Quantitative structure-activity relationship studies of TIBO derivatives using support vector machines

References

• Jacobo-Molina , A and Arnold , E . 1991 . HIV reverse transcriptase structure-function relationships . Biochemistry , 30 : 6351 – 6632 .
   PubMed Web of Science ®Google Scholar
• De Clercq , E . 1996 . Non-nucleoside reverse transcriptase inhibitors (NNRTIs) for the treatment of human immunodeficiency virus type 1 (HIV-1) infections: Strategies to overcome drug resistance development . Med. Chem. Rev. , 16 : 125 – 157 .
   Google Scholar
• De Clercq , E . 1995 . Antiviral therapy for human immunodeficiency virus infections . Clin. Microbiol. Rev. , 8 : 200 – 239 .
   PubMed Web of Science ®Google Scholar
• De Clercq , E . 1996 . What can be expected from non-nucleoside reverse transcriptase inhibitors (NNRTIs) in the treatment of human immunodeficiency virus type 1 (HIV-1) infections . Rev. Med. Virol. , 6 : 97 – 117 .
   Google Scholar
• Balzarini , J , Karlsson , A , Perez-Perez , MJ , Camarasa , MJ and De Clercq , E . 1993 . Knocking-out concentrations of HIV-1-specific inhibitors completely suppress HIV-1 infection and prevent the emergence of drug-resistant virus . Virology , 196 : 576 – 585 .
   PubMed Web of Science ®Google Scholar
• Vasudevachari , MB , Battista , C , Lane , HC , Psallidopoulos , MC , Zhao , B , Cook , J , Palmer , JR , Romero , DL , Tarpley , WG and Salzman , NP . 1992 . Prevention of the spread of HIV-1 infection with nonnucleoside reverse transcriptase inhibitors . Virology , 190 : 269 – 277 .
   PubMedGoogle Scholar
• Pauwels , R , Andries , K , Desmyter , J , Schols , D , Kukla , MJ , Breslin , HJ , Raeymaechers , A , Gelder , JV , Woestenborgs , R , Heykants , J , Schellekens , K , Janssen , MAC , DeClercq , E and Janssen , PAJ . 1990 . Potent and selective inhibition of HIV-1 replication in vitro by a novel series of TIBO derivative . Nature , 343 : 470 – 474 .
   PubMed Web of Science ®Google Scholar
• Hansch , C , Hoekman , D and Gao , H . 1996 . Comparative QSAR: Toward a deeper understanding of chemicobiological interactions . Chem. Rev. , 96 : 1045 – 1075 .
   PubMed Web of Science ®Google Scholar
• Karelson , M , Lobanov , VS and Katritzky , AR . 1996 . Quantum-chemical descriptors in QSAR/QSPR Studies . Chem. Rev. , 96 : 1027 – 1043 .
   PubMed Web of Science ®Google Scholar
• Garg , R , Gupta , SP , Gao , H , Babu , MS and Debnath , AK . 1999 . Comparative quantitative structure-activity relationship studies on anti-HIV drugs . Chem. Rev. , 99 : 3525 – 3601 .
   PubMed Web of Science ®Google Scholar
• Sattwa , MA and Kunal , R . 2009 . Predictive QSAR modeling of HIV reverse transcriptase inhibitor TIBO derivatives . Eur. J. Med. Chem. , 44(4) : 1509 – 1524 .
   Google Scholar
• Sapre , NS , Gupta , S , Pancholi , N and Sapre , N . 2009 . A group center overlap based approach for «3D QSAR» studies on TIBO derivatives . J. Comput. Chem. , 30(6) : 922 – 933 .
   Google Scholar
• Zupan , J and Gasteiger , J . 1993 . Neural Networks for Chemists: An Introduction , Weinheim : Wiley-VCH .
   Google Scholar
• Guyon , I , Weston , J , Barnhill , S and Vapnik , V . 2002 . Gene selection for cancer classification using support vector machines . Mach. Learn. , 46 : 389 – 422 .
   Web of Science ®Google Scholar
• Liu , HX , Zhang , RS , Luan , F , Yao , XJ , Liu , MC , Hu , ZD and Fan , BT . 2003 . Diagnosing breast cancer based on support vector machines . J. Chem. Inf. Comput. Sci. , 43 : 900 – 907 .
   PubMedGoogle Scholar
• Cai , YD , Liu , XJ , Xu , XB and Chou , KC . 2002 . Diagnosing breast cancer based on support vector machines . J. Comput. Chem. , 23 : 267 – 274 .
   PubMed Web of Science ®Google Scholar
• Hua , SJ and Sun , ZR . 2001 . A novel method of protein secondary structure prediction with high segment overlap measure: Support vector machine approach . J. Mol. Biol. , 308 : 397 – 407 .
   PubMed Web of Science ®Google Scholar
• Norinder , U . 2003 . Support vector machine models in drug design: applications to drug transport processes and QSAR using simplex optimisations and variable selection . Neurocomputing , 55 : 337 – 346 .
   Web of Science ®Google Scholar
• Hu , R , Doucet , JP , Delamar , M and Zhang , R . 2009 . QSAR models for 2-amino-6-arylsulfonylbenzonitriles and congeners HIV-1 reverse transcriptase inhibitors based on linear and nonlinear regression methods . Eur. J. Med. Chem. , 44 : 2158 – 2171 .
   PubMed Web of Science ®Google Scholar
• Darnag , R , Schmitzer , A , Belmiloud , Y , Villemin , D , Jarid , A , Chait , A , Seyagh , M and Cherqaoui , D . 2009 . QSAR studies of HEPT derivatives using support vector machines . QSAR. Comb. Sci. , 28 : 709 – 718 .
   Google Scholar
• Pavan , M , Netzeva , TI and Worth , AP . 2006 . Validation of a QSAR model for acute toxicity . SAR QSAR Environ. Res. , 17 : 147 – 171 .
   PubMed Web of Science ®Google Scholar
• Hannongbua , S , Pungpo , P , Limtrakul , J and Wolschann , P . 1999 . Quantitative structure-activity relationships and comparative molecular field analysis of TIBO derivatives HIV-1 reverse transcriptase inhibitors . J. Comput. Aided Mol. Design , 7 : 2283 – 2292 .
   Google Scholar
• Gupta , SP and Garg , R . 1996 . Quantitative structure-activity relationship studies on anti-HIV-1 TIBO derivatives as inhibitors of viral reverse transcriptase . J. Enzyme. Inhib. , 11 : 23 – 32 .
   Web of Science ®Google Scholar
• Hannongbua , S , Pungpo , P , Limtrakul , J and Wolschann , PJ . 1999 . Quantitative structure-activity relationships and comparative molecular field analysis of TIBO derivatised HIV-1 reverse transcriptase inhibitors . Comput. Aided Mol. Design. , 13 : 563 – 577 .
   Google Scholar
• Belmiloud , Y . Theoretical modeling and QSAR studies of TIBO derivatives, PhD thesis, Science and Technology University of Alger, 2009
   Google Scholar
• Cherqaoui , D and Villemin , D . 1994 . Use of a neural network to determine boiling point of alkanes . J. Chem. Soc. Faraday. Trans. , 90 : 97 – 102 .
   Google Scholar
• Freeman , JA and Skapura , DM . 1991 . Neural Networks Algorithms, Applications, and programming Techniques , Reading : Addition-Wesley Publishing Company .
   Google Scholar
• Vapnik , VN . 1998 . Statistical Learning Theory , New York : John Wiley & Sons .
   Google Scholar
• Cristianini , N and Shawe-Taylor , J . 2000 . An Introduction to Support Vector Machine , Cambridge, UK : Cambridge University Press .
   Google Scholar
• Scholkopf , B and Smola , A . 2002 . Learning with Kernels , Cambridge MA : MIT Press .
   Google Scholar
• Chang , CC and Lin , CJ . LIBSVM-A Library for support vector machine. Available at http://www/csie.edu/tw/cjlin/libs/libsvm
   Google Scholar
• So , S and Richards , G . 1992 . Application of neural networks: Quantitative structure-activity relationships of the derivatives of 2,4-diamino-5-(substituted-benzyl) pyrimidines as DHFR Inhibitors . J. Med. Chem. , 35 : 3201 – 3207 .
   PubMed Web of Science ®Google Scholar
• Andrea , TA and Kalayeh , H . 1991 . Application of neural networks in quantitative structure-activity relationships of dihydrofolate reductase inhibitors . J. Med. Chem. , 34 : 2824 – 2836 .
   PubMed Web of Science ®Google Scholar
• Lin , HT and Lin , CJ . A Study on Sigmoid Kernels for SVM and the Training of non-PSD Kernels by SMO-type Methods, National Taiwan University, 2004. Available at http://www.csie.ntu.edu.tw/~cjlin/papers/tanh.pdf
   Google Scholar
• Wang , WJ , Xu , ZB , Lu , WZ and Zhang , XY . 2003 . Determination of the spread parameter in the Gaussian kernel for classification and regression . Neurocomputing , 55 : 643 – 663 .
   Web of Science ®Google Scholar
• Tropsha , A , Gramatica , P and Gombar , VK . 2003 . The importance of being earnest: Validation is the absolute essential for successful application and interpretation of QSPR models . Quant. Struct-Act. Relat. , 22 : 1 – 9 .
   Google Scholar
• Eriksson , L , Jaworska , J , Worth , AP , Cronin , MT , McDowell , RM and Gramatica , P . 2003 . Methods for reliability and uncertainty assessment and for applicability evaluations of classification and regression-based QSARs . Environ. Health. Perspect. , 111 : 1361 – 1375 .
   PubMed Web of Science ®Google Scholar
• Gramatica , P . 2007 . Principles of QSAR models validation: internal and external . QSAR. Comb. Sci. , 26 : 694 – 701 .
   Google Scholar
• Cherqaoui , D , Esseffar , M , Villemin , D , Cense , JM , Chastrette , M and Zakarya , D . 1998 . Structure musk odour relationships studies of tetralin and indan compounds using neural networks . New. J. Chem. , 22 : 839 – 843 .
   Web of Science ®Google Scholar
• De Clercq , E . 1999 . Perspective of non-nucleoside reverse transcriptase inhibitors (NNRTIs) in the therapy of HIV-1 infection . Il Farmaco. , 54 : 26 – 45 .
   PubMedGoogle Scholar
• Douali , L , Villemin , D and Cherqaoui , D . 2004 . Exploring QSAR of non-nucleoside reverse transcriptase inhibitors by neural networks: TIBO derivatives . Int. J. Mol. Sci. , 5 : 8 – 55 .
   Google Scholar
• Esnouf , R , Ren , J , Ross , C , Jones , Y , Stammers , D and Stuart , D . 1995 . Mechanism of inhibition of HIV-1 reverse transcriptase by non-nucleoside inhibitors . Nat. Struct. Biol. , 2 : 303 – 308 .
   PubMedGoogle Scholar