METHODS TO IMPROVE THE RELIABILITY, VALIDITY AND INTERPRETABILITY OF QSAR MODELS

[1]  John A. Nelder,et al.  A Simplex Method for Function Minimization , 1965, Comput. J..

[2]  D. Manallack,et al.  Neural networks in drug discovery: Have they lived up to their promise? , 1999 .

[3]  D. Boschelli,et al.  2-Substituted aminopyrido[2,3-d]pyrimidin-7(8H)-ones. structure-activity relationships against selected tyrosine kinases and in vitro and in vivo anticancer activity. , 1998, Journal of medicinal chemistry.

[4]  M. Maloof Learning When Data Sets are Imbalanced and When Costs are Unequal and Unknown , 2003 .

[5]  D. L. Klayman,et al.  Qinghaosu (artemisinin): an antimalarial drug from China , 1985 .

[6]  Stu Borman,et al.  New QSAR Techniques Eyed For Environmental Assessments: Expert system, spectroscopy method use readily available data to develop quantitative structure-activity relationships for broad compound classes , 1990 .

[7]  Johann Gasteiger,et al.  Prediction of 1H NMR chemical shifts using neural networks. , 2002, Analytical chemistry.

[8]  L. Kier Distinguishing Atom Differences in a Molecular Graph Shape Index , 1986 .

[9]  Chris Aldrich,et al.  ANN-DT: an algorithm for extraction of decision trees from artificial neural networks , 1999, IEEE Trans. Neural Networks.

[10]  M Chastrette,et al.  Structure-musk odor relationships for tetralins and indans using neural networks (on the contribution of descriptors to the classification) , 1994 .

[11]  A. Tropsha,et al.  Beware of q2! , 2002, Journal of molecular graphics & modelling.

[12]  Dimitris K. Agrafiotis,et al.  Design and Prioritization of Plates for High-Throughput Screening , 2001, J. Chem. Inf. Comput. Sci..

[13]  D. Boschelli,et al.  2-Substituted Aminopyrido[2,3- d ]pyrimidin-7(8 H )-ones. Structure−Activity Relationships Against Selected Tyrosine Kinases and in Vitro and in Vivo Anticancer Activity , 1998 .

[14]  Kristina Luthman,et al.  Polar Molecular Surface Properties Predict the Intestinal Absorption of Drugs in Humans , 1997, Pharmaceutical Research.

[15]  David T. Stanton,et al.  Development and Use of Hydrophobic Surface Area (HSA) Descriptors for Computer-Assisted Quantitative Structure-Activity and Structure-Property Relationship Studies , 2004, J. Chem. Inf. Model..

[16]  P. Khadikar,et al.  Topological designing of 4-piperazinylquinazolines as antagonists of PDGFR tyrosine kinase family. , 2003, Bioorganic & medicinal chemistry letters.

[17]  S. Hubbard,et al.  Crystal structure of an angiogenesis inhibitor bound to the FGF receptor tyrosine kinase domain , 1998, The EMBO journal.

[18]  Bruno Bienfait Applications of High-Resolution Self-Organizing Maps to Retrosynthetic and QSAR Analysis , 1994, J. Chem. Inf. Comput. Sci..

[19]  Ross F. Hayward,et al.  The truth is in there: current issues in extracting rules from trained feedforward artificial neural networks , 1997, Proceedings of International Conference on Neural Networks (ICNN'97).

[20]  N. Japkowicz Learning from Imbalanced Data Sets: A Comparison of Various Strategies * , 2000 .

[21]  Leo Breiman,et al.  Bagging Predictors , 1996, Machine Learning.

[22]  Rajarshi Guha,et al.  Development of Linear, Ensemble, and Nonlinear Models for the Prediction and Interpretation of the Biological Activity of a Set of PDGFR Inhibitors , 2004, J. Chem. Inf. Model..

[23]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[24]  B. Mroczkowski,et al.  Crystal structure of the kinase domain of human vascular endothelial growth factor receptor 2: a key enzyme in angiogenesis. , 1999, Structure.

[25]  S. Meshnick,et al.  The mode of action of the antimalarial artemisinin and its derivatives. , 1996, General pharmacology.

[26]  V. Barnett Probability Plotting Methods and Order Statistics , 1975 .

[27]  D. E. Patterson,et al.  Crossvalidation, Bootstrapping, and Partial Least Squares Compared with Multiple Regression in Conventional QSAR Studies , 1988 .

[28]  A. Hopfinger,et al.  Construction of 3D-QSAR Models Using the 4D-QSAR Analysis Formalism , 1997 .

[29]  D. Boschelli,et al.  Synthesis and tyrosine kinase inhibitory activity of a series of 2-amino-8H-pyrido[2,3-d]pyrimidines: identification of potent, selective platelet-derived growth factor receptor tyrosine kinase inhibitors. , 1998, Journal of medicinal chemistry.

[30]  Mark J. Embrechts,et al.  New developments in PEST shape/property hybrid descriptors , 2003, J. Comput. Aided Mol. Des..

[31]  Ye Mei,et al.  New method for direct linear-scaling calculation of electron density of proteins. , 2005, The journal of physical chemistry. A.

[32]  P. Selzer,et al.  Fast calculation of molecular polar surface area as a sum of fragment-based contributions and its application to the prediction of drug transport properties. , 2000, Journal of medicinal chemistry.

[33]  Kurt Hornik,et al.  Multilayer feedforward networks are universal approximators , 1989, Neural Networks.

[34]  Egon L. Willighagen,et al.  The Chemistry Development Kit (CDK): An Open-Source Java Library for Chemo-and Bioinformatics , 2003, J. Chem. Inf. Comput. Sci..

[35]  Lemont B. Kier,et al.  A Shape Index from Molecular Graphs , 1985 .

[36]  G. Maggiora,et al.  Hit-directed nearest-neighbor searching. , 2005, Journal of medicinal chemistry.

[37]  Jacek M. Zurada,et al.  Extraction of rules from artificial neural networks for nonlinear regression , 2002, IEEE Trans. Neural Networks.

[38]  S. L. Dixon,et al.  Semiempirical molecular orbital calculations with linear system size scaling , 1996 .

[39]  Alexandre Arenas,et al.  An Integrated SOM-Fuzzy ARTMAP Neural System for the Evaluation of Toxicity , 2002, J. Chem. Inf. Comput. Sci..

[40]  S. Unger Molecular Connectivity in Structure–activity Analysis , 1987 .

[41]  D. Altman,et al.  Bootstrap investigation of the stability of a Cox regression model. , 1989, Statistics in medicine.

[42]  R. Geary,et al.  The Contiguity Ratio and Statistical Mapping , 1954 .

[43]  Robert S. Pearlman,et al.  Metric Validation and the Receptor-Relevant Subspace Concept , 1999, J. Chem. Inf. Comput. Sci..

[44]  N. Mantel Why Stepdown Procedures in Variable Selection , 1970 .

[45]  P. Jurs,et al.  Studies of Chemical Structure-Biological Activity Relations Using Pattern Recognition , 1979 .

[46]  Henry S. Rzepa,et al.  MNDO parameters for third period elements , 1978 .

[47]  L. Hall,et al.  Molecular connectivity in chemistry and drug research , 1976 .

[48]  R. Fletcher,et al.  A New Approach to Variable Metric Algorithms , 1970, Comput. J..

[49]  Mark T D Cronin,et al.  Quantitative structure-activity relationships (QSARs) for the prediction of skin permeation of exogenous chemicals. , 2002, Chemosphere.

[50]  M Karplus,et al.  Evolutionary optimization in quantitative structure-activity relationship: an application of genetic neural networks. , 1996, Journal of medicinal chemistry.

[51]  Milan Randic,et al.  On Interpretation of Well-Known Topological Indices , 2001, J. Chem. Inf. Comput. Sci..

[52]  Johann Gasteiger,et al.  Prediction of Aqueous Solubility of Organic Compounds Based on a 3D Structure Representation , 2003, J. Chem. Inf. Comput. Sci..

[53]  H. D. Showalter,et al.  Biochemical and cellular effects of c-Src kinase-selective pyrido[2, 3-d]pyrimidine tyrosine kinase inhibitors. , 2000, Biochemical pharmacology.

[54]  Guido Bologna Rule extraction from linear combinations of DIMLP neural networks , 2000, Proceedings. Vol.1. Sixth Brazilian Symposium on Neural Networks.

[55]  Lemont B. Kier,et al.  An Electrotopological-State Index for Atoms in Molecules , 1990, Pharmaceutical Research.

[56]  Peter J. Fleming,et al.  Combinatorial Library Design Using a Multiobjective Genetic Algorithm , 2002, J. Chem. Inf. Comput. Sci..

[57]  Johann Gasteiger,et al.  Chemical Information in 3D Space , 1996, J. Chem. Inf. Comput. Sci..

[58]  Peter C. Jurs,et al.  Prediction of the Normal Boiling Points of Organic Compounds from Molecular Structures with a Computational Neural Network Model , 1999, J. Chem. Inf. Comput. Sci..

[59]  Peter C. Jurs,et al.  Prediction of Glycine/NMDA Receptor Antagonist Inhibition from Molecular Structure , 2002, J. Chem. Inf. Comput. Sci..

[60]  Tetsuya Takahashi An information theoretical interpretation of neuronal activities , 1991, IJCNN-91-Seattle International Joint Conference on Neural Networks.

[61]  R. Hertzberg,et al.  High-throughput screening: new technology for the 21st century. , 2000, Current opinion in chemical biology.

[62]  J. Gálvez,et al.  Pharmacological distribution diagrams: a tool for de novo drug design. , 1996, Journal of molecular graphics.

[63]  M. Karelson,et al.  Quantum-Chemical Descriptors in QSAR/QSPR Studies. , 1996, Chemical reviews.

[64]  J. Gasteiger,et al.  Knowledge Discovery in Reaction Databases: Landscaping Organic Reactions by a Self-Organizing Neural Network , 1997 .

[65]  Bruno Boulanger,et al.  A Fast Exchange Algorithm for Designing Focused Libraries in Lead Optimization , 2005, J. Chem. Inf. Model..

[66]  J. K. Mills,et al.  Modeling of neural networks in feedback systems using describing functions , 1997, Proceedings of International Conference on Neural Networks (ICNN'97).

[67]  A. Shaha Implications of Prognostic Factors and Risk Groups in the Management of Differentiated Thyroid Cancer , 2004, The Laryngoscope.

[68]  Peter C. Jurs,et al.  Prediction of Glass Transition Temperatures from Monomer and Repeat Unit Structure Using Computational Neural Networks , 2002, J. Chem. Inf. Comput. Sci..

[69]  L. Kier Shape Indexes of Orders One and Three from Molecular Graphs , 1986 .

[70]  F. Pettersson,et al.  Modeling of the Blast Furnace Burden Distribution by Evolving Neural Networks , 2003 .

[71]  Lemont B. Kier,et al.  Molecular structure description , 1999 .

[72]  S. Free,et al.  A MATHEMATICAL CONTRIBUTION TO STRUCTURE-ACTIVITY STUDIES. , 1964, Journal of medicinal chemistry.

[73]  Richard K. Haynes,et al.  From Qinghao, marvelous herb of antiquity, to the antimalarial trioxane Qinghaosu - And some remarkable new chemistry , 1997 .

[74]  Weiliang Zhu,et al.  Molecular docking and 3-D-QSAR studies on the possible antimalarial mechanism of artemisinin analogues. , 2002, Bioorganic & medicinal chemistry.

[75]  Hermann Ney,et al.  On the Probabilistic Interpretation of Neural Network Classifiers and Discriminative Training Criteria , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[76]  César Hervás-Martínez,et al.  Heuristic Extraction of Rules in Pruned Artificial Neural Networks Models Used for Quantifying Highly Overlapping Chromatographic Peaks , 2004, J. Chem. Inf. Model..

[77]  Jun Feng,et al.  PowerMV: A Software Environment for Molecular Viewing, Descriptor Generation, Data Analysis and Hit Evaluation , 2005, J. Chem. Inf. Model..

[78]  A. J. Stuper,et al.  Computer assisted studies of chemical structure and biological function , 1979 .

[79]  Rudolf H. Winger,et al.  Comparative molecular field analysis of artemisinin derivatives: Ab initio versus semiempirical optimized structures , 1998, J. Comput. Aided Mol. Des..

[80]  D. Shanno Conditioning of Quasi-Newton Methods for Function Minimization , 1970 .

[81]  Mitchell A. Avery,et al.  Synthesis and structure-activity relationships of peroxidic antimalarials based on artemisinin , 1999 .

[82]  Milan Randic,et al.  Search for all self-avoiding paths graphs for molecular graphs , 1979, Comput. Chem..

[83]  Tor Guimaraes,et al.  Integrating artificial neural networks with rule-based expert systems , 1994, Decis. Support Syst..

[84]  Mitchell A. Avery,et al.  Comparison of 3D quantitative structure-activity relationship methods: Analysis of the in vitro antimalarial activity of 154 artemisinin analogues by hypothetical active-site lattice and comparative molecular field analysis , 1998, J. Comput. Aided Mol. Des..

[85]  W. Delano The PyMOL Molecular Graphics System , 2002 .

[86]  J. Topliss,et al.  Chance factors in studies of quantitative structure-activity relationships. , 1979, Journal of medicinal chemistry.

[87]  W. Milhous,et al.  Structure-activity relationships of the antimalarial agent artemisinin. 1. Synthesis and comparative molecular field analysis of C-9 analogs of artemisinin and 10-deoxoartemisinin. , 1993, Journal of medicinal chemistry.

[88]  Johann Gasteiger,et al.  The comparison of geometric and electronic properties of molecular surfaces by neural networks: Application to the analysis of corticosteroid-binding globulin activity of steroids , 1996, J. Comput. Aided Mol. Des..

[89]  Peter C. Jurs,et al.  Atomic charge calculations for quantitative structure—property relationships , 1992 .

[90]  Terry R. Stouch,et al.  A simple method for the representation, quantification, and comparison of the volumes and shapes of chemical compounds , 1986, J. Chem. Inf. Comput. Sci..

[91]  Alexander Golbraikh,et al.  Rational selection of training and test sets for the development of validated QSAR models , 2003, J. Comput. Aided Mol. Des..

[92]  Zheng Rong Yang,et al.  Evaluation of Mutual Information and Genetic Programming for Feature Selection in QSAR , 2004, J. Chem. Inf. Model..

[93]  J. Kazius,et al.  Derivation and validation of toxicophores for mutagenicity prediction. , 2005, Journal of medicinal chemistry.

[94]  Bernhard Schölkopf,et al.  Feature selection and transduction for prediction of molecular bioactivity for drug design , 2003, Bioinform..

[95]  Matthew D. Wessel,et al.  Prediction of Reduced Ion Mobility Constants from Structural Information Using Multiple Linear Regression Analysis and Computational Neural Networks , 1994 .

[96]  Y. Martin Diverse viewpoints on computational aspects of molecular diversity. , 2001, Journal of combinatorial chemistry.

[97]  A. K. Madan,et al.  Superpendentic Index: A Novel Topological Descriptor for Predicting Biological Activity , 1999, J. Chem. Inf. Comput. Sci..

[98]  L. Breiman OUT-OF-BAG ESTIMATION , 1996 .

[99]  Brian D. Ripley,et al.  Pattern Recognition and Neural Networks , 1996 .

[100]  Gordon M. Crippen,et al.  Prediction of Physicochemical Parameters by Atomic Contributions , 1999, J. Chem. Inf. Comput. Sci..

[101]  Zhiliang Li,et al.  Approach to Estimation and Prediction for Normal Boiling Point (NBP) of Alkanes Based on a Novel Molecular Distance-Edge (MDE) Vector , 1998, J. Chem. Inf. Comput. Sci..

[102]  Palanisamy Thanikaivelan,et al.  Application of quantum chemical descriptor in quantitative structure activity and structure property relationship , 2000 .

[103]  R. Tibshirani Regression Shrinkage and Selection via the Lasso , 1996 .

[104]  Leo Breiman,et al.  Randomizing Outputs to Increase Prediction Accuracy , 2000, Machine Learning.

[105]  A. K. Madan,et al.  Eccentric Connectivity Index: A Novel Highly Discriminating Topological Descriptor for Structure-Property and Structure-Activity Studies , 1997, J. Chem. Inf. Comput. Sci..

[106]  Fei Gu,et al.  Design of Diverse and Focused Combinatorial Libraries Using an Alternating Algorithm , 2003, J. Chem. Inf. Comput. Sci..

[107]  Nichael Lynn Cramer,et al.  A Representation for the Adaptive Generation of Simple Sequential Programs , 1985, ICGA.

[108]  Bahram Hemmateenejad,et al.  Toward an Optimal Procedure for PC-ANN Model Building: Prediction of the Carcinogenic Activity of a Large Set of Drugs , 2005, J. Chem. Inf. Model..

[109]  H. Keselman,et al.  Backward, forward and stepwise automated subset selection algorithms: Frequency of obtaining authentic and noise variables , 1992 .

[110]  A. Hopfinger Computer-assisted drug design. , 1985, Journal of medicinal chemistry.

[111]  Joydeep Ghosh,et al.  Symbolic Interpretation of Artificial Neural Networks , 1999, IEEE Trans. Knowl. Data Eng..

[112]  Teuvo Kohonen,et al.  Self-Organizing Maps , 2010 .

[113]  M. E. Johnson,et al.  Generalized simulated annealing for function optimization , 1986 .

[114]  Ralph Kühne,et al.  Stepwise discrimination between four modes of toxic action of phenols in the Tetrahymena pyriformis assay. , 2003, Chemical research in toxicology.

[115]  Rajarshi Guha,et al.  Generation of QSAR sets with a self-organizing map. , 2004, Journal of molecular graphics & modelling.

[116]  Paola Gramatica,et al.  QSAR study on the tropospheric degradation of organic compounds , 1999 .

[118]  Jon W. Ball,et al.  Quantitative structure‐activity relationships for toxicity of phenols using regression analysis and computational neural networks , 1994 .

[119]  V. Ramakrishnan,et al.  Functional Importance of Platelet-derived Growth Factor (PDGF) Receptor Extracellular Immunoglobulin-like Domains , 1997, The Journal of Biological Chemistry.

[120]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[121]  Weida Tong,et al.  Using Decision Forest to Classify Prostate Cancer Samples on the Basis of SELDI-TOF MS Data: Assessing Chance Correlation and Prediction Confidence , 2004, Environmental health perspectives.

[122]  Kurt Hornik,et al.  Universal approximation of an unknown mapping and its derivatives using multilayer feedforward networks , 1990, Neural Networks.

[123]  Jesús Vicente de Julián-Ortiz,et al.  Topological Approach to Drug Design , 1995, J. Chem. Inf. Comput. Sci..

[124]  S Wold,et al.  Statistical molecular design of building blocks for combinatorial chemistry. , 2000, Journal of medicinal chemistry.

[125]  Jian-hui Jiang,et al.  Quantitative structure-activity relationships (QSAR): studies of inhibitors of tyrosine kinase. , 2003, European journal of pharmaceutical sciences : official journal of the European Federation for Pharmaceutical Sciences.

[126]  Thomas Kailath,et al.  Rational approximation, harmonic analysis and neural networks , 1992, [Proceedings 1992] IJCNN International Joint Conference on Neural Networks.

[127]  Stephen K. Durham,et al.  Predicting the Genotoxicity of Secondary and Aromatic Amines Using Data Subsetting To Generate a Model Ensemble , 2003, J. Chem. Inf. Comput. Sci..

[128]  Samuel H. Yalkowsky,et al.  Physical chemical properties of drugs , 1980 .

[129]  Vudhichai Parasuk,et al.  QSAR study of antimalarial activities and artemisinin-heme binding properties obtained from docking calculations. , 2000 .

[130]  Robert W. Blanning,et al.  An empirical measure of element contribution in neural networks , 1998, IEEE Trans. Syst. Man Cybern. Part C.

[131]  Alejandro C. Olivieri,et al.  Wavelength Selection for Multivariate Calibration Using a Genetic Algorithm: A Novel Initialization Strategy , 2002, J. Chem. Inf. Comput. Sci..

[132]  Leo Breiman,et al.  Classification and Regression Trees , 1984 .

[133]  Mitchell A. Avery,et al.  Structure–Activity Relationships of Peroxide-Based Artemisinin Antimalarials , 1999 .

[134]  C. Hansch,et al.  p-σ-π Analysis. A Method for the Correlation of Biological Activity and Chemical Structure , 1964 .

[135]  Amit Gupta,et al.  Generalized Analytic Rule Extraction for Feedforward Neural Networks , 1999, IEEE Trans. Knowl. Data Eng..

[136]  C. G. Broyden The Convergence of a Class of Double-rank Minimization Algorithms 2. The New Algorithm , 1970 .

[137]  Adalbert Kerber,et al.  QSPR Using MOLGEN-QSPR: The Example of Haloalkane Boiling Points , 2004, J. Chem. Inf. Model..

[138]  Kurt Hornik,et al.  Some new results on neural network approximation , 1993, Neural Networks.

[139]  S Wold,et al.  Statistical molecular design, parallel synthesis, and biological evaluation of a library of thrombin inhibitors. , 2001, Journal of medicinal chemistry.

[140]  C. Hansch Quantitative approach to biochemical structure-activity relationships , 1969 .

[141]  W L Jorgensen,et al.  Prediction of drug solubility from Monte Carlo simulations. , 2000, Bioorganic & medicinal chemistry letters.

[142]  L. Burggraf,et al.  Hydration of small anions: Calculations by the AM1 semiempirical method , 1991 .

[143]  Eamonn F. Healy,et al.  Development and use of quantum mechanical molecular models. 76. AM1: a new general purpose quantum mechanical molecular model , 1985 .

[144]  A. Tropsha,et al.  Development and validation of k-nearest-neighbor QSPR models of metabolic stability of drug candidates. , 2003, Journal of medicinal chemistry.

[145]  Bert-Jan Baars,et al.  Risk assessment of peak exposure to genotoxic carcinogens: a pragmatic approach. , 2004, Toxicology letters.

[146]  Peter C Jurs,et al.  Predicting the genotoxicity of thiophene derivatives from molecular structure. , 2003, Chemical research in toxicology.

[147]  Stan Matwin,et al.  Addressing the Curse of Imbalanced Training Sets: One-Sided Selection , 1997, ICML.

[148]  Darko Butina,et al.  Modeling Aqueous Solubility , 2003, J. Chem. Inf. Comput. Sci..

[149]  W. Denny,et al.  Structure-activity relationships for 5-substituted 1-phenylbenzimidazoles as selective inhibitors of the platelet-derived growth factor receptor. , 1999, Journal of medicinal chemistry.

[150]  P. Jurs,et al.  Development and use of charged partial surface area structural descriptors in computer-assisted quantitative structure-property relationship studies , 1990 .

[151]  R. Leardi Genetic algorithms in chemometrics and chemistry: a review , 2001 .

[152]  Subhash C. Basak,et al.  Prediction of Mutagenicity of Aromatic and Heteroaromatic Amines from Structure: A Hierarchical QSAR Approach , 2001, J. Chem. Inf. Comput. Sci..

[153]  K. Luthman,et al.  Evaluation of dynamic polar molecular surface area as predictor of drug absorption: comparison with other computational and experimental predictors. , 1998, Journal of medicinal chemistry.

[154]  S. Rajagopal,et al.  Correlations of nitrenium ion selectivities with quantitative mutagenicity and carcinogenicity of the corresponding amines. , 2002, Chemical research in toxicology.

[155]  Brian E. Mattioni,et al.  Prediction of dihydrofolate reductase inhibition and selectivity using computational neural networks and linear discriminant analysis. , 2003, Journal of molecular graphics & modelling.

[156]  K. M. Smith,et al.  Novel software tools for chemical diversity , 1998 .

[157]  LiMin Fu,et al.  Rule Generation from Neural Networks , 1994, IEEE Trans. Syst. Man Cybern. Syst..

[158]  A. Balaban Highly discriminating distance-based topological index , 1982 .

[159]  Ulf Norinder,et al.  Single and domain mode variable selection in 3D QSAR applications , 1996 .

[160]  Rajarshi Guha,et al.  Interpreting Computational Neural Network QSAR Models: A Measure of Descriptor Importance , 2005, J. Chem. Inf. Model..

[161]  W. L. Jorgensen,et al.  Prediction of Properties from Simulations: Free Energies of Solvation in Hexadecane, Octanol, and Water , 2000 .

[162]  Peter J. Rousseeuw,et al.  Robust regression and outlier detection , 1987 .

[163]  José Manuel Benítez,et al.  Interpretation of artificial neural networks by means of fuzzy rules , 2002, IEEE Trans. Neural Networks.

[164]  Lipo Wang,et al.  Rule extraction by genetic algorithms based on a simplified RBF neural network , 2001, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546).

[165]  N. Metropolis,et al.  Equation of State Calculations by Fast Computing Machines , 1953, Resonance.

[166]  John H. Kalivas,et al.  Comparison of Forward Selection, Backward Elimination, and Generalized Simulated Annealing for Variable Selection , 1993 .

[167]  H. Ishibuchi,et al.  Fuzzy-arithmetic-based approach for extracting positive and negative linguistic rules from trained neural networks , 1999, FUZZ-IEEE'99. 1999 IEEE International Fuzzy Systems. Conference Proceedings (Cat. No.99CH36315).

[168]  Thomas G. Dietterich An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees: Bagging, Boosting, and Randomization , 2000, Machine Learning.

[169]  Ramon Carbó-Dorca,et al.  Modeling Antimalarial Activity: Application of Kinetic Energy Density Quantum Similarity Measures as Descriptors in QSAR , 2000, J. Chem. Inf. Comput. Sci..

[170]  Carlos R Rodrigues,et al.  Structure-activity relationships of the antimalarial agent artemisinin. 6. The development of predictive in vitro potency models using CoMFA and HQSAR methodologies. , 2002, Journal of medicinal chemistry.

[171]  Timothy Clark,et al.  New Molecular Descriptors Based on Local Properties at the Molecular Surface and a Boiling-Point Model Derived from Them , 2004, J. Chem. Inf. Model..

[172]  H. Kubinyi,et al.  3D QSAR in drug design. , 2002 .

[173]  Robert P. Sheridan,et al.  Random Forest: A Classification and Regression Tool for Compound Classification and QSAR Modeling , 2003, J. Chem. Inf. Comput. Sci..

[174]  Rajarshi Guha,et al.  Development of QSAR Models To Predict and Interpret the Biological Activity of Artemisinin Analogues , 2004, J. Chem. Inf. Model..

[175]  H. Tsukimoto,et al.  Rule extraction from neural networks via decision tree induction , 2001, IJCNN'01. International Joint Conference on Neural Networks. Proceedings (Cat. No.01CH37222).

[176]  Peter C. Jurs,et al.  Automated Descriptor Selection for Quantitative Structure-Activity Relationships Using Generalized Simulated Annealing , 1995, J. Chem. Inf. Comput. Sci..

[177]  Poonsakdi Ploypradith,et al.  Evidence for Fe(IV):O in the Molecular Mechanism of Action of the Trioxane Antimalarial Artemisinin , 1995 .

[178]  Kristina Luthman,et al.  Prediction of Membrane Permeability to Peptides from Calculated Dynamic Molecular Surface Properties , 1999, Pharmaceutical Research.

[179]  Martyn G. Ford,et al.  Selecting Screening Candidates for Kinase and G Protein-Coupled Receptor Targets Using Neural Networks , 2002, J. Chem. Inf. Comput. Sci..

[180]  Marina Lasagni,et al.  New molecular descriptors for 2D and 3D structures. Theory , 1994 .

[181]  D. Agrafiotis,et al.  Feature selection for structure-activity correlation using binary particle swarms. , 2002, Journal of medicinal chemistry.

[182]  D. E. Goldberg,et al.  Genetic Algorithms in Search, Optimization & Machine Learning , 1989 .

[183]  Walter Cedeño,et al.  On the Use of Neural Network Ensembles in QSAR and QSPR , 2002, J. Chem. Inf. Comput. Sci..

[184]  Warren T. Jones,et al.  DENDRITE: A system for visual interpretation of neural network data , 1992, Proceedings IEEE Southeastcon '92.

[185]  A. Ullrich,et al.  Growth factor signaling by receptor tyrosine kinases , 1992, Neuron.

[186]  Marjana Novic,et al.  Investigation of Infrared Spectra-Structure Correlation Using Kohonen and Counterpropagation Neural Network , 1995, J. Chem. Inf. Comput. Sci..

[187]  David T. Stanton,et al.  Evaluation and Use of BCUT Descriptors in QSAR and QSPR Studies , 1999, J. Chem. Inf. Comput. Sci..

[188]  Johann Gasteiger,et al.  The Coding of the Three-Dimensional Structure of Molecules by Molecular Transforms and Its Application to Structure-Spectra Correlations and Studies of Biological Activity , 1996, J. Chem. Inf. Comput. Sci..

[189]  Giuseppina C. Gini,et al.  Description of the Electronic Structure of Organic Chemicals Using Semiempirical and Ab Initio Methods for Development of Toxicological QSARs , 2005, J. Chem. Inf. Model..

[190]  R. Venkataraghavan,et al.  Atom pairs as molecular features in structure-activity studies: definition and applications , 1985, J. Chem. Inf. Comput. Sci..

[191]  L B Kier,et al.  Molecular connectivity. I: Relationship to nonspecific local anesthesia. , 1975, Journal of pharmaceutical sciences.

[192]  Keith Abe,et al.  Identification of orally active, potent, and selective 4-piperazinylquinazolines as antagonists of the platelet-derived growth factor receptor tyrosine kinase family. , 2002, Journal of medicinal chemistry.

[193]  J. Cumming,et al.  EVIDENCE FOR THE IMPORTANCE OF HIGH-VALENT FE=O AND OF A DIKETONE IN THE MOLECULAR MECHANISM OF ACTION OF ANTIMALARIAL TRIOXANE ANALOGS OF ARTEMISININ , 1996 .

[194]  D. E. Clark Rapid calculation of polar molecular surface area and its application to the prediction of transport phenomena. 1. Prediction of intestinal absorption. , 1999, Journal of pharmaceutical sciences.

[195]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[196]  E. Dow,et al.  Automated analysis of proton NMR spectra from combinatorial rapid parallel synthesis using self-organizing maps. , 2002, Journal of combinatorial chemistry.

[197]  L B Kier,et al.  Molecular connectivity VII: specific treatment of heteroatoms. , 1976, Journal of pharmaceutical sciences.

[198]  Harpreet S. Chadha,et al.  Molecular Factors Influencing Drug Transfer across the Blood‐Brain Barrier , 1997, The Journal of pharmacy and pharmacology.

[199]  Donald W. Miller,et al.  Brain uptake of drugs : the influence of chemical and biological factors , 1992 .

[200]  C. Hansch,et al.  Comparative QSAR study of tyrosine kinase inhibitors. , 2001, Chemical reviews.

[201]  David T. Stanton,et al.  On the Physical Interpretation of QSAR Models , 2003, J. Chem. Inf. Comput. Sci..

[202]  A. Gown,et al.  Platelet-derived growth factor (PDGF) and PDGF receptor are induced in mesangial proliferative nephritis in the rat. , 1991, Proceedings of the National Academy of Sciences of the United States of America.

[203]  Anne Robert,et al.  Is alkylation the main mechanism of action of the antimalarial drug artemisinin , 1998 .

[204]  Susu Yao,et al.  Evolving fuzzy neural networks for extracting rules , 1996, Proceedings of IEEE 5th International Fuzzy Systems.

[205]  H. Kubinyi,et al.  Three-dimensional quantitative similarity-activity relationships (3D QSiAR) from SEAL similarity matrices. , 1998, Journal of medicinal chemistry.

[206]  P. Jurs,et al.  Prediction of peptide ion collision cross sections from topological molecular structure and amino acid parameters. , 2002, Analytical chemistry.