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ORCIDMartin Vogt 0001
Person information
- affiliation: University of Bonn, LIMES Institute, Germany
Other persons with the same name
- Martin Vogt — disambiguation page
- Martin Vogt 0002 — Trier University of Applied Sciences, Germany (and 1 more)
- Martin Vogt 0003 — Fraunhofer Institute for Applied Information Technology (FIT), Sankt Augustin, Germany (and 1 more)
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Journal Articles
- 2021
[j36]Javed Iqbal, Martin Vogt, Jürgen Bajorath
:
Prediction of activity cliffs on the basis of images using convolutional neural networks. J. Comput. Aided Mol. Des. 35(12): 1157-1164 (2021)- 2020
- [j35]Javed Iqbal, Martin Vogt, Jürgen Bajorath:
Activity landscape image analysis using convolutional neural networks. J. Cheminformatics 12(1): 34 (2020) - [j34]Gerald M. Maggiora, José L. Medina-Franco, Javed Iqbal, Martin Vogt, Jürgen Bajorath:
From Qualitative to Quantitative Analysis of Activity and Property Landscapes. J. Chem. Inf. Model. 60(12): 5873-5880 (2020) - 2019
- [j33]Filip Miljkovic, Martin Vogt, Jürgen Bajorath:
Systematic computational identification of promiscuity cliff pathways formed by inhibitors of the human kinome. J. Comput. Aided Mol. Des. 33(6): 559-572 (2019) - 2017
- [j32]Raquel Rodríguez-Pérez, Martin Vogt, Jürgen Bajorath:
Influence of Varying Training Set Composition and Size on Support Vector Machine-Based Prediction of Active Compounds. J. Chem. Inf. Model. 57(4): 710-716 (2017) - 2016
- [j31]Mengjun Wu, Martin Vogt, Gerald M. Maggiora, Jürgen Bajorath:
Design of chemical space networks on the basis of Tversky similarity. J. Comput. Aided Mol. Des. 30(1): 1-12 (2016) - [j30]Martin Vogt, Dagmar Stumpfe, Gerald M. Maggiora, Jürgen Bajorath:
Lessons learned from the design of chemical space networks and opportunities for new applications. J. Comput. Aided Mol. Des. 30(3): 191-208 (2016) - [j29]Ryo Kunimoto, Martin Vogt, Jürgen Bajorath:
Maximum common substructure-based Tversky index: an asymmetric hybrid similarity measure. J. Comput. Aided Mol. Des. 30(7): 523-531 (2016) - 2015
- [j28]Magdalena Zwierzyna, Martin Vogt, Gerald M. Maggiora, Jürgen Bajorath:
Design and characterization of chemical space networks for different compound data sets. J. Comput. Aided Mol. Des. 29(2): 113-125 (2015) - [j27]Roman Garnett, Thomas Gärtner, Martin Vogt, Jürgen Bajorath:
Introducing the 'active search' method for iterative virtual screening. J. Comput. Aided Mol. Des. 29(4): 305-314 (2015) - [j26]Bijun Zhang, Martin Vogt, Gerald M. Maggiora, Jürgen Bajorath:
Comparison of bioactive chemical space networks generated using substructure- and fingerprint-based measures of molecular similarity. J. Comput. Aided Mol. Des. 29(7): 595-608 (2015) - [j25]Bijun Zhang, Martin Vogt, Gerald M. Maggiora, Jürgen Bajorath:
Design of chemical space networks using a Tanimoto similarity variant based upon maximum common substructures. J. Comput. Aided Mol. Des. 29(10): 937-950 (2015) - [j24]Bijun Zhang, Martin Vogt, Gerald M. Maggiora, Jürgen Bajorath:
Erratum to: Design of chemical space networks using a Tanimoto similarity variant based upon maximum common substructures. J. Comput. Aided Mol. Des. 29(11): 1071-1072 (2015) - 2014
- [j23]Bijun Zhang, Martin Vogt, Jürgen Bajorath:
Design of an activity landscape view taking compound-based feature probabilities into account. J. Comput. Aided Mol. Des. 28(9): 919-926 (2014) - [j22]Shilva Kayastha, Dilyana Dimova, Preeti Iyer, Martin Vogt, Jürgen Bajorath:
Large-Scale Assessment of Activity Landscape Feature Probabilities of Bioactive Compounds. J. Chem. Inf. Model. 54(2): 442-450 (2014) - 2013
- [j21]Martin Vogt, Jürgen Bajorath:
Statistical modeling of value distributions of similarity coefficients in virtual screening and its application to predicting fingerprint search performance. J. Cheminformatics 5(S-1): 5 (2013) - [j20]Mohsen Ahmadi, Martin Vogt, Preeti Iyer, Jürgen Bajorath, Holger Fröhlich:
Predicting Potent Compounds via Model-Based Global Optimization. J. Chem. Inf. Model. 53(3): 553-559 (2013) - [j19]Martin Vogt, Preeti Iyer, Gerald M. Maggiora, Jürgen Bajorath:
Conditional Probabilities of Activity Landscape Features for Individual Compounds. J. Chem. Inf. Model. 53(7): 1602-1612 (2013) - [j18]Martin Vogt, Jürgen Bajorath:
Similarity Searching for Potent Compounds Using Feature Selection. J. Chem. Inf. Model. 53(7): 1613-1619 (2013) - [j17]Jenny Balfer, Martin Vogt, Jürgen Bajorath:
Searching for Closely Related Ligands with Different Mechanisms of Action Using Machine Learning and Mapping Algorithms. J. Chem. Inf. Model. 53(9): 2252-2274 (2013) - 2012
- [j16]Xiaoying Hu, Ye Hu, Martin Vogt, Dagmar Stumpfe, Jürgen Bajorath:
MMP-Cliffs: Systematic Identification of Activity Cliffs on the Basis of Matched Molecular Pairs. J. Chem. Inf. Model. 52(5): 1138-1145 (2012) - [j15]Preeti Iyer, Dilyana Dimova, Martin Vogt, Jürgen Bajorath:
Navigating High-Dimensional Activity Landscapes: Design and Application of the Ligand-Target Differentiation Map. J. Chem. Inf. Model. 52(8): 1962-1969 (2012) - [j14]Bijun Zhang, Anne Mai Wassermann, Martin Vogt, Jürgen Bajorath:
Systematic Assessment of Compound Series with SAR Transfer Potential. J. Chem. Inf. Model. 52(12): 3138-3143 (2012) - 2011
- [j13]Anne Mai Wassermann, Britta Nisius, Martin Vogt, Jürgen Bajorath:
Correction to Identification of Descriptors Capturing Compound Class-Specific Features by Mutual Information Analysis. J. Chem. Inf. Model. 51(2): 508-509 (2011) - [j12]Martin Vogt, Yun Huang, Jürgen Bajorath:
From Activity Cliffs to Activity Ridges: Informative Data Structures for SAR Analysis. J. Chem. Inf. Model. 51(8): 1848-1856 (2011) - [j11]Martin Vogt, Jürgen Bajorath:
Introduction of the Conditional Correlated Bernoulli Model of Similarity Value Distributions and its Application to the Prospective Prediction of Fingerprint Search Performance. J. Chem. Inf. Model. 51(10): 2496-2506 (2011) - [j10]Ruifang Li, Dagmar Stumpfe, Martin Vogt, Hanna Geppert, Jürgen Bajorath:
Development of a Method To Consistently Quantify the Structural Distance between Scaffolds and To Assess Scaffold Hopping Potential. J. Chem. Inf. Model. 51(10): 2507-2514 (2011) - 2010
- [j9]Martin Vogt, Anne Mai Wassermann, Jürgen Bajorath:
Application of Information - Theoretic Concepts in Chemoinformatics. Inf. 1(2): 60-73 (2010) - [j8]Hanna Geppert, Martin Vogt, Jürgen Bajorath:
Current Trends in Ligand-Based Virtual Screening: Molecular Representations, Data Mining Methods, New Application Areas, and Performance Evaluation. J. Chem. Inf. Model. 50(2): 205-216 (2010) - [j7]Hany E. A. Ahmed, Martin Vogt, Jürgen Bajorath:
Design and Evaluation of Bonded Atom Pair Descriptors. J. Chem. Inf. Model. 50(4): 487-499 (2010) - [j6]Anne Mai Wassermann, Britta Nisius, Martin Vogt, Jürgen Bajorath:
Identification of Descriptors Capturing Compound Class-Specific Features by Mutual Information Analysis. J. Chem. Inf. Model. 50(11): 1935-1940 (2010) - 2009
- [j5]Britta Nisius, Martin Vogt, Jürgen Bajorath:
Development of a Fingerprint Reduction Approach for Bayesian Similarity Searching Based on Kullback-Leibler Divergence Analysis. J. Chem. Inf. Model. 49(6): 1347-1358 (2009) - [j4]Martin Vogt, Britta Nisius, Jürgen Bajorath:
Predicting the similarity search performance of fingerprints and their combination with molecular property descriptors using probabilistic and information theoretic modeling. Stat. Anal. Data Min. 2(2): 123-134 (2009) - 2008
- [j3]Martin Vogt, Jürgen Bajorath:
Bayesian Similarity Searching in High-Dimensional Descriptor Spaces Combined with Kullback-Leibler Descriptor Divergence Analysis. J. Chem. Inf. Model. 48(2): 247-255 (2008) - 2007
- [j2]Martin Vogt, Jeffrey W. Godden, Jürgen Bajorath:
Bayesian Interpretation of a Distance Function for Navigating High-Dimensional Descriptor Spaces. J. Chem. Inf. Model. 47(1): 39-46 (2007) - [j1]Martin Vogt, Jürgen Bajorath:
Introduction of an Information-Theoretic Method to Predict Recovery Rates of Active Compounds for Bayesian in Silico Screening: Theory and Screening Trials. J. Chem. Inf. Model. 47(2): 337-341 (2007)
Conference and Workshop Papers
- 2017
- [c2]Katrin Ullrich, Michael Kamp, Thomas Gärtner, Martin Vogt, Stefan Wrobel:
Co-Regularised Support Vector Regression. ECML/PKDD (2) 2017: 338-354 - 2016
- [c1]Katrin Ullrich, Michael Kamp, Thomas Gärtner, Martin Vogt, Stefan Wrobel:
Ligand-Based Virtual Screening with Co-regularised Support Vector Regression. ICDM Workshops 2016: 261-268
Parts in Books or Collections
- 2013
- [p1]Jens Auer, Martin Vogt, Jürgen Bajorath:
Emerging Chemical Patterns - Theory and Applications. Contrast Data Mining 2013: 253-268
Data and Artifacts
- 2020
- [d2]Martin Vogt, Jürgen Bajorath:
ccbmlib - a Python Package for Modeling Tanimoto Coefficient Distributions for Molecular Fingerprints. Zenodo, 2020
- [d1]Martin Vogt, Jürgen Bajorath:
ccbmlib - a Python Package for Modeling Tanimoto Coefficient Distributions for Molecular Fingerprints. Zenodo, 2020
Informal and Other Publications
- 2020
- [i3]Martin Vogt, Jürgen Bajorath:
ccbmlib - a Python package for modeling Tanimoto similarity value distributions. F1000Research 9: 100 (2020) - 2016
- [i2]Swarit Jasial, Ye Hu, Martin Vogt, Jürgen Bajorath:
Activity-relevant similarity values for fingerprints and implications for similarity searching. F1000Research 5: 591 (2016) - 2015
- [i1]Ye Hu, Bijun Zhang, Martin Vogt, Jürgen Bajorath:
AnalogExplorer2 - Stereochemistry sensitive graphical analysis of large analog series. F1000Research 4: 1031 (2015)
Coauthor Index
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