Machine Learning Technique Approaches in Drug Discovery, Design and Development

[1]  Vladimir Vapnik,et al.  Principles of Risk Minimization for Learning Theory , 1991, NIPS.

[2]  Valerie J Gillet,et al.  Multiobjective optimization in quantitative structure-activity relationships: deriving accurate and interpretable QSARs. , 2002, Journal of medicinal chemistry.

[3]  B. Roth,et al.  Magic shotguns versus magic bullets: selectively non-selective drugs for mood disorders and schizophrenia , 2004, Nature Reviews Drug Discovery.

[4]  Tingjun Hou,et al.  Recent development and application of virtual screening in drug discovery: an overview. , 2004, Current pharmaceutical design.

[5]  David M. Reif,et al.  Integrated analysis of genetic, genomic and proteomic data , 2004, Expert review of proteomics.

[6]  André Schrattenholz,et al.  Proteomics: how to control highly dynamic patterns of millions of molecules and interpret changes correctly? , 2004, Drug discovery today. Technologies.

[7]  Ruisheng Zhang,et al.  QSAR and classification models of a novel series of COX-2 selective inhibitors: 1, 5-diarylimidazoles based on support vector machines , 2004, J. Comput. Aided Mol. Des..

[8]  Allen D. Roses,et al.  Genome-based pharmacogenetics and the pharmaceutical industry , 2002, Nature Reviews Drug Discovery.

[9]  Yael Mandel-Gutfreund,et al.  Exploring functional relationships between components of the gene expression machinery , 2005, Nature Structural &Molecular Biology.

[10]  Albert P. Li,et al.  Preclinical in vitro screening assays for drug-like properties. , 2005, Drug discovery today. Technologies.

[11]  David Haussler,et al.  Classifying G-protein coupled receptors with support vector machines , 2002, Bioinform..

[12]  A. Hagler,et al.  Chemoinformatics and Drug Discovery , 2002, Molecules : A Journal of Synthetic Chemistry and Natural Product Chemistry.

[13]  Nello Cristianini,et al.  A statistical framework for genomic data fusion , 2004, Bioinform..

[14]  Ru-Qin Yu,et al.  Hybridized particle swarm algorithm for adaptive structure training of multilayer feed‐forward neural network: QSAR studies of bioactivity of organic compounds , 2004, J. Comput. Chem..