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Bhat-Ambure J, Ambure P, Serrano-Candelas E, Galiana-Roselló C, Gil-Martínez A, Guerrero M, Martin M, González-García J, García-España E, Gozalbes R. G4-QuadScreen: A Computational Tool for Identifying Multi-Target-Directed Anticancer Leads against G-Quadruplex DNA. Cancers (Basel) 2023; 15:3817. [PMID: 37568632 PMCID: PMC10416877 DOI: 10.3390/cancers15153817] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Revised: 07/16/2023] [Accepted: 07/21/2023] [Indexed: 08/13/2023] Open
Abstract
The study presents 'G4-QuadScreen', a user-friendly computational tool for identifying MTDLs against G4s. Also, it offers a few hit MTDLs based on in silico and in vitro approaches. Multi-tasking QSAR models were developed using linear discriminant analysis and random forest machine learning techniques for predicting the responses of interest (G4 interaction, G4 stabilization, G4 selectivity, and cytotoxicity) considering the variations in the experimental conditions (e.g., G4 sequences, endpoints, cell lines, buffers, and assays). A virtual screening with G4-QuadScreen and molecular docking using YASARA (AutoDock-Vina) was performed. G4 activities were confirmed via FRET melting, FID, and cell viability assays. Validation metrics demonstrated the high discriminatory power and robustness of the models (the accuracy of all models is ~>90% for the training sets and ~>80% for the external sets). The experimental evaluations showed that ten screened MTDLs have the capacity to selectively stabilize multiple G4s. Three screened MTDLs induced a strong inhibitory effect on various human cancer cell lines. This pioneering computational study serves a tool to accelerate the search for new leads against G4s, reducing false positive outcomes in the early stages of drug discovery. The G4-QuadScreen tool is accessible on the ChemoPredictionSuite website.
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Affiliation(s)
| | - Pravin Ambure
- ProtoQSAR SL, Centro Europeo de Empresas Innovadoras (CEEI), Parque Tecnológico de Valencia, 46980 Valencia, Spain; (P.A.); (E.S.-C.)
| | - Eva Serrano-Candelas
- ProtoQSAR SL, Centro Europeo de Empresas Innovadoras (CEEI), Parque Tecnológico de Valencia, 46980 Valencia, Spain; (P.A.); (E.S.-C.)
| | - Cristina Galiana-Roselló
- Department of Inorganic Chemistry, Institute of Molecular Science, University of Valencia, 46980 Valencia, Spain; (C.G.-R.); (A.G.-M.); (J.G.-G.); (E.G.-E.)
| | - Ariadna Gil-Martínez
- Department of Inorganic Chemistry, Institute of Molecular Science, University of Valencia, 46980 Valencia, Spain; (C.G.-R.); (A.G.-M.); (J.G.-G.); (E.G.-E.)
| | - Mario Guerrero
- Biochemistry and Molecular Biology Unit, Biomedicine Department, Faculty of Medicine and Health Sciences, University of Barcelona, 08036 Barcelona, Spain; (M.G.); (M.M.)
| | - Margarita Martin
- Biochemistry and Molecular Biology Unit, Biomedicine Department, Faculty of Medicine and Health Sciences, University of Barcelona, 08036 Barcelona, Spain; (M.G.); (M.M.)
- Clinical and Experimental Respiratory Immunoallergy (IRCE), Institut d’Investigacions Biomediques August Pi i Sunyer (IDIBAPS), 08036 Barcelona, Spain
| | - Jorge González-García
- Department of Inorganic Chemistry, Institute of Molecular Science, University of Valencia, 46980 Valencia, Spain; (C.G.-R.); (A.G.-M.); (J.G.-G.); (E.G.-E.)
| | - Enrique García-España
- Department of Inorganic Chemistry, Institute of Molecular Science, University of Valencia, 46980 Valencia, Spain; (C.G.-R.); (A.G.-M.); (J.G.-G.); (E.G.-E.)
| | - Rafael Gozalbes
- MolDrug AI Systems SL, c/Olimpia Arozena Torres, 46018 Valencia, Spain;
- ProtoQSAR SL, Centro Europeo de Empresas Innovadoras (CEEI), Parque Tecnológico de Valencia, 46980 Valencia, Spain; (P.A.); (E.S.-C.)
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Halder AK, Ambure P, Perez-Castillo Y, Cordeiro MND. Turning deep-eutectic solvents into value-added products for CO2 capture: A desirability-based virtual screening study. J CO2 UTIL 2022. [DOI: 10.1016/j.jcou.2022.101926] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
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De P, Kar S, Ambure P, Roy K. Prediction reliability of QSAR models: an overview of various validation tools. Arch Toxicol 2022; 96:1279-1295. [PMID: 35267067 DOI: 10.1007/s00204-022-03252-y] [Citation(s) in RCA: 32] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2021] [Accepted: 02/14/2022] [Indexed: 01/20/2023]
Abstract
The reliability of any quantitative structure-activity relationship (QSAR) model depends on multiple aspects such as the accuracy of the input dataset, selection of significant descriptors, the appropriate splitting process of the dataset, statistical tools used, and most notably on the measures of validation. Validation, the most crucial step in QSAR model development, confirms the reliability of the developed QSAR models and the acceptability of each step in the model development. The present review deals with various validation tools that involve multiple techniques that improve the model quality and robustness. The double cross-validation tool helps in building improved quality models using different combinations of the same training set in an inner cross-validation loop. This exhaustive method is also integrated for small datasets (< 40 compounds) in another tool, namely the small dataset modeler tool. The main aim of QSAR researchers is to improve prediction quality by lowering the prediction errors for the query compounds. 'Intelligent' selection of multiple models and consensus predictions integrated in the intelligent consensus predictor tool were found to be more externally predictive than individual models. Furthermore, another tool called Prediction Reliability Indicator was explained to understand the quality of predictions for a true external set. This tool uses a composite scoring technique to identify query compounds as 'good' or 'moderate' or 'bad' predictions. We have also discussed a quantitative read-across tool which predicts a chemical response based on the similarity with structural analogues. The discussed tools are freely available from https://dtclab.webs.com/software-tools or http://teqip.jdvu.ac.in/QSAR_Tools/DTCLab/ and https://sites.google.com/jadavpuruniversity.in/dtc-lab-software/home (for read-across).
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Affiliation(s)
- Priyanka De
- Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, 700032, India
| | - Supratik Kar
- Interdisciplinary Center for Nanotoxicity, Department of Chemistry, Physics and Atmospheric Sciences, Jackson State University, Jackson, MS, 39217, USA
| | | | - Kunal Roy
- Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, 700032, India.
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Ambure P, Ballesteros A, Huertas F, Camilleri P, Barigye SJ, Gozalbes R. Development of Generalized QSAR Models for Predicting Cytotoxicity and Genotoxicity of Metal Oxides Nanoparticles. ACTA ACUST UNITED AC 2020. [DOI: 10.4018/ijqspr.20201001.oa2] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
In recent years, nanomaterials have gained tremendous attention due to their wide variety of industrial applications including food packaging, consumer products, nanomedicines, etc. The fascinating properties of nanoparticles which are responsible for creating several exciting opportunities, however, are also accountable for growing concerns of their toxic effects on humans as well as the environment. Thus, in the present study, the authors have developed generalized models for predicting the cytotoxicity and genotoxicity of seven metal oxide nanoparticles. The models not only take into account the structural features, but also the diverse experimental conditions under which the toxicity of nanoparticles was determined. The diverse experimental conditions were captured in the generalized models using the Box-Jenkins moving average approach. Here, two machine learning techniques, namely, linear discriminant analysis and random forest were utilized to build the final models. Importantly, the validation metrics showed that the developed models have significant discriminatory power.
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Ambure P, Cordeiro MNDS. Importance of Data Curation in QSAR Studies Especially While Modeling Large-Size Datasets. Methods in Pharmacology and Toxicology 2020. [DOI: 10.1007/978-1-0716-0150-1_5] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
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Ambure P, Gajewicz-Skretna A, Cordeiro MNDS, Roy K. New Workflow for QSAR Model Development from Small Data Sets: Small Dataset Curator and Small Dataset Modeler. Integration of Data Curation, Exhaustive Double Cross-Validation, and a Set of Optimal Model Selection Techniques. J Chem Inf Model 2019; 59:4070-4076. [DOI: 10.1021/acs.jcim.9b00476] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Affiliation(s)
- Pravin Ambure
- LAQV@REQUIMTE/Department of Chemistry and Biochemistry, University of Porto, 4169-007 Porto, Portugal
| | - Agnieszka Gajewicz-Skretna
- Laboratory of Environmental Chemometrics, Faculty of Chemistry, University of Gdansk, Gdansk 80-308, Poland
| | - M. Natalia D. S. Cordeiro
- LAQV@REQUIMTE/Department of Chemistry and Biochemistry, University of Porto, 4169-007 Porto, Portugal
| | - Kunal Roy
- Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata 700032, India
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Ambure P, Halder AK, González Díaz H, Cordeiro MNDS. QSAR-Co: An Open Source Software for Developing Robust Multitasking or Multitarget Classification-Based QSAR Models. J Chem Inf Model 2019; 59:2538-2544. [PMID: 31083984 DOI: 10.1021/acs.jcim.9b00295] [Citation(s) in RCA: 49] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Quantitative structure-activity relationships (QSAR) modeling is a well-known computational technique with wide applications in fields such as drug design, toxicity predictions, nanomaterials, etc. However, QSAR researchers still face certain problems to develop robust classification-based QSAR models, especially while handling response data pertaining to diverse experimental and/or theoretical conditions. In the present work, we have developed an open source standalone software "QSAR-Co" (available to download at https://sites.google.com/view/qsar-co ) to setup classification-based QSAR models that allow mining the response data coming from multiple conditions. The software comprises two modules: (1) the Model development module and (2) the Screen/Predict module. This user-friendly software provides several functionalities required for developing a robust multitasking or multitarget classification-based QSAR model using linear discriminant analysis or random forest techniques, with appropriate validation, following the principles set by the Organisation for Economic Co-operation and Development (OECD) for applying QSAR models in regulatory assessments.
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Affiliation(s)
- Pravin Ambure
- LAQV@REQUIMTE, Department of Chemistry and Biochemistry , University of Porto , 4169-007 Porto , Portugal
| | - Amit Kumar Halder
- LAQV@REQUIMTE, Department of Chemistry and Biochemistry , University of Porto , 4169-007 Porto , Portugal
| | - Humbert González Díaz
- Department of Organic Chemistry II , University of Basque Country UPV/EHU , 48940 Leioa , Spain
| | - M Natália D S Cordeiro
- LAQV@REQUIMTE, Department of Chemistry and Biochemistry , University of Porto , 4169-007 Porto , Portugal
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Roy K, Ambure P, Kar S. How Precise Are Our Quantitative Structure-Activity Relationship Derived Predictions for New Query Chemicals? ACS Omega 2018; 3:11392-11406. [PMID: 31459245 PMCID: PMC6645132 DOI: 10.1021/acsomega.8b01647] [Citation(s) in RCA: 71] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/13/2018] [Accepted: 09/06/2018] [Indexed: 05/03/2023]
Abstract
Quantitative structure-activity relationship (QSAR) models have long been used for making predictions and data gap filling in diverse fields including medicinal chemistry, predictive toxicology, environmental fate modeling, materials science, agricultural science, nanoscience, food science, and so forth. Usually a QSAR model is developed based on chemical information of a properly designed training set and corresponding experimental response data while the model is validated using one or more test set(s) for which the experimental response data are available. However, it is interesting to estimate the reliability of predictions when the model is applied to a completely new data set (true external set) even when the new data points are within applicability domain (AD) of the developed model. In the present study, we have categorized the quality of predictions for the test set or true external set into three groups (good, moderate, and bad) based on absolute prediction errors. Then, we have used three criteria [(a) mean absolute error of leave-one-out predictions for 10 most close training compounds for each query molecule; (b) AD in terms of similarity based on the standardization approach; and (c) proximity of the predicted value of the query compound to the mean training response] in different weighting schemes for making a composite score of predictions. It was found that using the most frequently appearing weighting scheme 0.5-0-0.5, the composite score-based categorization showed concordance with absolute prediction error-based categorization for more than 80% test data points while working with 5 different datasets with 15 models for each set derived in three different splitting techniques. These observations were also confirmed with true external sets for another four endpoints suggesting applicability of the scheme to judge the reliability of predictions for new datasets. The scheme has been implemented in a tool "Prediction Reliability Indicator" available at http://dtclab.webs.com/software-tools and http://teqip.jdvu.ac.in/QSAR_Tools/DTCLab/, and the tool is presently valid for multiple linear regression models only.
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Affiliation(s)
- Kunal Roy
- Drug
Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical
Technology, Jadavpur University, Kolkata 700 032, India
- E-mail: and . Phone: +91 98315 94140. Fax: +91-33-2837-1078. URL: http://sites.google.com/site/kunalroyindia/
| | - Pravin Ambure
- Drug
Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical
Technology, Jadavpur University, Kolkata 700 032, India
| | - Supratik Kar
- Interdisciplinary
Center for Nanotoxicity, Department of Chemistry, Physics and Atmospheric
Sciences, Jackson State University, Jackson, Mississippi 39217, United States
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Ambure P, Bhat J, Puzyn T, Roy K. Identifying natural compounds as multi-target-directed ligands against Alzheimer's disease: an in silico approach. J Biomol Struct Dyn 2018; 37:1282-1306. [PMID: 29578387 DOI: 10.1080/07391102.2018.1456975] [Citation(s) in RCA: 34] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
Abstract
Alzheimer's disease (AD) is a multi-factorial disease, which can be simply outlined as an irreversible and progressive neurodegenerative disorder with an unclear root cause. It is a major cause of dementia in old aged people. In the present study, utilizing the structural and biological activity information of ligands for five important and mostly studied vital targets (i.e. cyclin-dependant kinase 5, β-secretase, monoamine oxidase B, glycogen synthase kinase 3β, acetylcholinesterase) that are believed to be effective against AD, we have developed five classification models using linear discriminant analysis (LDA) technique. Considering the importance of data curation, we have given more attention towards the chemical and biological data curation, which is a difficult task especially in case of big data-sets. Thus, to ease the curation process we have designed Konstanz Information Miner (KNIME) workflows, which are made available at http://teqip.jdvu.ac.in/QSAR_Tools/ . The developed models were appropriately validated based on the predictions for experiment derived data from test sets, as well as true external set compounds including known multi-target compounds. The domain of applicability for each classification model was checked based on a confidence estimation approach. Further, these validated models were employed for screening of natural compounds collected from the InterBioScreen natural database ( https://www.ibscreen.com/natural-compounds ). Further, the natural compounds that were categorized as 'actives' in at least two classification models out of five developed models were considered as multi-target leads, and these compounds were further screened using the drug-like filter, molecular docking technique and then thoroughly analyzed using molecular dynamics studies. Finally, the most potential multi-target natural compounds against AD are suggested.
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Key Words
- 3D, three-dimensional
- ACh, acetylcholine
- AChE, acetylcholinesterase
- AD, Alzheimer’s disease
- ADME, absorption, distribution, metabolism, and elimination
- APP, amyloid precursor protein
- AUROC, area under the ROC curve
- Alzheimer’s disease
- Aβ, amyloid beta
- BACE1, beta-APP-cleaving enzyme 1
- CDK5, cyclin-dependant kinase 5
- FDA, food and drug administration
- FN, false negative
- FP, false positive
- GSK-3β, glycogen synthase kinase 3β
- HTVS, high-throughput virtual screening
- InChI, International Chemical Identifier
- KNIME, Konstanz Information Miner
- LBDD, ligand-based drug design
- LDA, linear discriminant analysis
- MAO-B, monoamine oxidase B
- MMGBSA, molecular mechanics/generalized born surface area
- MMPBSA, molecular mechanics/Poisson–Boltzmann surface area
- MMPs, matched molecular pairs
- MSA, molecular spectrum analysis
- MTDLs, multi-target-directed ligands
- NMDA, N-methyl-D-aspartate
- PDB, protein data bank
- PP, posterior probability
- QSAR, quantitative structure–activity relationship
- RMSD, root-mean-square deviation
- ROC, receiver operating curve
- ROS, reactive oxygen species
- SBDD, structure-based drug design
- SDF, structure data format
- SMILES, simplified molecular-input line-entry system
- TN, true negative
- TP, true positive
- big data
- data curation
- linear discriminant analysis
- molecular docking
- molecular dynamics
- multi-target drug design
- natural compounds
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Affiliation(s)
- Pravin Ambure
- a Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology , Jadavpur University , Kolkata 700 032 , India
| | - Jyotsna Bhat
- b Laboratory of Environmental Chemometrics, Faculty of Chemistry , University of Gdańsk , ul. Wita Stwosza 63, Gdańsk 80-308 , Poland
| | - Tomasz Puzyn
- b Laboratory of Environmental Chemometrics, Faculty of Chemistry , University of Gdańsk , ul. Wita Stwosza 63, Gdańsk 80-308 , Poland
| | - Kunal Roy
- a Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology , Jadavpur University , Kolkata 700 032 , India
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Ambure P, Roy K. CADD Modeling of Multi-Target Drugs Against Alzheimer's Disease. Curr Drug Targets 2017; 18:522-533. [PMID: 26343117 DOI: 10.2174/1389450116666150907104855] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2015] [Revised: 08/16/2015] [Accepted: 09/06/2015] [Indexed: 11/22/2022]
Abstract
Alzheimer's disease (AD) is a neurodegenerative disorder that is described by multiple factors linked with the progression of the disease. The currently approved drugs in the market are not capable of curing AD; instead, they merely provide symptomatic relief. Development of multi-target directed ligands (MTDLs) is an emerging strategy for improving the quality of the treatment against complex diseases like AD. Polypharmacology is a branch of pharmaceutical sciences that deals with the MTDL development. In this mini-review, we have summarized and discussed different strategies that are reported in the literature to design MTDLs for AD. Further, we have discussed the role of different in silico techniques and online resources in computer-aided drug discovery (CADD), for designing or identifying MTDLs against AD.
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Affiliation(s)
- Pravin Ambure
- Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata 700032, India
| | - Kunal Roy
- Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata 700032, India
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Ambure P, Roy K. Understanding the structural requirements of cyclic sulfone hydroxyethylamines as hBACE1 inhibitors against Aβ plaques in Alzheimer's disease: a predictive QSAR approach. RSC Adv 2016. [DOI: 10.1039/c6ra04104c] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
Beta (β)-site amyloid precursor protein cleaving enzyme 1 (BACE1) is one of the most important targets in Alzheimer's disease (AD), which is responsible for production and accumulation of beta amyloid (Aβ).
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Affiliation(s)
- Pravin Ambure
- Drug Theoretics and Cheminformatics Laboratory
- Department of Pharmaceutical Technology
- Jadavpur University
- Kolkata 700032
- India
| | - Kunal Roy
- Drug Theoretics and Cheminformatics Laboratory
- Department of Pharmaceutical Technology
- Jadavpur University
- Kolkata 700032
- India
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Brahmachari G, Choo C, Ambure P, Roy K. In vitro evaluation and in silico screening of synthetic acetylcholinesterase inhibitors bearing functionalized piperidine pharmacophores. Bioorg Med Chem 2015; 23:4567-4575. [DOI: 10.1016/j.bmc.2015.06.005] [Citation(s) in RCA: 38] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2015] [Revised: 06/01/2015] [Accepted: 06/02/2015] [Indexed: 10/23/2022]
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Ambure P, Roy K. Exploring Structural Requirements of Imaging Agents Against Aβ Plaques in Alzheimer’s Disease: A QSAR Approach. Comb Chem High Throughput Screen 2015; 18:411-9. [DOI: 10.2174/1386207318666150305124225] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2014] [Revised: 09/14/2014] [Accepted: 11/10/2014] [Indexed: 11/22/2022]
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Ambure P, Roy K. Exploring structural requirements of leads for improving activity and selectivity against CDK5/p25 in Alzheimer's disease: an in silico approach. RSC Adv 2014. [DOI: 10.1039/c3ra46861e] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022] Open
Abstract
A congeneric series of 224 cyclin-dependant kinase 5/p25 (CDK5/p25) inhibitors was exploited to understand the structural requirements for improving activity against CDK5/p25 and selectivity over CDK2.
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Affiliation(s)
- Pravin Ambure
- Drug Theoretics and Cheminformatics Laboratory
- Department of Pharmaceutical Technology
- Jadavpur University
- Kolkata 700032, India
| | - Kunal Roy
- Drug Theoretics and Cheminformatics Laboratory
- Department of Pharmaceutical Technology
- Jadavpur University
- Kolkata 700032, India
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