메뉴 건너뛰기




Volumn 56, Issue 8, 2016, Pages 1576-1587

Benchmarking the Predictive Power of Ligand Efficiency Indices in QSAR

Author keywords

[No Author keywords available]

Indexed keywords

BINDING ENERGY; BIOACTIVITY; COMPUTATIONAL CHEMISTRY; DECISION TREES; EFFICIENCY; LEAST SQUARES APPROXIMATIONS; LIGANDS; MOLECULES; OUTSOURCING; PHYSICOCHEMICAL PROPERTIES; SUPPORT VECTOR MACHINES;

EID: 84983440693     PISSN: 15499596     EISSN: 1549960X     Source Type: Journal    
DOI: 10.1021/acs.jcim.6b00136     Document Type: Article
Times cited : (36)

References (56)
  • 1
    • 79952171625 scopus 로고    scopus 로고
    • Probing the Links between in Vitro Potency, ADMET and Physicochemical Parameters
    • Gleeson, M. P.; Hersey, A.; Montanari, D.; Overington, J. Probing the Links Between in Vitro Potency, ADMET and Physicochemical Parameters Nat. Rev. Drug Discovery 2011, 10, 197-208 10.1038/nrd3367
    • (2011) Nat. Rev. Drug Discovery , vol.10 , pp. 197-208
    • Gleeson, M.P.1    Hersey, A.2    Montanari, D.3    Overington, J.4
  • 2
    • 77950560159 scopus 로고    scopus 로고
    • An Analysis of the Binding Efficiencies of Drugs and Their Leads in Successful Drug Discovery Programs
    • Perola, E. An Analysis of the Binding Efficiencies of Drugs and Their Leads in Successful Drug Discovery Programs J. Med. Chem. 2010, 53, 2986-2997 10.1021/jm100118x
    • (2010) J. Med. Chem. , vol.53 , pp. 2986-2997
    • Perola, E.1
  • 4
    • 43049088827 scopus 로고    scopus 로고
    • Ligand Binding Efficiency: Trends, Physical Basis, and Implications
    • Reynolds, C. H.; Tounge, B. A.; Bembenek, S. D. Ligand Binding Efficiency: Trends, Physical Basis, and Implications J. Med. Chem. 2008, 51, 2432-2438 10.1021/jm701255b
    • (2008) J. Med. Chem. , vol.51 , pp. 2432-2438
    • Reynolds, C.H.1    Tounge, B.A.2    Bembenek, S.D.3
  • 5
    • 1942453243 scopus 로고    scopus 로고
    • Ligand Efficiency: A Useful Metric for Lead Selection
    • Hopkins, A. L.; Groom, C. R.; Alex, A. Ligand Efficiency: A Useful Metric for Lead Selection Drug Discovery Today 2004, 9, 430-431 10.1016/S1359-6446(04)03069-7
    • (2004) Drug Discovery Today , vol.9 , pp. 430-431
    • Hopkins, A.L.1    Groom, C.R.2    Alex, A.3
  • 6
    • 77957682613 scopus 로고    scopus 로고
    • Ligand Efficiency Indices for an Effective Mapping of Chemico-Biological Space: The Concept of an Atlas-like Representation
    • Abad-Zapatero, C.; Perisic, O.; Wass, J.; Bento, A. P.; Overington, J.; Al-Lazikani, B.; Johnson, M. E. Ligand Efficiency Indices for an Effective Mapping of Chemico-Biological Space: The Concept of an Atlas-like Representation Drug Discovery Today 2010, 15, 804-811 10.1016/j.drudis.2010.08.004
    • (2010) Drug Discovery Today , vol.15 , pp. 804-811
    • Abad-Zapatero, C.1    Perisic, O.2    Wass, J.3    Bento, A.P.4    Overington, J.5    Al-Lazikani, B.6    Johnson, M.E.7
  • 7
    • 84885172160 scopus 로고    scopus 로고
    • Setting Expectations in Molecular Optimizations: Strengths and Limitations of Commonly Used Composite Parameters
    • Shultz, M. D. Setting Expectations in Molecular Optimizations: Strengths and Limitations of Commonly Used Composite Parameters Bioorg. Med. Chem. Lett. 2013, 23, 5980-5991 10.1016/j.bmcl.2013.08.029
    • (2013) Bioorg. Med. Chem. Lett. , vol.23 , pp. 5980-5991
    • Shultz, M.D.1
  • 8
    • 84938514887 scopus 로고    scopus 로고
    • Ligand Efficiency Metrics: Why All the Fuss?
    • Reynolds, C. H. Ligand Efficiency Metrics: Why All the Fuss? Future Med. Chem. 2015, 7, 1363-1365 10.4155/fmc.15.70
    • (2015) Future Med. Chem. , vol.7 , pp. 1363-1365
    • Reynolds, C.H.1
  • 9
    • 84892596742 scopus 로고    scopus 로고
    • Improving the Plausibility of Success with Inefficient Metrics
    • Shultz, M. D. Improving the Plausibility of Success with Inefficient Metrics ACS Med. Chem. Lett. 2014, 5, 2-5 10.1021/ml4004638
    • (2014) ACS Med. Chem. Lett. , vol.5 , pp. 2-5
    • Shultz, M.D.1
  • 10
  • 11
    • 0036589285 scopus 로고    scopus 로고
    • Current Trends in Lead Discovery: Are We Looking for the Appropriate Properties?
    • Oprea, T. I. Current Trends in Lead Discovery: Are We Looking for the Appropriate Properties? J. Comput.-Aided Mol. Des. 2002, 16, 325-334 10.1023/A:1020877402759
    • (2002) J. Comput.-Aided Mol. Des. , vol.16 , pp. 325-334
    • Oprea, T.I.1
  • 12
    • 17044403086 scopus 로고    scopus 로고
    • Ligand Efficiency Indices As Guideposts for Drug Discovery
    • Abad-Zapatero, C.; Metz, J. T. Ligand Efficiency Indices As Guideposts for Drug Discovery Drug Discovery Today 2005, 10, 464-469 10.1016/S1359-6446(05)03386-6
    • (2005) Drug Discovery Today , vol.10 , pp. 464-469
    • Abad-Zapatero, C.1    Metz, J.T.2
  • 13
  • 15
    • 84887036726 scopus 로고    scopus 로고
    • Training Based on Ligand Efficiency Improves Prediction of Bioactivities of Ligands and Drug Target Proteins in a Machine Learning Approach
    • Sugaya, N. Training Based on Ligand Efficiency Improves Prediction of Bioactivities of Ligands and Drug Target Proteins in a Machine Learning Approach J. Chem. Inf. Model. 2013, 53, 2525-2537 10.1021/ci400240u
    • (2013) J. Chem. Inf. Model. , vol.53 , pp. 2525-2537
    • Sugaya, N.1
  • 16
    • 84908247106 scopus 로고    scopus 로고
    • Ligand Efficiency-Based Support Vector Regression Models for Predicting Bioactivities of Ligands to Drug Target Proteins
    • Sugaya, N. Ligand Efficiency-Based Support Vector Regression Models for Predicting Bioactivities of Ligands to Drug Target Proteins J. Chem. Inf. Model. 2014, 54, 2751-2763 10.1021/ci5003262
    • (2014) J. Chem. Inf. Model. , vol.54 , pp. 2751-2763
    • Sugaya, N.1
  • 19
    • 67650085841 scopus 로고    scopus 로고
    • Simple Size-Independent Measure of Ligand Efficiency
    • Nissink, J. W. M. Simple Size-Independent Measure of Ligand Efficiency J. Chem. Inf. Model. 2009, 49, 1617-1622 10.1021/ci900094m
    • (2009) J. Chem. Inf. Model. , vol.49 , pp. 1617-1622
    • Nissink, J.W.M.1
  • 20
    • 84879570665 scopus 로고    scopus 로고
    • Beyond the Scope of Free-Wilson Analysis: Building Interpretable QSAR Models with Machine Learning Algorithms
    • Chen, H.; Carlsson, L.; Eriksson, M.; Varkonyi, P.; Norinder, U.; Nilsson, I. Beyond the Scope of Free-Wilson Analysis: Building Interpretable QSAR Models with Machine Learning Algorithms J. Chem. Inf. Model. 2013, 53, 1324-1336 10.1021/ci4001376
    • (2013) J. Chem. Inf. Model. , vol.53 , pp. 1324-1336
    • Chen, H.1    Carlsson, L.2    Eriksson, M.3    Varkonyi, P.4    Norinder, U.5    Nilsson, I.6
  • 21
    • 84940547595 scopus 로고    scopus 로고
    • Chemically Aware Model Builder (camb): An R Package for Property and Bioactivity Modelling of Small Molecules
    • Murrell, D. S.; Cortes-Ciriano, I.; van Westen, G. J. P.; Stott, I. P.; Bender, A.; Malliavin, T. E.; Glen, R. C. Chemically Aware Model Builder (camb): An R Package for Property and Bioactivity Modelling of Small Molecules J. Cheminf. 2015, 7, 45 10.1186/s13321-015-0086-2
    • (2015) J. Cheminf. , vol.7 , pp. 45
    • Murrell, D.S.1    Cortes-Ciriano, I.2    Van Westen, G.J.P.3    Stott, I.P.4    Bender, A.5    Malliavin, T.E.6    Glen, R.C.7
  • 22
    • 77952772341 scopus 로고    scopus 로고
    • Extended-Connectivity Fingerprints
    • Rogers, D.; Hahn, M. Extended-Connectivity Fingerprints J. Chem. Inf. Model. 2010, 50, 742-754 10.1021/ci100050t
    • (2010) J. Chem. Inf. Model. , vol.50 , pp. 742-754
    • Rogers, D.1    Hahn, M.2
  • 23
    • 33644963750 scopus 로고    scopus 로고
    • Circular Fingerprints: Flexible Molecular Descriptors with Applications from Physical Chemistry to ADME
    • Glen, R. C.; Bender, A.; Arnby, C. H.; Carlsson, L.; Boyer, S.; Smith, J. Circular Fingerprints: Flexible Molecular Descriptors with Applications from Physical Chemistry to ADME IDrugs 2006, 9, 199-204
    • (2006) IDrugs , vol.9 , pp. 199-204
    • Glen, R.C.1    Bender, A.2    Arnby, C.H.3    Carlsson, L.4    Boyer, S.5    Smith, J.6
  • 24
    • 84983412261 scopus 로고    scopus 로고
    • RDKit: Open-Source Cheminformatics, Version 2014.09.2. (accessed December)
    • Landrum, G. RDKit: Open-Source Cheminformatics, Version 2014.09.2. http://rdkit.org/ (accessed December 2014).
    • (2014)
    • Landrum, G.1
  • 25
    • 61949166066 scopus 로고    scopus 로고
    • How Similar Are Similarity Searching Methods? a Principal Component Analysis of Molecular Descriptor Space
    • Bender, A.; Jenkins, J. L.; Scheiber, J.; Sukuru, S. C. K.; Glick, M.; Davies, J. W. How Similar Are Similarity Searching Methods? a Principal Component Analysis of Molecular Descriptor Space J. Chem. Inf. Model. 2009, 49, 108-119 10.1021/ci800249s
    • (2009) J. Chem. Inf. Model. , vol.49 , pp. 108-119
    • Bender, A.1    Jenkins, J.L.2    Scheiber, J.3    Sukuru, S.C.K.4    Glick, M.5    Davies, J.W.6
  • 27
    • 56249113343 scopus 로고    scopus 로고
    • Building Predictive Models in R Using the Caret Package
    • Kuhn, M. Building Predictive Models in R Using the Caret Package J. Stat. Soft. 2008, 28, 1-26 10.18637/jss.v028.i05
    • (2008) J. Stat. Soft. , vol.28 , pp. 1-26
    • Kuhn, M.1
  • 29
    • 0037361983 scopus 로고    scopus 로고
    • Assessing Model Fit by Cross-Validation
    • Hawkins, D. M.; Basak, S. C.; Mills, D. Assessing Model Fit by Cross-Validation J. Chem. Inf. Model. 2003, 43, 579-586 10.1021/ci025626i
    • (2003) J. Chem. Inf. Model. , vol.43 , pp. 579-586
    • Hawkins, D.M.1    Basak, S.C.2    Mills, D.3
  • 30
    • 57549095014 scopus 로고    scopus 로고
    • External Validation and Prediction Employing the Predictive Squared Correlation Coefficient - Test Set Activity Mean vs Training Set Activity Mean
    • Schuurmann, G.; Ebert, R.-U.; Chen, J.; Wang, B.; Kuhne, R. External Validation and Prediction Employing the Predictive Squared Correlation Coefficient-Test Set Activity Mean vs Training Set Activity Mean J. Chem. Inf. Model. 2008, 48, 2140-2145 10.1021/ci800253u
    • (2008) J. Chem. Inf. Model. , vol.48 , pp. 2140-2145
    • Schuurmann, G.1    Ebert, R.-U.2    Chen, J.3    Wang, B.4    Kuhne, R.5
  • 31
    • 84938066627 scopus 로고    scopus 로고
    • Beware of R2: Simple, Unambiguous Assessment of the Prediction Accuracy of QSAR and QSPR Models
    • Alexander, D. L. J.; Tropsha, A.; Winkler, D. A. Beware of R2: Simple, Unambiguous Assessment of the Prediction Accuracy of QSAR and QSPR Models J. Chem. Inf. Model. 2015, 55, 1316-1322 10.1021/acs.jcim.5b00206
    • (2015) J. Chem. Inf. Model. , vol.55 , pp. 1316-1322
    • Alexander, D.L.J.1    Tropsha, A.2    Winkler, D.A.3
  • 32
    • 84952542694 scopus 로고
    • Cautionary Note about R2
    • Kvalseth, T. O. Cautionary Note About R2 Am. Stat. 1985, 39, 279-285 10.2307/2683704
    • (1985) Am. Stat. , vol.39 , pp. 279-285
    • Kvalseth, T.O.1
  • 33
    • 0035965476 scopus 로고    scopus 로고
    • PLS-Regression: A Basic Tool of Chemometrics
    • Wold, S.; Sjostrom, M.; Eriksson, L. PLS-Regression: a Basic Tool of Chemometrics Chemom. Intell. Lab. Syst. 2001, 58, 109-130 10.1016/S0169-7439(01)00155-1
    • (2001) Chemom. Intell. Lab. Syst. , vol.58 , pp. 109-130
    • Wold, S.1    Sjostrom, M.2    Eriksson, L.3
  • 34
    • 76349111180 scopus 로고    scopus 로고
    • Partial Least Squares Regression and Projection on Latent Structure Regression (PLS Regression)
    • Abdi, H. Partial Least Squares Regression and Projection on Latent Structure Regression (PLS Regression) Wiley Interdisciplinary Reviews: Computational Statistics 2010, 2, 97-106 10.1002/wics.51
    • (2010) Wiley Interdisciplinary Reviews: Computational Statistics , vol.2 , pp. 97-106
    • Abdi, H.1
  • 35
    • 77957553895 scopus 로고    scopus 로고
    • Wiley Interdisciplinary Reviews: Computational Statistics
    • Abdi, H.; Williams, L. J. Wiley Interdisciplinary Reviews: Computational Statistics Volume 2010, 2, 433-459 10.1002/wics.101
    • (2010) Volume , vol.2 , pp. 433-459
    • Abdi, H.1    Williams, L.J.2
  • 36
    • 0038259114 scopus 로고    scopus 로고
    • Classes of Kernels for Machine Learning: A Statistics Perspective
    • Genton, M. G. Classes of Kernels for Machine Learning: A Statistics Perspective J. Mach. Learn. Res. 2002, 2, 299-312
    • (2002) J. Mach. Learn. Res. , vol.2 , pp. 299-312
    • Genton, M.G.1
  • 38
    • 34249753618 scopus 로고
    • Support-Vector Networks
    • Cortes, C.; Vapnik, V. Support-Vector Networks Mach. Learn. 1995, 20, 273-297 10.1007/BF00994018
    • (1995) Mach. Learn. , vol.20 , pp. 273-297
    • Cortes, C.1    Vapnik, V.2
  • 39
    • 55449125185 scopus 로고    scopus 로고
    • Support Vector Machines and Kernels for Computational Biology
    • Ben-Hur, A.; Ong, C. S.; Sonnenburg, S.; Scholkopf, B.; Ratsch, G. Support Vector Machines and Kernels for Computational Biology PLoS Comput. Biol. 2008, 4, e1000173 10.1371/journal.pcbi.1000173
    • (2008) PLoS Comput. Biol. , vol.4 , pp. e1000173
    • Ben-Hur, A.1    Ong, C.S.2    Sonnenburg, S.3    Scholkopf, B.4    Ratsch, G.5
  • 41
    • 84924256017 scopus 로고    scopus 로고
    • Prediction of the Potency of Mammalian Cyclooxygenase Inhibitors with Ensemble Proteochemometric Modeling
    • Cortes-Ciriano, I.; Murrell, D. S.; van Westen, G.; Bender, A.; Malliavin, T. Prediction of the Potency of Mammalian Cyclooxygenase Inhibitors with Ensemble Proteochemometric Modeling J. Cheminf. 2015, 7, 1 10.1186/s13321-014-0049-z
    • (2015) J. Cheminf. , vol.7 , pp. 1
    • Cortes-Ciriano, I.1    Murrell, D.S.2    Van Westen, G.3    Bender, A.4    Malliavin, T.5
  • 42
    • 0346786584 scopus 로고    scopus 로고
    • Arcing Classifier (with Discussion and a Rejoinder by the Author)
    • Breiman, L. Arcing Classifier (with Discussion and a Rejoinder by the Author) Ann. Statist. 1998, 26, 801-849 10.1214/aos/1024691079
    • (1998) Ann. Statist. , vol.26 , pp. 801-849
    • Breiman, L.1
  • 43
    • 84892667860 scopus 로고    scopus 로고
    • Gradient Boosting Machines, a Tutorial
    • Natekin, A.; Knoll, A. Gradient Boosting Machines, a Tutorial Front. Neurorobot. 2013, 7, 21 10.3389/fnbot.2013.00021
    • (2013) Front. Neurorobot. , vol.7 , pp. 21
    • Natekin, A.1    Knoll, A.2
  • 44
    • 84938086444 scopus 로고    scopus 로고
    • Comparing the Influence of Simulated Experimental Errors on 12 Machine Learning Algorithms in Bioactivity Modeling Using 12 Diverse Data Sets
    • Cortes-Ciriano, I.; Bender, A.; Malliavin, T. E. Comparing the Influence of Simulated Experimental Errors on 12 Machine Learning Algorithms in Bioactivity Modeling Using 12 Diverse Data Sets J. Chem. Inf. Model. 2015, 55, 1413-1425 10.1021/acs.jcim.5b00101
    • (2015) J. Chem. Inf. Model. , vol.55 , pp. 1413-1425
    • Cortes-Ciriano, I.1    Bender, A.2    Malliavin, T.E.3
  • 45
    • 0035478854 scopus 로고    scopus 로고
    • Random Forests
    • Breiman, L. Random Forests Mach. Learn. 2001, 45, 5-32 10.1023/A:1010933404324
    • (2001) Mach. Learn. , vol.45 , pp. 5-32
    • Breiman, L.1
  • 46
    • 84888603687 scopus 로고    scopus 로고
    • Using Random Forest to Model the Domain Applicability of Another Random Forest Model
    • Sheridan, R. P. Using Random Forest to Model the Domain Applicability of Another Random Forest Model J. Chem. Inf. Model. 2013, 53, 2837-2850 10.1021/ci400482e
    • (2013) J. Chem. Inf. Model. , vol.53 , pp. 2837-2850
    • Sheridan, R.P.1
  • 47
    • 84859204703 scopus 로고    scopus 로고
    • Three Useful Dimensions for Domain Applicability in QSAR Models Using Random Forest
    • Sheridan, R. P. Three Useful Dimensions for Domain Applicability in QSAR Models Using Random Forest J. Chem. Inf. Model. 2012, 52, 814-823 10.1021/ci300004n
    • (2012) J. Chem. Inf. Model. , vol.52 , pp. 814-823
    • Sheridan, R.P.1
  • 49
    • 84983393472 scopus 로고    scopus 로고
    • PLS: Partial Least Squares and Principal Component Regression; R package version 2.4-3
    • Mevik, B.-H.; Wehrens, R.; Liland, K. H. PLS: Partial Least Squares and Principal Component Regression; R package version 2.4-3, 2013.
    • (2013)
    • Mevik, B.-H.1    Wehrens, R.2    Liland, K.H.3
  • 50
    • 11244352554 scopus 로고    scopus 로고
    • Kernlab - An S4 Package for Kernel Methods in R
    • Karatzoglou, A.; Smola, A.; Hornik, K.; Zeileis, A. kernlab-an S4 Package for Kernel Methods in R J. Stat. Soft. 2004, 11, 1-20 10.18637/jss.v011.i09
    • (2004) J. Stat. Soft. , vol.11 , pp. 1-20
    • Karatzoglou, A.1    Smola, A.2    Hornik, K.3    Zeileis, A.4
  • 51
    • 84983430695 scopus 로고    scopus 로고
    • GMB: Generalized Boosted Regression Models; R package version 2.1
    • Ridgeway, G. GMB: Generalized Boosted Regression Models; R package version 2.1, 2013.
    • (2013)
    • Ridgeway, G.1
  • 52
    • 0035470889 scopus 로고    scopus 로고
    • Greedy Function Approximation: A Gradient Boosting Machine
    • Friedman, J. H. Greedy Function Approximation: A Gradient Boosting Machine Ann. Stat. 2001, 29, 1189-1232 10.1214/aos/1013203451
    • (2001) Ann. Stat. , vol.29 , pp. 1189-1232
    • Friedman, J.H.1
  • 53
    • 0345040873 scopus 로고    scopus 로고
    • Classification and Regression by RandomForest
    • Liaw, A.; Wiener, M. Classification and Regression by RandomForest R News 2002, 2, 18-22
    • (2002) R News , vol.2 , pp. 18-22
    • Liaw, A.1    Wiener, M.2
  • 56
    • 84958826961 scopus 로고    scopus 로고
    • Be aware of error measures. Further studies on validation of predictive QSAR models
    • Roy, K.; Das, R. N.; Ambure, P.; Aher, R. B. Be aware of error measures. Further studies on validation of predictive QSAR models Chemom. Intell. Lab. Syst. 2016, 152, 18-33 10.1016/j.chemolab.2016.01.008
    • (2016) Chemom. Intell. Lab. Syst. , vol.152 , pp. 18-33
    • Roy, K.1    Das, R.N.2    Ambure, P.3    Aher, R.B.4


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.