promoter sequences in the function regulation of several important mycobacterial pathogens creates the necessity to design simple and fast theoretical models that can predict them. This work proposes two DNA promoter QSAR models based on pseudo-folding lattice network (LN) and star-graphs (SG) topological indices. In addition, a comparative study with the previous RNA electrostatic parameters of thermodynamically-driven secondary structure folding representations has been carried out. The best model of this work was obtained with only two LN stochastic electrostatic potentials and it is characterized by accuracy, selectivity and specificity of 90.87%, 82.96% and 92.95%, respectively. In addition, we pointed out the SG result dependence on the DNA sequence codification and we proposed a QSAR model based on codons and only three SG spectral moments.
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Affiliation(s)
- Alcides Perez-Bello
- Department of Microbiology and Parasitology, University of Santiago de Compostela, Santiago de Compostela 15782, Spain.
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Guha R. On the interpretation and interpretability of quantitative structure–activity relationship models. J Comput Aided Mol Des 2008; 22:857-71. [DOI: 10.1007/s10822-008-9240-5] [Citation(s) in RCA: 58] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2008] [Accepted: 08/14/2008] [Indexed: 01/28/2023]
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Silverman JEY, Ciustea M, Shudofsky AMD, Bender F, Shoemaker RH, Ricciardi RP. Identification of polymerase and processivity inhibitors of vaccinia DNA synthesis using a stepwise screening approach. Antiviral Res 2008; 80:114-23. [PMID: 18621425 DOI: 10.1016/j.antiviral.2008.05.010] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2007] [Revised: 05/08/2008] [Accepted: 05/14/2008] [Indexed: 12/16/2022]
Abstract
Nearly all DNA polymerases require processivity factors to ensure continuous incorporation of nucleotides. Processivity factors are specific for their cognate DNA polymerases. For this reason, the vaccinia DNA polymerase (E9) and the proteins associated with processivity (A20 and D4) are excellent therapeutic targets. In this study, we show the utility of stepwise rapid plate assays that (i) screen for compounds that block vaccinia DNA synthesis, (ii) eliminate trivial inhibitors, e.g. DNA intercalators, and (iii) distinguish whether inhibitors are specific for blocking DNA polymerase activity or processivity. The sequential plate screening of 2222 compounds from the NCI Diversity Set library yielded a DNA polymerase inhibitor (NSC 55636) and a processivity inhibitor (NSC 123526) that were capable of reducing vaccinia viral plaques with minimal cellular cytotoxicity. These compounds are predicted to block cellular infection by the smallpox virus, variola, based on the very high sequence identity between A20, D4 and E9 of vaccinia and the corresponding proteins of variola.
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Affiliation(s)
- Janice Elaine Y Silverman
- Department of Microbiology, School of Dental Medicine, University of Pennsylvania, Philadelphia, PA, USA
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Prado-Prado FJ, González-Díaz H, de la Vega OM, Ubeira FM, Chou KC. Unified QSAR approach to antimicrobials. Part 3: first multi-tasking QSAR model for input-coded prediction, structural back-projection, and complex networks clustering of antiprotozoal compounds. Bioorg Med Chem 2008; 16:5871-80. [PMID: 18485714 DOI: 10.1016/j.bmc.2008.04.068] [Citation(s) in RCA: 104] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2008] [Revised: 04/22/2008] [Accepted: 04/25/2008] [Indexed: 10/22/2022]
Abstract
Several pathogen parasite species show different susceptibilities to different antiparasite drugs. Unfortunately, almost all structure-based methods are one-task or one-target Quantitative Structure-Activity Relationships (ot-QSAR) that predict the biological activity of drugs against only one parasite species. Consequently, multi-tasking learning to predict drugs activity against different species by a single model (mt-QSAR) is vitally important. In the two previous works of the present series we reported two single mt-QSAR models in order to predict the antimicrobial activity against different fungal (Bioorg. Med. Chem.2006, 14, 5973-5980) or bacterial species (Bioorg. Med. Chem.2007, 15, 897-902). These mt-QSARs offer a good opportunity (unpractical with ot-QSAR) to construct drug-drug similarity Complex Networks and to map the contribution of sub-structures to function for multiple species. These possibilities were unattended in our previous works. In the present work, we continue this series toward other important direction of chemotherapy (antiparasite drugs) with the development of an mt-QSAR for more than 500 drugs tested in the literature against different parasites. The data were processed by Linear Discriminant Analysis (LDA) classifying drugs as active or non-active against the different tested parasite species. The model correctly classifies 212 out of 244 (87.0%) cases in training series and 207 out of 243 compounds (85.4%) in external validation series. In order to illustrate the performance of the QSAR for the selection of active drugs we carried out an additional virtual screening of antiparasite compounds not used in training or predicting series; the model recognized 97 out of 114 (85.1%) of them. We also give the procedures to construct back-projection maps and to calculate sub-structures contribution to the biological activity. Finally, we used the outputs of the QSAR to construct, by the first time, a multi-species Complex Networks of antiparasite drugs. The network predicted has 380 nodes (compounds), 634 edges (pairs of compounds with similar activity). This network allows us to cluster different compounds and identify on average three known compounds similar to a new query compound according to their profile of biological activity. This is the first attempt to calculate probabilities of antiparasitic action of drugs against different parasites.
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Agüero-Chapín G, González-Díaz H, de la Riva G, Rodríguez E, Sánchez-Rodríguez A, Podda G, Vazquez-Padrón RI. MMM-QSAR Recognition of Ribonucleases without Alignment: Comparison with an HMM Model and Isolation from Schizosaccharomyces pombe, Prediction, and Experimental Assay of a New Sequence. J Chem Inf Model 2008; 48:434-48. [DOI: 10.1021/ci7003225] [Citation(s) in RCA: 29] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Guillermín Agüero-Chapín
- Dipartimento Farmaco Chimico Tecnologico, Universitá Degli Studi di Cagliari, Cagliari, 09124, Italy, CAP, Faculty of Chemistry and Pharmacy, IBP, and CBQ, UCLV, Santa Clara 54830, Cuba, Unit for Bioinformatics & Connectivity Analysis (UBICA), Institute of Industrial Pharmacy and Department of Organic Chemistry, Faculty of Pharmacy, USC, Santiago de Compostela 15782, Spain, CINVESTAV-LANGEBIO, Irapuato, Guanajuato 36821, México, Caribbean Vitroplants, Santo Domingo 1464, Dominican Republic, and Vascular
| | - Humberto González-Díaz
- Dipartimento Farmaco Chimico Tecnologico, Universitá Degli Studi di Cagliari, Cagliari, 09124, Italy, CAP, Faculty of Chemistry and Pharmacy, IBP, and CBQ, UCLV, Santa Clara 54830, Cuba, Unit for Bioinformatics & Connectivity Analysis (UBICA), Institute of Industrial Pharmacy and Department of Organic Chemistry, Faculty of Pharmacy, USC, Santiago de Compostela 15782, Spain, CINVESTAV-LANGEBIO, Irapuato, Guanajuato 36821, México, Caribbean Vitroplants, Santo Domingo 1464, Dominican Republic, and Vascular
| | - Gustavo de la Riva
- Dipartimento Farmaco Chimico Tecnologico, Universitá Degli Studi di Cagliari, Cagliari, 09124, Italy, CAP, Faculty of Chemistry and Pharmacy, IBP, and CBQ, UCLV, Santa Clara 54830, Cuba, Unit for Bioinformatics & Connectivity Analysis (UBICA), Institute of Industrial Pharmacy and Department of Organic Chemistry, Faculty of Pharmacy, USC, Santiago de Compostela 15782, Spain, CINVESTAV-LANGEBIO, Irapuato, Guanajuato 36821, México, Caribbean Vitroplants, Santo Domingo 1464, Dominican Republic, and Vascular
| | - Edrey Rodríguez
- Dipartimento Farmaco Chimico Tecnologico, Universitá Degli Studi di Cagliari, Cagliari, 09124, Italy, CAP, Faculty of Chemistry and Pharmacy, IBP, and CBQ, UCLV, Santa Clara 54830, Cuba, Unit for Bioinformatics & Connectivity Analysis (UBICA), Institute of Industrial Pharmacy and Department of Organic Chemistry, Faculty of Pharmacy, USC, Santiago de Compostela 15782, Spain, CINVESTAV-LANGEBIO, Irapuato, Guanajuato 36821, México, Caribbean Vitroplants, Santo Domingo 1464, Dominican Republic, and Vascular
| | - Aminael Sánchez-Rodríguez
- Dipartimento Farmaco Chimico Tecnologico, Universitá Degli Studi di Cagliari, Cagliari, 09124, Italy, CAP, Faculty of Chemistry and Pharmacy, IBP, and CBQ, UCLV, Santa Clara 54830, Cuba, Unit for Bioinformatics & Connectivity Analysis (UBICA), Institute of Industrial Pharmacy and Department of Organic Chemistry, Faculty of Pharmacy, USC, Santiago de Compostela 15782, Spain, CINVESTAV-LANGEBIO, Irapuato, Guanajuato 36821, México, Caribbean Vitroplants, Santo Domingo 1464, Dominican Republic, and Vascular
| | - Gianni Podda
- Dipartimento Farmaco Chimico Tecnologico, Universitá Degli Studi di Cagliari, Cagliari, 09124, Italy, CAP, Faculty of Chemistry and Pharmacy, IBP, and CBQ, UCLV, Santa Clara 54830, Cuba, Unit for Bioinformatics & Connectivity Analysis (UBICA), Institute of Industrial Pharmacy and Department of Organic Chemistry, Faculty of Pharmacy, USC, Santiago de Compostela 15782, Spain, CINVESTAV-LANGEBIO, Irapuato, Guanajuato 36821, México, Caribbean Vitroplants, Santo Domingo 1464, Dominican Republic, and Vascular
| | - Roberto I. Vazquez-Padrón
- Dipartimento Farmaco Chimico Tecnologico, Universitá Degli Studi di Cagliari, Cagliari, 09124, Italy, CAP, Faculty of Chemistry and Pharmacy, IBP, and CBQ, UCLV, Santa Clara 54830, Cuba, Unit for Bioinformatics & Connectivity Analysis (UBICA), Institute of Industrial Pharmacy and Department of Organic Chemistry, Faculty of Pharmacy, USC, Santiago de Compostela 15782, Spain, CINVESTAV-LANGEBIO, Irapuato, Guanajuato 36821, México, Caribbean Vitroplants, Santo Domingo 1464, Dominican Republic, and Vascular
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Tymoshenko D. Chapter 1 Benzoheteropines with Fused Pyrrole, Furan and Thiophene Rings. ADVANCES IN HETEROCYCLIC CHEMISTRY 2008. [DOI: 10.1016/s0065-2725(07)00001-3] [Citation(s) in RCA: 13] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
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González-Díaz H, Bonet I, Terán C, De Clercq E, Bello R, García MM, Santana L, Uriarte E. ANN-QSAR model for selection of anticancer leads from structurally heterogeneous series of compounds. Eur J Med Chem 2007; 42:580-5. [PMID: 17207560 DOI: 10.1016/j.ejmech.2006.11.016] [Citation(s) in RCA: 48] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2006] [Revised: 11/29/2006] [Accepted: 11/30/2006] [Indexed: 11/28/2022]
Abstract
Developing a model for predicting anticancer activity of any classes of organic compounds based on molecular structure is very important goal for medicinal chemist. Different molecular descriptors can be used to solve this problem. Stochastic molecular descriptors so-called the MARCH-INSIDE approach, shown to be very successful in drug design. Nevertheless, the structural diversity of compounds is so vast that we may need non-linear models such as artificial neural networks (ANN) instead of linear ones. SmartMLP-ANN analysis used to model the anticancer activity of organic compounds has shown high average accuracy of 93.79% (train performance) and predictability of 90.88% (validation performance) for the 8:3-MLP topology with different training and predicting series. This ANN model favourably compares with respect to a previous linear discriminant analysis (LDA) model [H. González-Díaz et al., J. Mol. Model 9 (2003) 395] that showed only 80.49% of accuracy and 79.34% of predictability. The present SmartMLP approach employed shorter training times of only 10h while previous models give accuracies of 70-89% only after 25-46 h of training. In order to illustrate the practical use of the model in bioorganic medicinal chemistry, we report the in silico prediction, and in vitro evaluation of six new synthetic tegafur analogues having IC(50) values in a broad range between 37.1 and 138 microgmL(-1) for leukemia (L1210/0) and human T-lymphocyte (Molt4/C8, CEM/0) cells. Theoretical predictions coincide very well with experimental results.
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Affiliation(s)
- Humberto González-Díaz
- Department of Organic Chemistry, University of Santiago de Compostela, 15782 Santiago de Compostela, Spain.
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González-Díaz H, Olazábal E, Santana L, Uriarte E, González-Díaz Y, Castañedo N. QSAR study of anticoccidial activity for diverse chemical compounds: Prediction and experimental assay of trans-2-(2-nitrovinyl)furan. Bioorg Med Chem 2007; 15:962-8. [PMID: 17081758 DOI: 10.1016/j.bmc.2006.10.032] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2006] [Revised: 10/03/2006] [Accepted: 10/17/2006] [Indexed: 11/21/2022]
Abstract
In this work we report a QSAR model that discriminates between chemically heterogeneous classes of anticoccidial and non-anticoccidial compounds. For this purpose we used the Markovian Chemicals in silico Design (MARCH-INSIDE) approach J. Mol. Mod.2002, 8, 237-245; J. Mol. Mod.2003, 9, 395-407]. Linear discriminant analysis allowed us to fit the discriminant function. This function correctly classifies 86.67% of anticoccidial compounds and 96.23% of inactive compounds in the training series. Overall classification is 94.12%. We validated the model by means of an external predicting series, with 86.96% of global predictability. Remarkably, the present model is based on topological as well as configuration-dependent molecular descriptors. Therefore, the model performs timely calculations and allows discrimination between Z/E and chiral isomers. Finally, to exemplify the use of the model in practice we report the prediction and experimental assay of trans-2-(2-nitrovinyl)furan. It is notable that lesion control was 72.86% at mg/kg of body weight with respect to 60% at 125 mg/kg for amprolium (control drug). The back-projection map for this compound predicts a high level of importance for the double bond and for the nitro group in the trans position. We conclude that the MARCH-INSIDE approach enables the accurate fast track identification of anticoccidial hits. Moreover, trans-2-(2-nitrovinyl)furan seems to be a promising drug for the treatment of coccidiosis.
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Affiliation(s)
- Humberto González-Díaz
- Department of Organic Chemistry & Institute of Industrial Pharmacy, Faculty of Pharmacy, University of Santiago de Compostela, Santiago 15782, Spain.
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50
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Derksen S, Rau O, Schneider P, Schubert-Zsilavecz M, Schneider G. Virtual Screening for PPAR Modulators Using a Probabilistic Neural Network. ChemMedChem 2006; 1:1346-50. [PMID: 17066499 DOI: 10.1002/cmdc.200600166] [Citation(s) in RCA: 17] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Affiliation(s)
- Swetlana Derksen
- Johann Wolfgang Goethe University, Institute of Organic Chemistry and Chemical Biology/ZAFES, Siesmayerstrasse 70, 60323 Frankfurt/Main, Germany
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