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Gok EC, Yildirim MO, Eren E, Oksuz AU. Comparison of Machine Learning Models on Performance of Single- and Dual-Type Electrochromic Devices. ACS OMEGA 2020; 5:23257-23267. [PMID: 32954176 PMCID: PMC7495761 DOI: 10.1021/acsomega.0c03048] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/24/2020] [Accepted: 08/10/2020] [Indexed: 05/04/2023]
Abstract
This study shows that the model fitting based on machine learning (ML) from experimental data can successfully predict the electrochromic characteristics of single- and dual-type flexible electrochromic devices (ECDs) by using tungsten trioxide (WO3) and WO3/vanadium pentoxide (V2O5), respectively. Seven different regression methods were used for experimental observations, which belong to single and dual ECDs where 80% percent was used as training data and the remaining was taken as testing data. Among the seven different regression methods, K-nearest neighbor (KNN) achieves the best results with higher coefficient of determination (R 2) score and lower root-mean-squared error (RMSE) for the bleaching state of ECDs. Furthermore, higher R 2 score and lower RMSE for the coloration state of ECDs were achieved with Gaussian process regressor. The robustness result of the ML modeling demonstrates the reliability of prediction outcomes. These results can be proposed as promising models for different energy-saving flexible electronic systems.
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Affiliation(s)
- Elif Ceren Gok
- Department
of Industrial Engineering, Engineering Faculty, Suleyman Demirel University, 32260 Isparta, Turkey
| | - Murat Onur Yildirim
- Department
of Industrial Engineering, Engineering Faculty, Suleyman Demirel University, 32260 Isparta, Turkey
| | - Esin Eren
- Department
of Energy Technologies, Innovative Technologies Application and Research
Center, Suleyman Demirel University, 32260 Isparta, Turkey
- Department
of Chemistry, Faculty of Arts and Science, Suleyman Demirel University, 32260 Isparta, Turkey
| | - Aysegul Uygun Oksuz
- Department
of Chemistry, Faculty of Arts and Science, Suleyman Demirel University, 32260 Isparta, Turkey
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Prediction model of human ABCC2/MRP2 efflux pump inhibitors: a QSAR study. Mol Divers 2020; 25:741-751. [PMID: 32048150 DOI: 10.1007/s11030-020-10047-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2019] [Accepted: 02/03/2020] [Indexed: 10/25/2022]
Abstract
The overexpression of ABCC2/MRP2, an ATP-binding cassette transporter, contributes to multidrug resistance in cancer cells. In this study, a quantitative structure-activity relationship (QSAR) analysis on ABCC2 inhibitors has been carried out, aiming to establish a computational prediction model for ABCC2 modulators. Seven classification models and two regression models were built by SONNIA 4.2, and two other regression models were built by MOE 2008.10 based on a data set comprising 372 compounds collected from 16 relevant publications. The CPG-C iABCC2 model for classifying ABCC2 inhibitors has total accuracy of 0.88 and Matthews correlation coefficient MCC = 0.75. The CPG-C iEG model for classifying ABCC2 inhibitors (substrate EG: β-estradiol 17-β-D-glucuronide) has total accuracy of 0.91 and MCC = 0.82. The regression model PLS EG-IC50 for predicting ABCC2 inhibitors (substrate EG) gave root-mean-square error RMSE = 0.26, Q2 = 0.73 and [Formula: see text]. The regression model PLS CDCF-IC50 for predicting ABCC2 inhibitors [substrate CDCF: 5(6)-carboxy-2',7'-dichlorofluorescein] gave RMSE = 0.31, Q2 = 0.74 and [Formula: see text]. Four 2D-QSAR models were applied to 1661 compounds, with results indicating 369 compounds having the ability to reverse the efflux of both EG and CDCF by ABCC2, 152 among them having IC50 < 100 µM.
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Mayr F, Vieider C, Temml V, Stuppner H, Schuster D. Open-Access Activity Prediction Tools for Natural Products. Case Study: hERG Blockers. PROGRESS IN THE CHEMISTRY OF ORGANIC NATURAL PRODUCTS 2019; 110:177-238. [PMID: 31621014 DOI: 10.1007/978-3-030-14632-0_6] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Interference with the hERG potassium ion channel may cause cardiac arrhythmia and can even lead to death. Over the last few decades, several drugs, already on the market, and many more investigational drugs in various development stages, have had to be discontinued because of their hERG-associated toxicity. To recognize potential hERG activity in the early stages of drug development, a wide array of computational tools, based on different principles, such as 3D QSAR, 2D and 3D similarity, and machine learning, have been developed and are reviewed in this chapter. The various available prediction tools Similarity Ensemble Approach, SuperPred, SwissTargetPrediction, HitPick, admetSAR, PASSonline, Pred-hERG, and VirtualToxLab™ were used to screen a dataset of known hERG synthetic and natural product actives and inactives to quantify and compare their predictive power. This contribution will allow the reader to evaluate the suitability of these computational methods for their own related projects. There is an unmet need for natural product-specific prediction tools in this field.
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Affiliation(s)
- Fabian Mayr
- Institute of Pharmacy/Pharmacognosy, University of Innsbruck, Innsbruck, Austria
- Institute of Pharmacy/Pharmaceutical Chemistry, University of Innsbruck, Innsbruck, Austria
| | - Christian Vieider
- Institute of Pharmacy/Pharmaceutical Chemistry, University of Innsbruck, Innsbruck, Austria
| | - Veronika Temml
- Institute of Pharmacy/Pharmacognosy, University of Innsbruck, Innsbruck, Austria
| | - Hermann Stuppner
- Institute of Pharmacy/Pharmacognosy, University of Innsbruck, Innsbruck, Austria
| | - Daniela Schuster
- Institute of Pharmacy/Pharmaceutical Chemistry, University of Innsbruck, Innsbruck, Austria.
- Department of Pharmaceutical and Medicinal Chemistry, Institute of Pharmacy, Paracelsus Medical University Salzburg, Salzburg, Austria.
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Munawar S, Windley MJ, Tse EG, Todd MH, Hill AP, Vandenberg JI, Jabeen I. Experimentally Validated Pharmacoinformatics Approach to Predict hERG Inhibition Potential of New Chemical Entities. Front Pharmacol 2018; 9:1035. [PMID: 30333745 PMCID: PMC6176658 DOI: 10.3389/fphar.2018.01035] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2018] [Accepted: 08/27/2018] [Indexed: 12/17/2022] Open
Abstract
The hERG (human ether-a-go-go-related gene) encoded potassium ion (K+) channel plays a major role in cardiac repolarization. Drug-induced blockade of hERG has been a major cause of potentially lethal ventricular tachycardia termed Torsades de Pointes (TdPs). Therefore, we presented a pharmacoinformatics strategy using combined ligand and structure based models for the prediction of hERG inhibition potential (IC50) of new chemical entities (NCEs) during early stages of drug design and development. Integrated GRid-INdependent Descriptor (GRIND) models, and lipophilic efficiency (LipE), ligand efficiency (LE) guided template selection for the structure based pharmacophore models have been used for virtual screening and subsequent hERG activity (pIC50) prediction of identified hits. Finally selected two hits were experimentally evaluated for hERG inhibition potential (pIC50) using whole cell patch clamp assay. Overall, our results demonstrate a difference of less than ±1.6 log unit between experimentally determined and predicted hERG inhibition potential (IC50) of the selected hits. This revealed predictive ability and robustness of our models and could help in correctly rank the potency order (lower μM to higher nM range) against hERG.
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Affiliation(s)
- Saba Munawar
- Research Center for Modeling and Simulation, National University of Science and Technology, Islamabad, Pakistan.,Victor Chang Cardiac Research Institute, Sydney, NSW, Australia
| | | | - Edwin G Tse
- School of Chemistry, The University of Sydney, Sydney, NSW, Australia
| | - Matthew H Todd
- School of Chemistry, The University of Sydney, Sydney, NSW, Australia
| | - Adam P Hill
- Victor Chang Cardiac Research Institute, Sydney, NSW, Australia
| | | | - Ishrat Jabeen
- Research Center for Modeling and Simulation, National University of Science and Technology, Islamabad, Pakistan
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Fang Y, Cao W, Xia M, Pan S, Xu X. Study of Structure and Permeability Relationship of Flavonoids in Caco-2 Cells. Nutrients 2017; 9:nu9121301. [PMID: 29186068 PMCID: PMC5748751 DOI: 10.3390/nu9121301] [Citation(s) in RCA: 51] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2017] [Revised: 11/22/2017] [Accepted: 11/24/2017] [Indexed: 01/23/2023] Open
Abstract
Flavonoids exhibit a broad range of biological activities. However, poor absorption of some flavonoids is a major limitation for use of flavonoids as nutraceuticals. To investigate the structure requirements for flavonoids intestinal absorption, transepithelial transport and cellular accumulation (CA) of 30 flavonoids were determined using the Caco-2 cell monolayer. The bilateral permeation of five types of flavonoids followed the order: flavanones ≥ isoflavones > flavones ≥ chalcones > flavonols. The concentration of flavonoids accumulated in cells did not correlate with cell penetration since the correlation coefficient between the apparent permeability coefficient (Papp) and their corresponding CA was poor (R2 < 0.3). Most flavonoids exhibited a ratio of 0.8–1.5 for Papp A to B/Papp B to A, suggesting passive diffusion pathways. However, luteolin, morin and taxifolin may involve the efflux mechanisms. The quantitative structure-permeability relationship (QSPR) study demonstrated that the intestinal absorption of flavonoids can be related to atomic charges on carbon 3′ (QC3′), molecule surface area (SlogP_V3), balance between the center of mass and position of hydrophobic region (vsurf_ID1) and solvation energy of flavonoids (E_sol). These results provide useful information for initially screening of flavonoids with high intestinal absorption.
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Affiliation(s)
- Yajing Fang
- Key Laboratory of Environment Correlative Dietology, Ministry of Education, Huazhong Agricultural University, Wuhan 430070, China.
| | - Weiwei Cao
- Key Laboratory of Environment Correlative Dietology, Ministry of Education, Huazhong Agricultural University, Wuhan 430070, China.
| | - Mengmeng Xia
- Key Laboratory of Environment Correlative Dietology, Ministry of Education, Huazhong Agricultural University, Wuhan 430070, China.
| | - Siyi Pan
- Key Laboratory of Environment Correlative Dietology, Ministry of Education, Huazhong Agricultural University, Wuhan 430070, China.
| | - Xiaoyun Xu
- Key Laboratory of Environment Correlative Dietology, Ministry of Education, Huazhong Agricultural University, Wuhan 430070, China.
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Kratz JM, Grienke U, Scheel O, Mann SA, Rollinger JM. Natural products modulating the hERG channel: heartaches and hope. Nat Prod Rep 2017; 34:957-980. [PMID: 28497823 PMCID: PMC5708533 DOI: 10.1039/c7np00014f] [Citation(s) in RCA: 42] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
This review covers natural products modulating the hERG potassium channel. Risk assessment strategies, structural features of blockers, and the duality target/antitarget are discussed.
Covering: 1996–December 2016 The human Ether-à-go-go Related Gene (hERG) channel is a voltage-gated potassium channel playing an essential role in the normal electrical activity in the heart. It is involved in the repolarization and termination of action potentials in excitable cardiac cells. Mutations in the hERG gene and hERG channel blockage by small molecules are associated with increased risk of fatal arrhythmias. Several drugs have been withdrawn from the market due to hERG channel-related cardiotoxicity. Moreover, as a result of its notorious ligand promiscuity, this ion channel has emerged as an important antitarget in early drug discovery and development. Surprisingly, the hERG channel blocking profile of natural compounds present in frequently consumed botanicals (i.e. dietary supplements, spices, and herbal medicinal products) is not routinely assessed. This comprehensive review will address these issues and provide a critical compilation of hERG channel data for isolated natural products and extracts over the past two decades (1996–2016). In addition, the review will provide (i) a solid basis for the molecular understanding of the physiological functions of the hERG channel, (ii) the translational potential of in vitro/in vivo results to cardiotoxicity in humans, (iii) approaches for the identification of hERG channel blockers from natural sources, (iv) future perspectives for cardiac safety guidelines and their applications within phytopharmaceuticals and dietary supplements, and (v) novel applications of hERG channel modulation (e.g. as a drug target).
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Affiliation(s)
- Jadel M Kratz
- Department of Pharmacognosy, Faculty of Life Sciences, University of Vienna, Althanstraße 14, 1090 Vienna, Austria.
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8
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Ngo TD, Tran TD, Le MT, Thai KM. Machine learning-, rule- and pharmacophore-based classification on the inhibition of P-glycoprotein and NorA. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2016; 27:747-780. [PMID: 27667641 DOI: 10.1080/1062936x.2016.1233137] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/05/2016] [Accepted: 09/02/2016] [Indexed: 06/06/2023]
Abstract
The efflux pumps P-glycoprotein (P-gp) in humans and NorA in Staphylococcus aureus are of great interest for medicinal chemists because of their important roles in multidrug resistance (MDR). The high polyspecificity as well as the unavailability of high-resolution X-ray crystal structures of these transmembrane proteins lead us to combining ligand-based approaches, which in the case of this study were machine learning, perceptual mapping and pharmacophore modelling. For P-gp inhibitory activity, individual models were developed using different machine learning algorithms and subsequently combined into an ensemble model which showed a good discrimination between inhibitors and noninhibitors (acctrain-diverse = 84%; accinternal-test = 92% and accexternal-test = 100%). For ligand promiscuity between P-gp and NorA, perceptual maps and pharmacophore models were generated for the detection of rules and features. Based on these in silico tools, hit compounds for reversing MDR were discovered from the in-house and DrugBank databases through virtual screening in an attempt to restore drug sensitivity in cancer cells and bacteria.
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Affiliation(s)
- T-D Ngo
- a Department of Medicinal Chemistry, Faculty of Pharmacy , University of Medicine and Pharmacy at Ho Chi Minh City , Viet Nam
| | - T-D Tran
- a Department of Medicinal Chemistry, Faculty of Pharmacy , University of Medicine and Pharmacy at Ho Chi Minh City , Viet Nam
| | - M-T Le
- a Department of Medicinal Chemistry, Faculty of Pharmacy , University of Medicine and Pharmacy at Ho Chi Minh City , Viet Nam
| | - K-M Thai
- a Department of Medicinal Chemistry, Faculty of Pharmacy , University of Medicine and Pharmacy at Ho Chi Minh City , Viet Nam
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Thai KM, Huynh NT, Ngo TD, Mai TT, Nguyen TH, Tran TD. Three- and four-class classification models for P-glycoprotein inhibitors using counter-propagation neural networks. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2015; 26:139-163. [PMID: 25588022 DOI: 10.1080/1062936x.2014.995701] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
P-glycoprotein (P-gp) is an ATP binding cassette (ABC) transporter that helps to protect several certain human organs from xenobiotic exposure. This efflux pump is also responsible for multi-drug resistance (MDR), an issue of the chemotherapy approach in the fight against cancer. Therefore, the discovery of P-gp inhibitors is considered one of the most popular strategies to reverse MDR in tumour cells and to improve therapeutic efficacy of commonly used cytotoxic drugs. Until now, several generations of P-gp inhibitors have been developed but they have largely failed in preclinical and clinical studies due to lack of selectivity, poor solubility and severe pharmacokinetic interactions. In this study, three models (SION, SIO, SIN) to classify specific 'true' P-gp inhibitors as well as three other models (CPBN, CPB1, CPN) to distinguish between P-gp inhibitors, CYP 3A inhibitors and co-inhibitors of these proteins with rather high accuracy values for the test set and the external set were generated based on counter-propagation neural networks (CPG-NN). Such three and four-class classification models helped provide more information about the bioactivities of compounds not only on one target (P-gp), but also on a combination of multiple targets (P-gp, CYP 3A).
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Affiliation(s)
- K-M Thai
- a Department of Medicinal Chemistry, School of Pharmacy , University of Medicine and Pharmacy at Ho Chi Minh City , Ho Chi Minh City , Viet Nam
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Mitchell JBO. Machine learning methods in chemoinformatics. WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL MOLECULAR SCIENCE 2014; 4:468-481. [PMID: 25285160 PMCID: PMC4180928 DOI: 10.1002/wcms.1183] [Citation(s) in RCA: 233] [Impact Index Per Article: 23.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Machine learning algorithms are generally developed in computer science or adjacent disciplines and find their way into chemical modeling by a process of diffusion. Though particular machine learning methods are popular in chemoinformatics and quantitative structure-activity relationships (QSAR), many others exist in the technical literature. This discussion is methods-based and focused on some algorithms that chemoinformatics researchers frequently use. It makes no claim to be exhaustive. We concentrate on methods for supervised learning, predicting the unknown property values of a test set of instances, usually molecules, based on the known values for a training set. Particularly relevant approaches include Artificial Neural Networks, Random Forest, Support Vector Machine, k-Nearest Neighbors and naïve Bayes classifiers.
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11
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Wang Z, Mussa HY, Lowe R, Glen RC, Yan A. Probability Based hERG Blocker Classifiers. Mol Inform 2012; 31:679-85. [DOI: 10.1002/minf.201200011] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2012] [Accepted: 07/03/2012] [Indexed: 11/11/2022]
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Thai KM, Bui QH, Tran TD, Huynh TNP. QSAR modeling on benzo[c]phenanthridine analogues as topoisomerase I inhibitors and anti-cancer agents. Molecules 2012; 17:5690-712. [PMID: 22580401 PMCID: PMC6268722 DOI: 10.3390/molecules17055690] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2012] [Revised: 04/25/2012] [Accepted: 05/04/2012] [Indexed: 12/28/2022] Open
Abstract
Benzo[c]phenanthridine (BCP) derivatives were identified as topoisomerase I (TOP-I) targeting agents with pronounced antitumor activity. In this study, hologram-QSAR, 2D-QSAR and 3D-QSAR models were developed for BCPs on topoisomerase I inbibitory activity and cytotoxicity against seven tumor cell lines including RPMI8402, CPT-K5, P388, CPT45, KB3-1, KBV-1and KBH5.0. The hologram, 2D, and 3D-QSAR models were obtained with the square of correlation coefficient R² = 0.58-0.77, the square of the crossvalidation coefficient q² = 0.41-0.60 as well as the external set's square of predictive correlation coefficient r² = 0.5-0.80. Moreover, the assessment method based on reliability test with confidence level of 95% was used to validate the predictive power of QSAR models and to prevent over-fitting phenomenon of classical QSAR models. Our QSAR model could be applied to design new analogues of BCPs with higher antitumor and topoisomerase I inhibitory activity.
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Affiliation(s)
- Khac-Minh Thai
- Department of Medicinal Chemistry, School of Pharmacy, University of Medicine and Pharmacy, 41 Dinh Tien Hoang St., Dist. 1, Ho Chi Minh City, Vietnam.
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Thai KM, Nguyen TQ, Ngo TD, Tran TD, Huynh TNP. A support vector machine classification model for benzo[c]phenathridine analogues with toposiomerase-I inhibitory activity. Molecules 2012; 17:4560-82. [PMID: 22510606 PMCID: PMC6268465 DOI: 10.3390/molecules17044560] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2012] [Revised: 04/08/2012] [Accepted: 04/10/2012] [Indexed: 11/18/2022] Open
Abstract
Benzo[c]phenanthridine (BCP) derivatives were identified as topoisomerase I (TOP-I) targeting agents with pronounced antitumor activity. In this study, a support vector machine model was performed on a series of 73 analogues to classify BCP derivatives according to TOP-I inhibitory activity. The best SVM model with total accuracy of 93% for training set was achieved using a set of 7 descriptors identified from a large set via a random forest algorithm. Overall accuracy of up to 87% and a Matthews coefficient correlation (MCC) of 0.71 were obtained after this SVM classifier was validated internally by a test set of 15 compounds. For two external test sets, 89% and 80% BCP compounds, respectively, were correctly predicted. The results indicated that our SVM model could be used as the filter for designing new BCP compounds with higher TOP-I inhibitory activity.
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Affiliation(s)
- Khac-Minh Thai
- Department of Medicinal Chemistry, School of Pharmacy, University of Medicine and Pharmacy at Ho Chi Minh City, 41 Dinh Tien Hoang St., District 1, Ho Chi Minh City, Vietnam.
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Jeon EH, Park JH, Jeong JH, Lee SK. 2D-QSAR analysis for hERG ion channel inhibitors. ANALYTICAL SCIENCE AND TECHNOLOGY 2011. [DOI: 10.5806/ast.2011.24.6.533] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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Digles D, Ecker GF. Self-Organizing Maps for In Silico Screening and Data Visualization. Mol Inform 2011; 30:838-46. [PMID: 27468103 DOI: 10.1002/minf.201100082] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2011] [Accepted: 08/05/2011] [Indexed: 02/04/2023]
Abstract
Self-organizing maps, which are unsupervised artificial neural networks, have become a very useful tool in a wide area of disciplines, including medicinal chemistry. Here, we will focus on two applications of self-organizing maps: the use of self-organizing maps for in silico screening and for clustering and visualisation of large datasets. Additionally, the importance of parameter selection is discussed and some modifications to the original algorithm are summarised.
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Affiliation(s)
- Daniela Digles
- Department of Medicinal Chemistry, University of Vienna, Althanstrasse 14, 1090 Vienna, Austria phone/fax: +43-1-4277-55110/+43-1-4277-9551
| | - Gerhard F Ecker
- Department of Medicinal Chemistry, University of Vienna, Althanstrasse 14, 1090 Vienna, Austria phone/fax: +43-1-4277-55110/+43-1-4277-9551.
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Kim JH, Chae CH, Kang SM, Lee JY, Lee GN, Hwang SH, Kang NS. The Predictive QSAR Model for hERG Inhibitors Using Bayesian and Random Forest Classification Method. B KOREAN CHEM SOC 2011. [DOI: 10.5012/bkcs.2011.32.4.1237] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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Abstract
This article reviews the use of informatics and computational chemistry methods in medicinal chemistry, with special consideration of how computational techniques can be adapted and extended to obtain more and higher-quality information. Special consideration is given to the computation of protein–ligand binding affinities, to the prediction of off-target bioactivities, bioactivity spectra and computational toxicology, and also to calculating absorption-, distribution-, metabolism- and excretion-relevant properties, such as solubility.
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Polak S, Wiśniowska B, Ahamadi M, Mendyk A. Prediction of the hERG potassium channel inhibition potential with use of artificial neural networks. Appl Soft Comput 2011. [DOI: 10.1016/j.asoc.2010.09.010] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
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Klon AE. Machine learning algorithms for the prediction of hERG and CYP450 binding in drug development. Expert Opin Drug Metab Toxicol 2011; 6:821-33. [PMID: 20465523 DOI: 10.1517/17425255.2010.489550] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
Abstract
IMPORTANCE OF THE FIELD The cost of developing new drugs is estimated at approximately $1 billion; the withdrawal of a marketed compound due to toxicity can result in serious financial loss for a pharmaceutical company. There has been a greater interest in the development of in silico tools that can identify compounds with metabolic liabilities before they are brought to market. AREAS COVERED IN THIS REVIEW The two largest classes of machine learning (ML) models, which will be discussed in this review, have been developed to predict binding to the human ether-a-go-go related gene (hERG) ion channel protein and the various CYP isoforms. Being able to identify potentially toxic compounds before they are made would greatly reduce the number of compound failures and the costs associated with drug development. WHAT THE READER WILL GAIN This review summarizes the state of modeling hERG and CYP binding towards this goal since 2003 using ML algorithms. TAKE HOME MESSAGE A wide variety of ML algorithms that are comparable in their overall performance are available. These ML methods may be applied regularly in discovery projects to flag compounds with potential metabolic liabilities.
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Affiliation(s)
- Anthony E Klon
- Ansaris, Computational Chemistry, Four Valley Square, 512 East Township Line Road, Blue Bell, PA 19422, USA.
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Thai KM, Windisch A, Stork D, Weinzinger A, Schiesaro A, Guy R, Timin E, Hering S, Ecker G. The hERG Potassium Channel and Drug Trapping: Insight from Docking Studies with Propafenone Derivatives. ChemMedChem 2010; 5:436-42. [DOI: 10.1002/cmdc.200900374] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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Raschi E, Ceccarini L, De Ponti F, Recanatini M. hERG-related drug toxicity and models for predicting hERG liability and QT prolongation. Expert Opin Drug Metab Toxicol 2009; 5:1005-21. [PMID: 19572824 DOI: 10.1517/17425250903055070] [Citation(s) in RCA: 57] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
BACKGROUND hERG K(+) channels have been recognized as a primary antitarget in safety pharmacology. Their blockade, caused by several drugs with different therapeutic indications, may lead to QT prolongation and, eventually, to potentially fatal arrhythmia, namely torsade de pointes. Therefore, a number of preclinical models have been developed to predict hERG liability early in the drug development process. OBJECTIVE The aim of this review is to outline the present state of the art on drug-induced hERG blockade, providing insights on the predictive value of in vitro and in silico models for hERG liability. METHODS On the basis of latest reports, high-throughput preclinical models have been discussed outlining advantages and limitations. CONCLUSION Although no single model has an absolute value, an integrated risk assessment is recommended to predict the pro-arrhythmic risk of a given drug. This prediction requires expertise from different areas and should encompass emerging issues such as interference with hERG trafficking and QT shortening.
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Affiliation(s)
- Emanuel Raschi
- University of Bologna, Department of Pharmacology, Italy
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Similarity-based SIBAR descriptors for classification of chemically diverse hERG blockers. Mol Divers 2009; 13:321-36. [DOI: 10.1007/s11030-009-9117-0] [Citation(s) in RCA: 29] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2008] [Accepted: 01/17/2009] [Indexed: 11/25/2022]
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