Introduction of an Information-Theoretic Method to Predict Recovery Rates of Active Compounds for Bayesian in Silico Screening: Theory and Screening Trials. - Martin Vogt 001; Jürgen Bajorath (J. Chem. Inf. Model., 2007)

Bayesian Interpretation of a Distance Function for Navigating High-Dimensional Descriptor Spaces. - Martin Vogt 001; Jeffrey W. Godden; Jürgen Bajorath (J. Chem. Inf. Model., 2007)

Bayesian Similarity Searching in High-Dimensional Descriptor Spaces Combined with Kullback-Leibler Descriptor Divergence Analysis. - Martin Vogt 001; Jürgen Bajorath (J. Chem. Inf. Model., 2008)

Development of a Fingerprint Reduction Approach for Bayesian Similarity Searching Based on Kullback-Leibler Divergence Analysis. - Britta Nisius; Martin Vogt 001; Jürgen Bajorath (J. Chem. Inf. Model., 2009)

Predicting the similarity search performance of fingerprints and their combination with molecular property descriptors using probabilistic and information theoretic modeling. - Martin Vogt 001; Britta Nisius; Jürgen Bajorath (Stat. Anal. Data Min., 2009)

Application of Information - Theoretic Concepts in Chemoinformatics. - Martin Vogt 001; Anne Mai Wassermann; Jürgen Bajorath (Inf., 2010)

Design and Evaluation of Bonded Atom Pair Descriptors. - Hany E. A. Ahmed; Martin Vogt 001; Jürgen Bajorath (J. Chem. Inf. Model., 2010)

Current Trends in Ligand-Based Virtual Screening: Molecular Representations, Data Mining Methods, New Application Areas, and Performance Evaluation. - Hanna Geppert; Martin Vogt 001; Jürgen Bajorath (J. Chem. Inf. Model., 2010)

Identification of Descriptors Capturing Compound Class-Specific Features by Mutual Information Analysis. - Anne Mai Wassermann; Britta Nisius; Martin Vogt 001; Jürgen Bajorath (J. Chem. Inf. Model., 2010)

Development of a Method To Consistently Quantify the Structural Distance between Scaffolds and To Assess Scaffold Hopping Potential. - Ruifang Li; Dagmar Stumpfe; Martin Vogt 001; Hanna Geppert; Jürgen Bajorath (J. Chem. Inf. Model., 2011)

Introduction of the Conditional Correlated Bernoulli Model of Similarity Value Distributions and its Application to the Prospective Prediction of Fingerprint Search Performance. - Martin Vogt 001; Jürgen Bajorath (J. Chem. Inf. Model., 2011)

From Activity Cliffs to Activity Ridges: Informative Data Structures for SAR Analysis. - Martin Vogt 001; Yun Huang; Jürgen Bajorath (J. Chem. Inf. Model., 2011)

Correction to Identification of Descriptors Capturing Compound Class-Specific Features by Mutual Information Analysis. - Anne Mai Wassermann; Britta Nisius; Martin Vogt 001; Jürgen Bajorath (J. Chem. Inf. Model., 2011)

MMP-Cliffs: Systematic Identification of Activity Cliffs on the Basis of Matched Molecular Pairs. - Xiaoying Hu; Ye Hu; Martin Vogt 001; Dagmar Stumpfe; Jürgen Bajorath (J. Chem. Inf. Model., 2012)

Navigating High-Dimensional Activity Landscapes: Design and Application of the Ligand-Target Differentiation Map. - Preeti Iyer; Dilyana Dimova; Martin Vogt 001; Jürgen Bajorath (J. Chem. Inf. Model., 2012)

Systematic Assessment of Compound Series with SAR Transfer Potential. - Bijun Zhang; Anne Mai Wassermann; Martin Vogt 001; Jürgen Bajorath (J. Chem. Inf. Model., 2012)

Emerging Chemical Patterns - Theory and Applications. - Jens Auer; Martin Vogt 001; Jürgen Bajorath (Contrast Data Mining, 2013)

Statistical modeling of value distributions of similarity coefficients in virtual screening and its application to predicting fingerprint search performance. - Martin Vogt 001; Jürgen Bajorath (J. Cheminformatics, 2013)

Predicting Potent Compounds via Model-Based Global Optimization. - Mohsen Ahmadi; Martin Vogt 001; Preeti Iyer; Jürgen Bajorath; Holger Fröhlich (J. Chem. Inf. Model., 2013)

Searching for Closely Related Ligands with Different Mechanisms of Action Using Machine Learning and Mapping Algorithms. - Jenny Balfer; Martin Vogt 001; Jürgen Bajorath (J. Chem. Inf. Model., 2013)

Similarity Searching for Potent Compounds Using Feature Selection. - Martin Vogt 001; Jürgen Bajorath (J. Chem. Inf. Model., 2013)

Conditional Probabilities of Activity Landscape Features for Individual Compounds. - Martin Vogt 001; Preeti Iyer; Gerald M. Maggiora; Jürgen Bajorath (J. Chem. Inf. Model., 2013)

Design of an activity landscape view taking compound-based feature probabilities into account. - Bijun Zhang; Martin Vogt 001; Jürgen Bajorath (J. Comput. Aided Mol. Des., 2014)

Large-Scale Assessment of Activity Landscape Feature Probabilities of Bioactive Compounds. - Shilva Kayastha; Dilyana Dimova; Preeti Iyer; Martin Vogt 001; Jürgen Bajorath (J. Chem. Inf. Model., 2014)

AnalogExplorer2 - Stereochemistry sensitive graphical analysis of large analog series. - Ye Hu; Bijun Zhang; Martin Vogt 001; Jürgen Bajorath (F1000Research, 2015)

Introducing the 'active search' method for iterative virtual screening. - Roman Garnett; Thomas Gärtner 001; Martin Vogt 001; Jürgen Bajorath (J. Comput. Aided Mol. Des., 2015)

Comparison of bioactive chemical space networks generated using substructure- and fingerprint-based measures of molecular similarity. - Bijun Zhang; Martin Vogt 001; Gerald M. Maggiora; Jürgen Bajorath (J. Comput. Aided Mol. Des., 2015)

Design of chemical space networks using a Tanimoto similarity variant based upon maximum common substructures. - Bijun Zhang; Martin Vogt 001; Gerald M. Maggiora; Jürgen Bajorath (J. Comput. Aided Mol. Des., 2015)

Erratum to: Design of chemical space networks using a Tanimoto similarity variant based upon maximum common substructures. - Bijun Zhang; Martin Vogt 001; Gerald M. Maggiora; Jürgen Bajorath (J. Comput. Aided Mol. Des., 2015)

Design and characterization of chemical space networks for different compound data sets. - Magdalena Zwierzyna; Martin Vogt 001; Gerald M. Maggiora; Jürgen Bajorath (J. Comput. Aided Mol. Des., 2015)

Ligand-Based Virtual Screening with Co-regularised Support Vector Regression. - Katrin Ullrich; Michael Kamp; Thomas Gärtner 001; Martin Vogt 001; Stefan Wrobel (ICDM Workshops, 2016)

Activity-relevant similarity values for fingerprints and implications for similarity searching. - Swarit Jasial; Ye Hu; Martin Vogt 001; Jürgen Bajorath (F1000Research, 2016)

Maximum common substructure-based Tversky index: an asymmetric hybrid similarity measure. - Ryo Kunimoto; Martin Vogt 001; Jürgen Bajorath (J. Comput. Aided Mol. Des., 2016)

Lessons learned from the design of chemical space networks and opportunities for new applications. - Martin Vogt 001; Dagmar Stumpfe; Gerald M. Maggiora; Jürgen Bajorath (J. Comput. Aided Mol. Des., 2016)

Design of chemical space networks on the basis of Tversky similarity. - Mengjun Wu; Martin Vogt 001; Gerald M. Maggiora; Jürgen Bajorath (J. Comput. Aided Mol. Des., 2016)

Co-Regularised Support Vector Regression. - Katrin Ullrich; Michael Kamp; Thomas Gärtner 001; Martin Vogt 001; Stefan Wrobel (ECML/PKDD (2), 2017)

Influence of Varying Training Set Composition and Size on Support Vector Machine-Based Prediction of Active Compounds. - Raquel Rodríguez-Pérez; Martin Vogt 001; Jürgen Bajorath (J. Chem. Inf. Model., 2017)

Systematic computational identification of promiscuity cliff pathways formed by inhibitors of the human kinome. - Filip Miljkovic; Martin Vogt 001; Jürgen Bajorath (J. Comput. Aided Mol. Des., 2019)

ccbmlib - a Python package for modeling Tanimoto similarity value distributions. - Martin Vogt 001; Jürgen Bajorath (F1000Research, 2020)

Activity landscape image analysis using convolutional neural networks. - Javed Iqbal; Martin Vogt 001; Jürgen Bajorath (J. Cheminformatics, 2020)

From Qualitative to Quantitative Analysis of Activity and Property Landscapes. - Gerald M. Maggiora; José L. Medina-Franco; Javed Iqbal; Martin Vogt 001; Jürgen Bajorath (J. Chem. Inf. Model., 2020)

ccbmlib - a Python Package for Modeling Tanimoto Coefficient Distributions for Molecular Fingerprints. - Martin Vogt 001; Jürgen Bajorath (Zenodo, 2020)

ccbmlib - a Python Package for Modeling Tanimoto Coefficient Distributions for Molecular Fingerprints. - Martin Vogt 001; Jürgen Bajorath (Zenodo, 2020)

Prediction of activity cliffs on the basis of images using convolutional neural networks. - Javed Iqbal; Martin Vogt 001; Jürgen Bajorath (J. Comput. Aided Mol. Des., 2021)

DeepAC - Conditional transformer-based chemical language model for the prediction of activity cliffs formed by bioactive compounds. - Hengwei Chen; Martin Vogt 001; Jürgen Bajorath (Zenodo, 2022)

Influence of Data Curation and Confidence Levels on Compound Predictions Using Machine Learning Models. - Elena Xerxa; Martin Vogt 001; Jürgen Bajorath (J. Chem. Inf. Model., 2024)