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Khaouane A, Khaouane L, Ferhat S, Hanini S. Deep Learning for Drug Development: Using CNNs in MIA-QSAR to Predict Plasma Protein Binding of Drugs. AAPS PharmSciTech 2023; 24:232. [PMID: 37964128 DOI: 10.1208/s12249-023-02686-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Accepted: 10/24/2023] [Indexed: 11/16/2023] Open
Abstract
Predicting plasma protein binding (PPB) is crucial in drug development due to its profound impact on drug efficacy and safety. In our study, we employed a convolutional neural network (CNN) as a tool to extract valuable information from the molecular structures of 100 different drugs. These extracted features were then used as inputs for a feedforward network to predict the PPB of each drug. Through this approach, we successfully obtained 10 specific numerical features from each drug's molecular structure, which represent fundamental aspects of their molecular composition. Leveraging the CNN's ability to capture these features significantly improved the precision of our predictions. Our modeling results revealed impressive accuracy, with an R2 train value of 0.89 for the training dataset, a [Formula: see text] of 0.98, a [Formula: see text] of 0.931 for the external validation dataset, and a low cross-validation mean squared error (CV-MSE) of 0.0213. These metrics highlight the effectiveness of our deep learning techniques in the fields of pharmacokinetics and drug development. This study makes a substantial contribution to the expanding body of research exploring the application of artificial intelligence (AI) and machine learning in drug development. By adeptly capturing and utilizing molecular features, our method holds promise for enhancing drug efficacy and safety assessments in pharmaceutical research. These findings underscore the potential for future investigations in this exciting and transformative field.
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Affiliation(s)
- Affaf Khaouane
- Laboratory of Biomaterial and Transport Phenomena (LBMPT), University of Médéa, pole urbain, 26000, Médéa, Algeria.
| | - Latifa Khaouane
- Laboratory of Biomaterial and Transport Phenomena (LBMPT), University of Médéa, pole urbain, 26000, Médéa, Algeria
| | - Samira Ferhat
- Laboratory of Biomaterial and Transport Phenomena (LBMPT), University of Médéa, pole urbain, 26000, Médéa, Algeria
| | - Salah Hanini
- Laboratory of Biomaterial and Transport Phenomena (LBMPT), University of Médéa, pole urbain, 26000, Médéa, Algeria
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Bagri K, Kumar A, Manisha, Kumar P. Computational Studies on Acetylcholinesterase Inhibitors: From Biochemistry to Chemistry. Mini Rev Med Chem 2021; 20:1403-1435. [PMID: 31884928 DOI: 10.2174/1389557520666191224144346] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2019] [Revised: 10/23/2019] [Accepted: 10/28/2019] [Indexed: 11/22/2022]
Abstract
Acetylcholinesterase inhibitors are the most promising therapeutics for Alzheimer's disease treatment as these prevent the loss of acetylcholine and slows the progression of the disease. The drugs approved for the management of Alzheimer's disease by the FDA are acetylcholinesterase inhibitors but are associated with side effects. Consistent and stringent efforts by the researchers with the help of computational methods opened new ways of developing novel molecules with good acetylcholinesterase inhibitory activity. In this manuscript, we reviewed the studies that identified the essential structural features of acetylcholinesterase inhibitors at the molecular level as well as the techniques like molecular docking, molecular dynamics, quantitative structure-activity relationship, virtual screening, and pharmacophore modelling that were used in designing these inhibitors.
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Affiliation(s)
- Kiran Bagri
- Department of Pharmaceutical Sciences, Guru Jambheshwar University of Science & Technology, Hisar 125001, India
| | - Ashwani Kumar
- Department of Pharmaceutical Sciences, Guru Jambheshwar University of Science & Technology, Hisar 125001, India
| | - Manisha
- Department of Pharmaceutical Sciences, Guru Jambheshwar University of Science & Technology, Hisar 125001, India
| | - Parvin Kumar
- Department of Chemistry, Kurukshetra University, Kurukshetra, India
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Kalin TN, Kilic D, Arslan F, Colak O, Altundas A. Synthesis, molecular modeling studies, ADME prediction of arachidonic acid carbamate derivatives, and evaluation of their acetylcholinesterase activity. Drug Dev Res 2019; 81:232-241. [PMID: 31758816 DOI: 10.1002/ddr.21621] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2019] [Revised: 10/09/2019] [Accepted: 10/12/2019] [Indexed: 11/05/2022]
Abstract
In this work, a series of novel anandamide units containing carbamate were designed and synthesized. All the derivatives were evaluated in vitro for their inhibitory potential against the electric eel acetylcholinesterase enzyme (AChE) and showed reversible inhibitions. The compounds 7a, 7d, 7e, and 7f are mixed inhibitors of AChE, while the compounds 7b, 7c, and 7g are uncompetitive (Ki in the range 0.93-8.86 μM). The kinetic studies revealed that compounds 7b, 7c, 7f, and 7g inhibit considerably AChE activity. Molecular docking analyses were made to evaluate the binding type and interactions of the synthesized compounds to the ligand-binding site of hAChE. It was observed that the docking results were in parallel with the in vitro results. The adsorption, distribution, metabolism, and excretion properties were computed for the compounds, and were found within the acceptable range. This study suggests the compounds 7b, 7c, 7f, and 7g identified as novel reversible AChE inhibitors may be useful lead compounds for the treatment of Alzheimer's disease.
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Affiliation(s)
- Tugce N Kalin
- Department of Chemistry, Faculty of Science, Gazi University, Ankara, Turkey
| | - Deryanur Kilic
- Department of Chemistry, Faculty of Science, Ataturk University, Erzurum, Turkey
| | - Fatma Arslan
- Department of Chemistry, Faculty of Science, Gazi University, Ankara, Turkey
| | - Ozlem Colak
- Department of Chemistry, Faculty of Science, Gazi University, Ankara, Turkey
| | - Aliye Altundas
- Department of Chemistry, Faculty of Science, Gazi University, Ankara, Turkey
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Aug-MIA-QSAR based strategy in bioactivity prediction of a series of flavonoid derivatives as HIV-1 inhibitors. J Theor Biol 2019; 469:18-24. [PMID: 30826336 DOI: 10.1016/j.jtbi.2019.02.019] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2018] [Revised: 02/21/2019] [Accepted: 02/27/2019] [Indexed: 12/13/2022]
Abstract
Multivariate image analysis-quantitative structure-activity relationship (MIA-QSAR) is a simple and quite accessible QSAR method for predicting biological activities of compounds based on two-dimensional image analysis. Aug-MIA-QSAR is a modified version of multivariate image analysis, where the atoms in 2D chemical structures were augmented (labelled by assigning specific colours). This study focuses on efficiently constructing such prediction models using a dataset of flavonoid derivatives possessing human immunodeficiency virus - 1 inhibition. The models were constructed by partial least square regression using non-linear iterative partial least square (NIPALS) algorithm and linearized by identifying an optimum number of seven latent variables. A leave-one-out cross validation (LOOCV) helped to verify the actual and predicted data. The two multivariate methods were compared and analysed to identify the most suitable method.
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Lagunin AA, Geronikaki A, Eleftheriou P, Pogodin PV, Zakharov AV. Rational Use of Heterogeneous Data in Quantitative Structure-Activity Relationship (QSAR) Modeling of Cyclooxygenase/Lipoxygenase Inhibitors. J Chem Inf Model 2019; 59:713-730. [PMID: 30688458 DOI: 10.1021/acs.jcim.8b00617] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
Numerous studies have been published in recent years with acceptable quantitative structure-activity relationship (QSAR) modeling based on heterogeneous data. In many cases, the training sets for QSAR modeling were constructed from compounds tested by different biological assays, contradicting the opinion that QSAR modeling should be based on the data measured by a single protocol. We attempted to develop approaches that help to determine how heterogeneous data should be used for the creation of QSAR models on the basis of different sets of compounds tested by different experimental methods for the same target and the same endpoint. To this end, more than 100 QSAR models for the IC50 values of ligands interacting with cyclooxygenase 1,2 (COX) and seed lipoxygenase (LOX), obtained from ChEMBL database were created using the GUSAR software. The QSAR models were tested on the external set, including 26 new thiazolidinone derivatives, which were experimentally tested for COX-1,2/LOX inhibition. The IC50 values of the derivatives varied from 89 μM to 26 μM for LOX, from 200 μM to 0.018 μM for COX-1, and from 210 μM to 1 μM for COX-2. This study showed that the accuracy of the models is dependent on the distribution of IC50 values of low activity compounds in the training sets. In the most cases, QSAR models created based on the combined training sets had advantages in comparison with QSAR models, based on a single publication. We introduced a new method of combination of quantitative data from different experimental studies based on the data of reference compounds, which was called "scaling".
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Affiliation(s)
- Alexey A Lagunin
- Pirogov Russian National Research Medical University , Ostrovitianov str. 1 , Moscow , 117997 , Russia
- Institute of Biomedical Chemistry , Pogodinskaya Str., 10/8 , Moscow , 119121 , Russia
| | - Athina Geronikaki
- School of Pharmacy , Aristotle University , Thessaloniki , 54124 , Greece
| | - Phaedra Eleftheriou
- School of Health and Medical Care , Alexander Technological Educational Institute of Thessaloniki , Thessaloniki , 57400 , Greece
| | - Pavel V Pogodin
- Institute of Biomedical Chemistry , Pogodinskaya Str., 10/8 , Moscow , 119121 , Russia
| | - Alexey V Zakharov
- National Center for Advancing Translational Sciences (NCATS) , National Institutes of Health , Rockville , Maryland 20850 , United States
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Makhouri FR, Ghasemi JB. In Silico Studies in Drug Research Against Neurodegenerative Diseases. Curr Neuropharmacol 2018; 16:664-725. [PMID: 28831921 PMCID: PMC6080098 DOI: 10.2174/1570159x15666170823095628] [Citation(s) in RCA: 37] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2017] [Revised: 07/24/2017] [Accepted: 08/16/2017] [Indexed: 01/14/2023] Open
Abstract
Background Neurodegenerative diseases such as Alzheimer's disease (AD), amyotrophic lateral sclerosis, Parkinson's disease (PD), spinal cerebellar ataxias, and spinal and bulbar muscular atrophy are described by slow and selective degeneration of neurons and axons in the central nervous system (CNS) and constitute one of the major challenges of modern medicine. Computer-aided or in silico drug design methods have matured into powerful tools for reducing the number of ligands that should be screened in experimental assays. Methods In the present review, the authors provide a basic background about neurodegenerative diseases and in silico techniques in the drug research. Furthermore, they review the various in silico studies reported against various targets in neurodegenerative diseases, including homology modeling, molecular docking, virtual high-throughput screening, quantitative structure activity relationship (QSAR), hologram quantitative structure activity relationship (HQSAR), 3D pharmacophore mapping, proteochemometrics modeling (PCM), fingerprints, fragment-based drug discovery, Monte Carlo simulation, molecular dynamic (MD) simulation, quantum-mechanical methods for drug design, support vector machines, and machine learning approaches. Results Detailed analysis of the recently reported case studies revealed that the majority of them use a sequential combination of ligand and structure-based virtual screening techniques, with particular focus on pharmacophore models and the docking approach. Conclusion Neurodegenerative diseases have a multifactorial pathoetiological origin, so scientists have become persuaded that a multi-target therapeutic strategy aimed at the simultaneous targeting of multiple proteins (and therefore etiologies) involved in the development of a disease is recommended in future.
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Affiliation(s)
| | - Jahan B Ghasemi
- Chemistry Department, Faculty of Sciences, University of Tehran, Tehran, Iran
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Ghaemian P, Shayanfar A. Image-based QSAR Model for the Prediction of P-gp Inhibitory Activity of Epigallocatechin and Gallocatechin Derivatives. Curr Comput Aided Drug Des 2018; 15:212-224. [PMID: 30280673 DOI: 10.2174/1573409914666181003152042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2017] [Revised: 09/09/2018] [Accepted: 09/28/2018] [Indexed: 11/22/2022]
Abstract
BACKGROUND Permeability glycoprotein (P-gp) is one of the cell membrane proteins that can push some drugs out of the cell causing drug tolerance and its inhibition can prevent drug resistance. OBJECTIVE In this study, we used image-based Quantitative Structure-Activity Relationship (QSAR) models to predict the P-gp inhibitory activity of epigallocatechin and gallocatechin derivatives. METHODS The 2D-chemical structures and their P-gp inhibitory activity were taken from literature. The pixels of images and their Principal Components (PCs) were calculated using MATLAB software. Principle Component Regression (PCR), Artificial Neural Network (ANN) and Support Vector Machine (SVM) approaches were used to develop QSAR models. Statistical parameters included the leave one out cross-validated correlation coefficient (q2) for internal validation of the models and R2 of test set, Root Mean Square Error (RMSE) and Concordance Correlation Coefficient (CCC) were applied for external validation. RESULTS Six PCs from image analysis method were selected by stepwise regression for developing linear and non-linear models. Non-linear models i.e. ANN (with the R2 of 0.80 for test set) were chosen as the best for the established QSAR models. CONCLUSION According to the result of the external validation, ANN model based on image analysis method can predict the P-gp inhibitory activity of epigallocatechin and gallocatechin derivatives better than the PCR and SVM models.
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Affiliation(s)
- Paria Ghaemian
- Biotechnology Research Center and Faculty of Pharmacy, Tabriz University of Medical Sciences, Tabriz, Iran.,Student Research Committee, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Ali Shayanfar
- Pharmaceutical Analysis Research Center and Faculty of Pharmacy, Tabriz University of Medical Sciences, Tabriz, Iran
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Muthukumaran P, Rajiniraja M. MIA-QSAR based model for bioactivity prediction of flavonoid derivatives as acetylcholinesterase inhibitors. J Theor Biol 2018; 459:103-110. [PMID: 30267791 DOI: 10.1016/j.jtbi.2018.09.030] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2018] [Revised: 09/21/2018] [Accepted: 09/25/2018] [Indexed: 01/02/2023]
Abstract
Alzheimer's disease is a common form of dementia, which considered to be a major health concern. Multivariate Image Analysis - Quantitative Structure-Activity Relationship (MIA-QSAR) is a simple and quite accessible QSAR method for predicting biological activities of unstudied compounds based on 2D image analysis. This study focuses on constructing an efficient QSAR model using a dataset of 52 flavonoid derivatives (substituted with amino-alkyl, alkoxy, alkyl-amines, and piperidine groups) as active compounds against acetylcholinesterase inhibitors (AChE). The model was constructed by PLS (Partial Least Square) using NIPALS (Non-Linear iterative Partial Least Square) algorithm. The comparable values obtained from calibration of training set using five latent variables (R2 = 0.955) and external validation of test set (Q2 = 0.948) confirmed the precision in the prediction of bioactivities for the set of flavonoid derivatives used in designing the model.
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Affiliation(s)
- Panchaksaram Muthukumaran
- Department of Biotechnology, School of Biosciences and Technology, Vellore Institute of Technology (VIT) University, Vellore, Tamil Nadu 632014, India
| | - Muniyan Rajiniraja
- Department of Biotechnology, School of Biosciences and Technology, Vellore Institute of Technology (VIT) University, Vellore, Tamil Nadu 632014, India.
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Wang T, Yuan XS, Wu MB, Lin JP, Yang LR. The advancement of multidimensional QSAR for novel drug discovery - where are we headed? Expert Opin Drug Discov 2017; 12:769-784. [PMID: 28562095 DOI: 10.1080/17460441.2017.1336157] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
INTRODUCTION The Multidimensional quantitative structure-activity relationship (multidimensional-QSAR) method is one of the most popular computational methods employed to predict interesting biochemical properties of existing or hypothetical molecules. With continuous progress, the QSAR method has made remarkable success in various fields, such as medicinal chemistry, material science and predictive toxicology. Areas covered: In this review, the authors cover the basic elements of multidimensional -QSAR including model construction, validation and application. It includes and emphasizes the very recent developments of multidimensional -QSAR such as: HQSAR, G-QSAR, MIA-QSAR, multi-target QSAR. The advantages and disadvantages of each method are also discussed and typical examples of their application are detailed. Expert opinion: Although there are defects in multidimensional-QSAR modeling, it is still of enormous help to chemists, biologists and other researchers in various fields. In the authors' opinion, the latest more precise and feasible QSAR models should be further developed by integrating new descriptors, algorithms and other relevant computational techniques. Apart from being applied in traditional fields (e.g. lead optimization and predictive risk assessment), QSAR should be used more widely as a routine method in other emerging research fields including the modeling of nanoparticles(NPs), mixture toxicity and peptides.
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Affiliation(s)
- Tao Wang
- a School of biological science , Jining Medical University , Jining , China.,b Department of Chemical and Biological Engineering , Zhejiang University , Hangzhou , China
| | - Xin-Song Yuan
- b Department of Chemical and Biological Engineering , Zhejiang University , Hangzhou , China
| | - Mian-Bin Wu
- b Department of Chemical and Biological Engineering , Zhejiang University , Hangzhou , China
| | - Jian-Ping Lin
- b Department of Chemical and Biological Engineering , Zhejiang University , Hangzhou , China
| | - Li-Rong Yang
- b Department of Chemical and Biological Engineering , Zhejiang University , Hangzhou , China
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Shayanfar S, Shayanfar A, Ghandadi M. Image-Based Analysis to Predict the Activity of Tariquidar Analogs as P-Glycoprotein Inhibitors: The Importance of External Validation. Arch Pharm (Weinheim) 2015; 349:124-31. [DOI: 10.1002/ardp.201500333] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2015] [Revised: 11/23/2015] [Accepted: 11/26/2015] [Indexed: 11/05/2022]
Affiliation(s)
- Shadi Shayanfar
- Biotechnology Research Center; Tabriz University of Medical Sciences; Tabriz Iran
- Faculty of Pharmacy, Student Research Committee; Tabriz University of Medical Sciences; Tabriz Iran
| | - Ali Shayanfar
- Drug Applied Research Center and Faculty of Pharmacy; Tabriz University of Medical Sciences; Tabriz Iran
- Pharmaceutical Analysis Research Center; Tabriz University of Medical Sciences; Tabriz Iran
| | - Morteza Ghandadi
- Department of Pharmaceutical Biotechnology, Faculty of Pharmacy; Mashhad University of Medical Sciences; Mashhad Iran
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Barigye SJ, Freitas MP. Is molecular alignment an indispensable requirement in the MIA-QSAR method? J Comput Chem 2015; 36:1748-55. [DOI: 10.1002/jcc.23992] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2015] [Revised: 05/18/2015] [Accepted: 06/07/2015] [Indexed: 11/08/2022]
Affiliation(s)
- Stephen J. Barigye
- Department of Chemistry; Federal University of Lavras; P.O. Box 3037 Lavras, Minas Gerais 37200-000 Brazil
| | - Matheus P. Freitas
- Department of Chemistry; Federal University of Lavras; P.O. Box 3037 Lavras, Minas Gerais 37200-000 Brazil
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Ambure P, Roy K. Advances in quantitative structure–activity relationship models of anti-Alzheimer’s agents. Expert Opin Drug Discov 2014; 9:697-723. [DOI: 10.1517/17460441.2014.909404] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
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