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Weka machine learning for predicting the phospholipidosis inducing potential

Current topics in medicinal chemistry, 2008-12, Vol.8 (18), p.1691-1709

Ivanciuc, Ovidiu

2008

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Applications of Support Vector Machines in Chemistry

Reviews in Computational Chemistry, 2007-01, p.291-400

2007

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Chemical graphs, molecular matrices and topological indices in chemoinformatics and quantitative structure-activity relationships

Current computer-aided drug design, 2013-06, Vol.9 (2), p.153-163

Ivanciuc, Ovidiu

2013

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Quantitative Structure – Activity Relationships (QSAR) with the MolNet Molecular Graph Machine

Current bioinformatics, 2011-06, Vol.6 (2), p.261-268

Ivanciuc, Ovidiu

2011

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Meet the Guest Editor

Combinatorial chemistry & high throughput screening, 2009-06, Vol.12 (5), p.520-520

Ivanciuc, Ovidiu

2009

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Bioinformatics Approaches to Classifying Allergens and Predicting Cross-Reactivity

Immunology and allergy clinics of North America, 2007-02, Vol.27 (1), p.1-27

2007

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Network-QSAR with Reaction Poset Quantitative Superstructure-Activity Relationships (QSSAR) for PCB Chromatographic Properties

Current bioinformatics, 2011-03, Vol.6 (1), p.25-34

2011

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Data mining of sequences and 3D structures of allergenic proteins

Bioinformatics, 2002-10, Vol.18 (10), p.1358-1364

2002

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Prediction of Environmental Properties for Chlorophenols with Posetic Quantitative Super-Structure/Property Relationships (QSSPR)

International journal of molecular sciences, 2006-09, Vol.7 (9), p.358-374

2006

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Flow network QSAR for the prediction of physicochemical properties by mapping an electrical resistance network onto a chemical reaction poset

Current computer-aided drug design, 2013-06, Vol.9 (2), p.233-240

2013

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Combinatorics of reaction-network posets

Combinatorial chemistry & high throughput screening, 2008-11, Vol.11 (9), p.723-733

2008

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Robust quantitative modeling of peptide binding affinities for MHC molecules using physical-chemical descriptors

Protein and peptide letters, 2007-01, Vol.14 (9), p.903-916

2007

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Machine Learning Quantitative Structure-Activity Relationships (QSAR) for Peptides Binding to the Human Amphiphysin-1 SH3 Domain

Current proteomics, 2009-12, Vol.6 (4), p.289-302

Ivanciuc, Ovidiu

2009

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Posetic cluster expansion for substitution-reaction networks and application to methylated cyclobutanes

Journal of mathematical chemistry, 2007-05, Vol.41 (4), p.355-379

2007

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Using property based sequence motifs and 3D modeling to determine structure and functional regions of proteins

Current medicinal chemistry, 2004-03, Vol.11 (5), p.583-593

2004

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Editorial (Hot Topic: Molecular Topology and Chemical Connectivity)

Current computer-aided drug design, 2013-06, Vol.9 (2), p.151-152

Ivanciuc, Ovidiu

2013

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Quantitative structure-property relationship study of normal boiling points for halogen-/ oxygen-/ sulfur-containing organic compounds using the CODESSA program

Tetrahedron, 1998-07, Vol.54 (31), p.9129-9142

1998

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An Allergen Portrait Gallery: Representative Structures and an Overview of IgE Binding Surfaces

Bioinformatics and Biology Insights, 2010-01, Vol.2010 (4), p.113-125

2010

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SDAP: database and computational tools for allergenic proteins

Nucleic acids research, 2003-01, Vol.31 (1), p.359-362

2003

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Editorial [Hot Topic: Machine Learning for Virtual Screening (Part 2) (Guest Editor: Ovidiu Ivanciuc)]

Combinatorial chemistry & high throughput screening, 2009-06, Vol.12 (5), p.451-452

Ivanciuc, Ovidiu

2009

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Machine learning for virtual screening (part 2)

Combinatorial chemistry & high throughput screening, 2009-06, Vol.12 (5), p.451

Ivanciuc, Ovidiu

2009

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Editorial [Hot Topic: Machine Learning for Virtual Screening (Part 1) (Guest Editor: Ovidiu Ivanciuc)]

Combinatorial chemistry & high throughput screening, 2009-05, Vol.12 (4), p.330-331

Ivanciuc, Ovidiu

2009

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Machine learning for virtual screening (part 1)

Combinatorial chemistry & high throughput screening, 2009-05, Vol.12 (4), p.330

Ivanciuc, Ovidiu

2009

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Modeling the bioconcentration factors and bioaccumulation factors of polychlorinated biphenyls with posetic quantitative super-structure/activity relationships (QSSAR)

Molecular diversity, 2006-05, Vol.10 (2), p.133-145

2006

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Assessment of 3D models for allergen research

Proteins, structure, function, and bioinformatics, 2013-04, Vol.81 (4), p.545-554

2013

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Weka machine learning for predicting the phospholipidosis inducing potential

Applications of Support Vector Machines in Chemistry

Chemical graphs, molecular matrices and topological indices in chemoinformatics and quantitative structure-activity relationships

Quantitative Structure – Activity Relationships (QSAR) with the MolNet Molecular Graph Machine

Meet the Guest Editor

Bioinformatics Approaches to Classifying Allergens and Predicting Cross-Reactivity

Network-QSAR with Reaction Poset Quantitative Superstructure-Activity Relationships (QSSAR) for PCB Chromatographic Properties

Data mining of sequences and 3D structures of allergenic proteins

Prediction of Environmental Properties for Chlorophenols with Posetic Quantitative Super-Structure/Property Relationships (QSSPR)

Flow network QSAR for the prediction of physicochemical properties by mapping an electrical resistance network onto a chemical reaction poset

Combinatorics of reaction-network posets

Robust quantitative modeling of peptide binding affinities for MHC molecules using physical-chemical descriptors

Machine Learning Quantitative Structure-Activity Relationships (QSAR) for Peptides Binding to the Human Amphiphysin-1 SH3 Domain

Posetic cluster expansion for substitution-reaction networks and application to methylated cyclobutanes

Using property based sequence motifs and 3D modeling to determine structure and functional regions of proteins

Editorial (Hot Topic: Molecular Topology and Chemical Connectivity)

Quantitative structure-property relationship study of normal boiling points for halogen-/ oxygen-/ sulfur-containing organic compounds using the CODESSA program

An Allergen Portrait Gallery: Representative Structures and an Overview of IgE Binding Surfaces

SDAP: database and computational tools for allergenic proteins

Editorial [Hot Topic: Machine Learning for Virtual Screening (Part 2) (Guest Editor: Ovidiu Ivanciuc)]

Machine learning for virtual screening (part 2)

Editorial [Hot Topic: Machine Learning for Virtual Screening (Part 1) (Guest Editor: Ovidiu Ivanciuc)]

Machine learning for virtual screening (part 1)

Modeling the bioconcentration factors and bioaccumulation factors of polychlorinated biphenyls with posetic quantitative super-structure/activity relationships (QSSAR)

Assessment of 3D models for allergen research

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