Martin Vogt - RankMe

43 papers Journal 40Unranked 2

Year	Rank	Type	Title / Venue / Authors
2024	J	`jnl`	Influence of Data Curation and Confidence Levels on Compound Predictions Using Machine Learning Models. J. Chem. Inf. Model. Elena Xerxa, Martin Vogt, Jürgen Bajorath
2021	J	`jnl`	Prediction of activity cliffs on the basis of images using convolutional neural networks. J. Comput. Aided Mol. Des. Javed Iqbal, Martin Vogt, Jürgen Bajorath
2020	J	`jnl`	Activity landscape image analysis using convolutional neural networks. J. Cheminformatics Javed Iqbal, Martin Vogt, Jürgen Bajorath
2020	J	`jnl`	From Qualitative to Quantitative Analysis of Activity and Property Landscapes. J. Chem. Inf. Model. Gerald M. Maggiora, José L. Medina-Franco, Javed Iqbal, Martin Vogt, Jürgen Bajorath
2020	J	`jnl`	ccbmlib - a Python package for modeling Tanimoto similarity value distributions. F1000Research Martin Vogt, Jürgen Bajorath
2019	J	`jnl`	Systematic computational identification of promiscuity cliff pathways formed by inhibitors of the human kinome. J. Comput. Aided Mol. Des. Filip Miljkovic, Martin Vogt, Jürgen Bajorath
2017	—	`conf`	Co-Regularised Support Vector Regression. ECML/PKDD (2) Katrin Ullrich, Michael Kamp, Thomas Gärtner, Martin Vogt, Stefan Wrobel
2017	J	`jnl`	Influence of Varying Training Set Composition and Size on Support Vector Machine-Based Prediction of Active Compounds. J. Chem. Inf. Model. Raquel Rodríguez-Pérez, Martin Vogt, Jürgen Bajorath
2016	J	`jnl`	Activity-relevant similarity values for fingerprints and implications for similarity searching. F1000Research Swarit Jasial, Ye Hu, Martin Vogt, Jürgen Bajorath
2016	J	`jnl`	Design of chemical space networks on the basis of Tversky similarity. J. Comput. Aided Mol. Des. Mengjun Wu, Martin Vogt, Gerald M. Maggiora, Jürgen Bajorath
2016	J	`jnl`	Lessons learned from the design of chemical space networks and opportunities for new applications. J. Comput. Aided Mol. Des. Martin Vogt, Dagmar Stumpfe, Gerald M. Maggiora, Jürgen Bajorath
2016	—	`conf`	Ligand-Based Virtual Screening with Co-regularised Support Vector Regression. ICDM Workshops Katrin Ullrich, Michael Kamp, Thomas Gärtner, Martin Vogt, Stefan Wrobel
2016	J	`jnl`	Maximum common substructure-based Tversky index: an asymmetric hybrid similarity measure. J. Comput. Aided Mol. Des. Ryo Kunimoto, Martin Vogt, Jürgen Bajorath
2015	J	`jnl`	AnalogExplorer2 - Stereochemistry sensitive graphical analysis of large analog series. F1000Research Ye Hu, Bijun Zhang, Martin Vogt, Jürgen Bajorath
2015	J	`jnl`	Comparison of bioactive chemical space networks generated using substructure- and fingerprint-based measures of molecular similarity. J. Comput. Aided Mol. Des. Bijun Zhang, Martin Vogt, Gerald M. Maggiora, Jürgen Bajorath
2015	J	`jnl`	Design and characterization of chemical space networks for different compound data sets. J. Comput. Aided Mol. Des. Magdalena Zwierzyna, Martin Vogt, Gerald M. Maggiora, Jürgen Bajorath
2015	J	`jnl`	Design of chemical space networks using a Tanimoto similarity variant based upon maximum common substructures. J. Comput. Aided Mol. Des. Bijun Zhang, Martin Vogt, Gerald M. Maggiora, Jürgen Bajorath
2015	J	`jnl`	Erratum to: Design of chemical space networks using a Tanimoto similarity variant based upon maximum common substructures. J. Comput. Aided Mol. Des. Bijun Zhang, Martin Vogt, Gerald M. Maggiora, Jürgen Bajorath
2015	J	`jnl`	Introducing the 'active search' method for iterative virtual screening. J. Comput. Aided Mol. Des. Roman Garnett, Thomas Gärtner, Martin Vogt, Jürgen Bajorath
2014	J	`jnl`	Design of an activity landscape view taking compound-based feature probabilities into account. J. Comput. Aided Mol. Des. Bijun Zhang, Martin Vogt, Jürgen Bajorath
2014	J	`jnl`	Large-Scale Assessment of Activity Landscape Feature Probabilities of Bioactive Compounds. J. Chem. Inf. Model. Shilva Kayastha, Dilyana Dimova, Preeti Iyer, Martin Vogt, Jürgen Bajorath
2013	J	`jnl`	Conditional Probabilities of Activity Landscape Features for Individual Compounds. J. Chem. Inf. Model. Martin Vogt, Preeti Iyer, Gerald M. Maggiora, Jürgen Bajorath
2013	—	`ch.`	Emerging Chemical Patterns - Theory and Applications. Contrast Data Mining Jens Auer, Martin Vogt, Jürgen Bajorath
2013	J	`jnl`	Predicting Potent Compounds via Model-Based Global Optimization. J. Chem. Inf. Model. Mohsen Ahmadi, Martin Vogt, Preeti Iyer, Jürgen Bajorath, Holger Fröhlich
2013	J	`jnl`	Searching for Closely Related Ligands with Different Mechanisms of Action Using Machine Learning and Mapping Algorithms. J. Chem. Inf. Model. Jenny Balfer, Martin Vogt, Jürgen Bajorath
2013	J	`jnl`	Similarity Searching for Potent Compounds Using Feature Selection. J. Chem. Inf. Model. Martin Vogt, Jürgen Bajorath
2013	J	`jnl`	Statistical modeling of value distributions of similarity coefficients in virtual screening and its application to predicting fingerprint search performance. J. Cheminformatics Martin Vogt, Jürgen Bajorath
2012	J	`jnl`	MMP-Cliffs: Systematic Identification of Activity Cliffs on the Basis of Matched Molecular Pairs. J. Chem. Inf. Model. Xiaoying Hu, Ye Hu, Martin Vogt, Dagmar Stumpfe, Jürgen Bajorath
2012	J	`jnl`	Navigating High-Dimensional Activity Landscapes: Design and Application of the Ligand-Target Differentiation Map. J. Chem. Inf. Model. Preeti Iyer, Dilyana Dimova, Martin Vogt, Jürgen Bajorath
2012	J	`jnl`	Systematic Assessment of Compound Series with SAR Transfer Potential. J. Chem. Inf. Model. Bijun Zhang, Anne Mai Wassermann, Martin Vogt, Jürgen Bajorath
2011	J	`jnl`	Correction to Identification of Descriptors Capturing Compound Class-Specific Features by Mutual Information Analysis. J. Chem. Inf. Model. Anne Mai Wassermann, Britta Nisius, Martin Vogt, Jürgen Bajorath
2011	J	`jnl`	Development of a Method To Consistently Quantify the Structural Distance between Scaffolds and To Assess Scaffold Hopping Potential. J. Chem. Inf. Model. Ruifang Li, Dagmar Stumpfe, Martin Vogt, Hanna Geppert, Jürgen Bajorath
2011	J	`jnl`	From Activity Cliffs to Activity Ridges: Informative Data Structures for SAR Analysis. J. Chem. Inf. Model. Martin Vogt, Yun Huang, Jürgen Bajorath
2011	J	`jnl`	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. Martin Vogt, Jürgen Bajorath
2010	J	`jnl`	Application of Information - Theoretic Concepts in Chemoinformatics. Inf. Martin Vogt, Anne Mai Wassermann, Jürgen Bajorath
2010	J	`jnl`	Current Trends in Ligand-Based Virtual Screening: Molecular Representations, Data Mining Methods, New Application Areas, and Performance Evaluation. J. Chem. Inf. Model. Hanna Geppert, Martin Vogt, Jürgen Bajorath
2010	J	`jnl`	Design and Evaluation of Bonded Atom Pair Descriptors. J. Chem. Inf. Model. Hany E. A. Ahmed, Martin Vogt, Jürgen Bajorath
2010	J	`jnl`	Identification of Descriptors Capturing Compound Class-Specific Features by Mutual Information Analysis. J. Chem. Inf. Model. Anne Mai Wassermann, Britta Nisius, Martin Vogt, Jürgen Bajorath
2009	J	`jnl`	Development of a Fingerprint Reduction Approach for Bayesian Similarity Searching Based on Kullback-Leibler Divergence Analysis. J. Chem. Inf. Model. Britta Nisius, Martin Vogt, Jürgen Bajorath
2009	J	`jnl`	Predicting the similarity search performance of fingerprints and their combination with molecular property descriptors using probabilistic and information theoretic modeling. Stat. Anal. Data Min. Martin Vogt, Britta Nisius, Jürgen Bajorath
2008	J	`jnl`	Bayesian Similarity Searching in High-Dimensional Descriptor Spaces Combined with Kullback-Leibler Descriptor Divergence Analysis. J. Chem. Inf. Model. Martin Vogt, Jürgen Bajorath
2007	J	`jnl`	Bayesian Interpretation of a Distance Function for Navigating High-Dimensional Descriptor Spaces. J. Chem. Inf. Model. Martin Vogt, Jeffrey W. Godden, Jürgen Bajorath
2007	J	`jnl`	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. Martin Vogt, Jürgen Bajorath