Support vector machines: development of QSAR models for predicting anti-HIV-1 activity of TIBO derivatives.

[1]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[2]  Marcello Imbriani,et al.  Development of QSAR models for predicting hepatocarcinogenic toxicity of chemicals. , 2009, European journal of medicinal chemistry.

[3]  J. Doucet,et al.  QSAR models for 2-amino-6-arylsulfonylbenzonitriles and congeners HIV-1 reverse transcriptase inhibitors based on linear and nonlinear regression methods. , 2009, European journal of medicinal chemistry.

[4]  Kunal Roy,et al.  Predictive QSAR modeling of HIV reverse transcriptase inhibitor TIBO derivatives. , 2009, European journal of medicinal chemistry.

[5]  X. Y. Zhang,et al.  Application of support vector machine (SVM) for prediction toxic activity of different data sets. , 2006, Toxicology.

[6]  B. Hemmateenejad,et al.  Computer-aided design of potential anti-HIV-1 non-nucleoside reverse transcriptase inhibitors by contraction of β-ring in TIBO derivatives , 2005 .

[7]  Jie Yang,et al.  Support Vector Machine In Chemistry , 2004 .

[8]  Didier Villemin,et al.  Exploring QSAR of Non-Nucleoside Reverse Transcriptase Inhibitors by Neural Networks: TIBO Derivatives , 2004 .

[9]  Jason Weston,et al.  Gene Selection for Cancer Classification using Support Vector Machines , 2002, Machine Learning.

[10]  Christopher J. C. Burges,et al.  A Tutorial on Support Vector Machines for Pattern Recognition , 1998, Data Mining and Knowledge Discovery.

[11]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[12]  Ulf Norinder,et al.  Support vector machine models in drug design: applications to drug transport processes and QSAR using simplex optimisations and variable selection , 2003, Neurocomputing.

[13]  Feng Luan,et al.  Diagnosing Breast Cancer Based on Support Vector Machines , 2003, J. Chem. Inf. Comput. Sci..

[14]  Paola Gramatica,et al.  The Importance of Being Earnest: Validation is the Absolute Essential for Successful Application and Interpretation of QSPR Models , 2003 .

[15]  Rudolf Kiralj,et al.  A priori molecular descriptors in QSAR: a case of HIV-1 protease inhibitors. I. The chemometric approach. , 2003, Journal of molecular graphics & modelling.

[16]  Lowell H. Hall,et al.  E-State Modeling of HIV-1 Protease Inhibitor Binding Independent of 3D Information , 2002, J. Chem. Inf. Comput. Sci..

[17]  Kuo-Chen Chou,et al.  Support vector machines for predicting HIV protease cleavage sites in protein , 2002, J. Comput. Chem..

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

[19]  T. Poggio,et al.  Multiclass cancer diagnosis using tumor gene expression signatures , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[20]  S. Hua,et al.  A novel method of protein secondary structure prediction with high segment overlap measure: support vector machine approach. , 2001, Journal of molecular biology.

[21]  Jarmo Huuskonen.  QSAR Modeling with the Electrotopological State: TIBO Derivatives , 2001 .

[22]  Nello Cristianini,et al.  Support vector machine classification and validation of cancer tissue samples using microarray expression data , 2000, Bioinform..

[23]  Supa Hannongbua,et al.  Accessible Charges in Structure-Activity Relationships. A Study on HEPT-based HIV-1 RT Inhibitors , 2000 .

[24]  Nello Cristianini,et al.  An introduction to Support Vector Machines , 2000 .

[25]  Andrea Zaliani,et al.  Global 3D-QSAR methods: MS-WHIM and autocorrelation , 2000, J. Comput. Aided Mol. Des..

[26]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[27]  C. Hansch,et al.  Comparative Quantitative Structure−Activity Relationship Studies on Anti-HIV Drugs , 1999 .

[28]  Didier Villemin,et al.  Structure-musk odour relationship studies of tetralin and indan compounds using neural networks , 1998 .

[29]  R Pabst,et al.  HIV-induced decline in blood CD4/CD8 ratios: viral killing or altered lymphocyte trafficking? , 1998, Immunology today.

[30]  Erik De Clercq,et al.  Toward improved anti-HIV chemotherapy: therapeutic strategies for intervention with HIV infections. , 1995 .

[31]  J. Mills,et al.  HIV replication in chronically infected macrophages is not inhibited by the Tat inhibitors Ro‐5‐3335 and Ro‐24‐7429 , 1994, Journal of leukocyte biology.

[32]  E. De Clercq,et al.  Knocking-out concentrations of HIV-1-specific inhibitors completely suppress HIV-1 infection and prevent the emergence of drug-resistant virus. , 1993, Virology.

[33]  Johann Gasteiger,et al.  Neural Networks for Chemists: An Introduction , 1993 .

[34]  H. Lane,et al.  Prevention of the spread of HIV-1 infection with nonnucleoside reverse transcriptase inhibitors. , 1992, Virology.

[35]  Erik De Clercq,et al.  Potent and selective inhibition of HIV-1 replication in vitro by a novel series of TIBO derivatives , 1990, Nature.

[36]  A. Leo,et al.  Hydrophobicity and central nervous system agents: on the principle of minimal hydrophobicity in drug design. , 1987, Journal of pharmaceutical sciences.

[37]  P. Howley,et al.  The colinear alignment of the genomes of papovaviruses JC, BK, and SV40. , 1979, Virology.

[38]  James W. McFarland,et al.  Parabolic relation between drug potency and hydrophobicity , 1970 .

[39]  J. Mercer.  Functions of Positive and Negative Type, and their Connection with the Theory of Integral Equations , 1909 .