Jürgen Bajorath

发表

SARANEA: A Freely Available Program To Mine Structure-Activity and Structure-Selectivity Relationship Information in Compound Data Sets

Anne Mai Wassermann, Eugen Lounkine, Mathias Wawer, 2010, J. Chem. Inf. Model..

Rendering Conventional Molecular Fingerprints for Virtual Screening Independent of Molecular Complexity and Size Effects

Jürgen Bajorath, Britta Nisius, J. Bajorath, 2010, ChemMedChem.

SAR Monitoring of Evolving Compound Data Sets Using Activity Landscapes

Jürgen Bajorath, Preeti Iyer, Ye Hu, 2011, J. Chem. Inf. Model..

Virtual Screening Methods Based on Bayesian Statistics

Jürgen Bajorath, Martin Vogt, J. Bajorath, 2011 .

Design of an activity landscape view taking compound-based feature probabilities into account

Jürgen Bajorath, Martin Vogt, Bijun Zhang, 2014, Journal of Computer-Aided Molecular Design.

Pushing the boundaries of computational approaches: special focus issue on computational chemistry and computer-aided drug discovery.

Jürgen Bajorath, 2015, Future medicinal chemistry.

Selected Concepts and Investigations in Compound Classification, Molecular Descriptor Analysis, and Virtual Screening

Jürgen Bajorath, J. Bajorath, 2001, J. Chem. Inf. Comput. Sci..

Advances in Activity Cliff Research

Jürgen Bajorath, Dilyana Dimova, J. Bajorath, 2016, Molecular informatics.

Assessment of complex SARs using Formal Concept Analysis of fragment combinations

Eugen Lounkine, Jürgen Bajorath, Jens Auer, 2009 .

Data analytics and deep learning in medicinal chemistry.

Jürgen Bajorath, J. Bajorath, 2018, Future medicinal chemistry.

Simplified activity cliff network representations with high interpretability and immediate access to SAR information

Jürgen Bajorath, Huabin Hu, J. Bajorath, 2020, Journal of Computer-Aided Molecular Design.

Systematic mining of analog series with related core structures in multi-target activity space

Jürgen Bajorath, Ye Hu, Disha Gupta-Ostermann, 2013, Journal of Computer-Aided Molecular Design.

DeepCOMO: from structure-activity relationship diagnostics to generative molecular design using the compound optimization monitor methodology

Jürgen Bajorath, Dimitar Yonchev, J. Bajorath, 2020, Journal of Computer-Aided Molecular Design.

Compound Classification Using the scikit‐learn Library

Jürgen Bajorath, Martin Vogt, Jenny Balfer, 2017 .

Systematic Identification of Matching Molecular Series and Mapping of Screening Hits

Jürgen Bajorath, Ye Hu, Antonio de la Vega de León, 2014, Molecular informatics.

Bio- and chemo-informatics beyond data management: crucial challenges and future opportunities.

Jürgen Bajorath, Florence L Stahura, J. Bajorath, 2002, Drug discovery today.

Combining structural and bioactivity-based fingerprints improves prediction performance and scaffold hopping capability

Jürgen Bajorath, Ola Engkvist, Hongming Chen, 2019, Journal of Cheminformatics.

Exploring sets of molecules from patents and relationships to other active compounds in chemical space networks

Jürgen Bajorath, Ryo Kunimoto, J. Bajorath, 2017, Journal of Computer-Aided Molecular Design.

Support-Vector-Machine-Based Ranking Significantly Improves the Effectiveness of Similarity Searching Using 2D Fingerprints and Multiple Reference Compounds

Thomas Gärtner, Hanna Geppert, Stefan Wrobel, 2008, J. Chem. Inf. Model..

Compound Structure‐Independent Activity Prediction in High‐Dimensional Target Space

Jürgen Bajorath, Ye Hu, Jenny Balfer, 2014, Molecular informatics.

Computational chemical biology on the rise.

Jürgen Bajorath, J B Brown, J. Bajorath, 2019, Future medicinal chemistry.

Recursive Median Partitioning for Virtual Screening of Large Databases

Jürgen Bajorath, Jeffrey W. Godden, John R. Furr, 2003, J. Chem. Inf. Comput. Sci..

SAR Matrices: Automated Extraction of Information-Rich SAR Tables from Large Compound Data Sets

Anne Mai Wassermann, Jürgen Bajorath, Peter Haebel, 2012, J. Chem. Inf. Model..

Virtual Screening Methods

Jürgen Bajorath, J. Bajorath, 2013 .

Analysis of Chemical Information Content Using Shannon Entropy

Jürgen Bajorath, Jeffrey W. Godden, J. Bajorath, 2007 .

Prediction of Activity Cliffs Using Support Vector Machines

Kathrin Heikamp, Jürgen Bajorath, Xiaoying Hu, 2012, J. Chem. Inf. Model..

Combinatorial Preferences Affect Molecular Similarity/Diversity Calculations Using Binary Fingerprints and Tanimoto Coefficients

Jürgen Bajorath, Ling Xue, Jeffrey W. Godden, 2000, J. Chem. Inf. Comput. Sci..

Kinase inhibitor data set for systematic analysis of representative kinases across the human kinome

Jürgen Bajorath, Stefan Laufer, Oliver Laufkötter, 2020, Data in brief.

Large-Scale Comparison of Alternative Similarity Search Strategies with Varying Chemical Information Contents

Jürgen Bajorath, Tomoyuki Miyao, Oliver Laufkötter, 2019, ACS omega.

Method for Systematic Analogue Search Using the Mega SAR Matrix Database

Jürgen Bajorath, Atsushi Yoshimori, Yuichi Horita, 2019, J. Chem. Inf. Model..

An Information-Theoretic Approach to Descriptor Selection for Database Profiling and QSAR Modeling

Jürgen Bajorath, Jeffrey W. Godden, 2003 .

Design of Array-Type Compound Libraries that Combine Information from Natural Products and Synthetic Molecules

Jürgen Bajorath, Jeffrey W. Godden, Florence L. Stahura, 2000 .

Extraction of Discontinuous Structure-Activity Relationships from Compound Data Sets through Particle Swarm Optimization

Jürgen Bajorath, Preeti Iyer, Vigneshwaran Namasivayam, 2011, J. Chem. Inf. Model..

Lessons learned from the design of chemical space networks and opportunities for new applications

Jürgen Bajorath, Martin Vogt, Dagmar Stumpfe, 2016, Journal of Computer-Aided Molecular Design.

Jürgen Bajorath, Martin Vogt, J. Bajorath, 2008, J. Chem. Inf. Model..

Recent developments in SAR visualization

Jürgen Bajorath, Dagmar Stumpfe, 2016 .

Predicting bioactive conformations and binding modes of macrocycles

Jürgen Bajorath, Andrew Anighoro, Antonio de la Vega de León, 2016, Journal of Computer-Aided Molecular Design.

Current Trends, Overlooked Issues, and Unmet Challenges in Virtual Screening

Jürgen Bajorath, Dagmar Stumpfe, J. Bajorath, 2020, J. Chem. Inf. Model..

Compound activity records associated with original publications in ChEMBL 21

Jürgen Bajorath, Ye Hu, J. Bajorath, 2016 .

Chemoinformatics for Drug Discovery: Bajorath/Chemoinformatics for Drug Discovery

Jürgen Bajorath, 2013 .

Visualization and Interpretation of Support Vector Machine Activity Predictions

Jürgen Bajorath, Jenny Balfer, J. Bajorath, 2015, J. Chem. Inf. Model..

Expanding the chemical information science gateway

Jürgen Bajorath, 2017, F1000Research.

Rationalizing Promiscuity Cliffs

Jürgen Bajorath, Dilyana Dimova, J. Bajorath, 2018, ChemMedChem.

Advances in 2D fingerprint similarity searching

Hanna Geppert, Jürgen Bajorath, J. Bajorath, 2010, Expert opinion on drug discovery.

Duality of activity cliffs in drug discovery

Jürgen Bajorath, J. Bajorath, 2019, Expert opinion on drug discovery.

Advanced fingerprint methods for similarity searching: balancing molecular complexity effects.

Jürgen Bajorath, Yuan Wang, 2010, Combinatorial chemistry & high throughput screening.

Assessing the Growth of Bioactive Compounds and Scaffolds over Time: Implications for Lead Discovery and Scaffold Hopping

Jürgen Bajorath, Ye Hu, Swarit Jasial, 2016, J. Chem. Inf. Model..

Computational Method for Quantitative Comparison of Activity Landscapes on the Basis of Image Data

Jürgen Bajorath, Javed Iqbal, Martin Vogt, 2020, Molecules.

Chemoinformatics and Computational Chemical Biology

Jürgen Bajorath, J. Bajorath, 2011, Methods in Molecular Biology.

Quantifying the Fingerprint Descriptor Dependence of Structure-Activity Relationship Information on a Large Scale

Jürgen Bajorath, Dilyana Dimova, Dagmar Stumpfe, 2013, J. Chem. Inf. Model..

Evolving Concept of Activity Cliffs

Jürgen Bajorath, Huabin Hu, Dagmar Stumpfe, 2019, ACS omega.

Chemical Database Mining through Entropy-Based Molecular Similarity Assessment of Randomly Generated Structural Fragment Populations

Jürgen Bajorath, José Batista, J. Bajorath, 2007, J. Chem. Inf. Model..

High-throughput screening

Jürgen Bajorath, Yang Wang, J. Bajorath, 2001, Nature.

Multiobjective Particle Swarm Optimization: Automated Identification of Structure-Activity Relationship-Informative Compounds with Favorable Physicochemical Property Distributions

Jürgen Bajorath, Vigneshwaran Namasivayam, J. Bajorath, 2012, J. Chem. Inf. Model..

Application of a New Scaffold Concept for Computational Target Deconvolution of Chemical Cancer Cell Line Screens

Jürgen Bajorath, Dilyana Dimova, Ryo Kunimoto, 2017, ACS omega.

Ligand-Target Interaction-Based Weighting of Substructures for Virtual Screening

Jürgen Bajorath, Thomas J. Crisman, Mihiret Tekeste Sisay, 2008, J. Chem. Inf. Model..

Multitask Machine Learning for Classifying Highly and Weakly Potent Kinase Inhibitors

Jürgen Bajorath, Raquel Rodríguez-Pérez, J. Bajorath, 2019, ACS Omega.

Development of a Compound Class-Directed Similarity Coefficient That Accounts for Molecular Complexity Effects in Fingerprint Searching

Jürgen Bajorath, Yuan Wang, Y. Wang, 2009, J. Chem. Inf. Model..

Ligand Prediction from Protein Sequence and Small Molecule Information Using Support Vector Machines and Fingerprint Descriptors

Thomas Gärtner, Hanna Geppert, Jürgen Bajorath, 2009, J. Chem. Inf. Model..

Mini-fingerprints Detect Similar Activity of Receptor Ligands Previously Recognized Only by Three-Dimensional Pharmacophore-Based Methods

Jürgen Bajorath, Ling Xue, Jeffrey W. Godden, 2001, J. Chem. Inf. Comput. Sci..

Data sets for orthologous target pair analysis

Jürgen Bajorath, Dilyana Dimova, Dagmar Stumpfe, 2015 .

Combining Cluster Analysis, Feature Selection and Multiple Support Vector Machine Models for the Identification of Human Ether‐a‐go‐go Related Gene Channel Blocking Compounds

Jürgen Bajorath, Britta Nisius, Andreas H Göller, 2009, Chemical biology & drug design.

QSAR without borders.

Artem Cherkasov, Tudor I. Oprea, Jürgen Bajorath, 2020, Chemical Society reviews.

Editorial Sp E cial Focu S: computational ch E mi S try

Jürgen Bajorath, J. Bajorath, 2014 .

Statistical modeling of value distributions of similarity coefficients in virtual screening and its application to predicting fingerprint search performance

Jürgen Bajorath, Martin Vogt, 2013, Journal of Cheminformatics.

Chemoinformatics : Perspectives and challenges

Jürgen Bajorath, Ling Xue, Florence L. Stahura, 2005 .

Integrating computational lead optimization diagnostics with analog design and candidate selection

Jürgen Bajorath, Dimitar Yonchev, J. Bajorath, 2020, Future science OA.

Mining of Randomly Generated Molecular Fragment Populations Uncovers Activity-Specific Fragment Hierarchies

Jürgen Bajorath, José Batista, J. Bajorath, 2007, J. Chem. Inf. Model..

Modeling of Compound Profiling Experiments Using Support Vector Machines

Jürgen Bajorath, Jenny Balfer, Stefan Laufer, 2014, Chemical biology & drug design.

Activity landscape image analysis using convolutional neural networks

Jürgen Bajorath, Javed Iqbal, Martin Vogt, 2020, Journal of Cheminformatics.

Extension of three-dimensional activity cliff information through systematic mapping of active analogs

Jürgen Bajorath, Ye Hu, Norbert Furtmann, 2015 .

Compound Data Mining for Drug Discovery.

Jürgen Bajorath, J. Bajorath, 2017, Methods in molecular biology.

Searching for Closely Related Ligands with Different Mechanisms of Action Using Machine Learning and Mapping Algorithms

Jürgen Bajorath, Martin Vogt, Jenny Balfer, 2013, J. Chem. Inf. Model..

Systematic Data Analysis and Diagnostic Machine Learning Reveal Differences between Compounds with Single- and Multitarget Activity.

Jürgen Bajorath, Christian Feldmann, Dimitar Yonchev, 2020, Molecular pharmaceutics.

Computer-Aided Drug Design - Session Introduction

Terry P. Lybrand, Jürgen Bajorath, Teri E. Klein, 1999, Pacific Symposium on Biocomputing.

Design of a tripartite network for the prediction of drug targets

Jürgen Bajorath, Ryo Kunimoto, J. Bajorath, 2018, Journal of Computer-Aided Molecular Design.

Relevance of Feature Combinations for Similarity Searching Using General or Activity Class-Directed Molecular Fingerprints

Eugen Lounkine, Jürgen Bajorath, José Batista, 2009, J. Chem. Inf. Model..

Virtual screening methods that complement HTS.

Jürgen Bajorath, Florence L Stahura, J. Bajorath, 2004, Combinatorial chemistry & high throughput screening.

Data structures for computational compound promiscuity analysis and exemplary applications to inhibitors of the human kinome

Jürgen Bajorath, Filip Miljković, J. Bajorath, 2019, Journal of Computer-Aided Molecular Design.

Systematic Assessment of Compound Series with SAR Transfer Potential

Anne Mai Wassermann, Jürgen Bajorath, Martin Vogt, 2012, J. Chem. Inf. Model..

Fingerprint Scaling Increases the Probability of Identifying Molecules with Similar Activity in Virtual Screening Calculations

Jürgen Bajorath, Ling Xue, Jeffrey W. Godden, 2001, J. Chem. Inf. Comput. Sci..

Is scaffold hopping a reliable indicator for the ability of computational methods to identify structurally diverse active compounds?

Jürgen Bajorath, Dilyana Dimova, 2017, Journal of Computer-Aided Molecular Design.

A Perspective on Computational Chemogenomics

Jürgen Bajorath, J. Bajorath, 2013, Molecular informatics.

Exploration of 3D Activity Cliffs on the Basis of Compound Binding Modes and Comparison of 2D and 3D Cliffs

Jürgen Bajorath, Ye Hu, J. Bajorath, 2012, J. Chem. Inf. Model..

How Do 2D Fingerprints Detect Structurally Diverse Active Compounds? Revealing Compound Subset-Specific Fingerprint Features through Systematic Selection

Kathrin Heikamp, Jürgen Bajorath, J. Bajorath, 2011, J. Chem. Inf. Model..

Anatomy of Fingerprint Search Calculations on Structurally Diverse Sets of Active Compounds

Jürgen Bajorath, Jeffrey W. Godden, Florence L. Stahura, 2005, J. Chem. Inf. Model..

Jürgen Bajorath, José Batista, J. Bajorath, 2008, ChemMedChem.

Activity cliff clusters as a source of structure–activity relationship information

Jürgen Bajorath, Ye Hu, Dilyana Dimova, 2015, Expert opinion on drug discovery.

Computational Polypharmacology Analysis of the Heat Shock Protein 90 Interactome

Kathrin Heikamp, Jürgen Bajorath, Giulio Rastelli, 2015, J. Chem. Inf. Model..

Partitioning methods for the identification of active molecules.

Jürgen Bajorath, Florence L Stahura, 2003, Current medicinal chemistry.

Introduction of the Conditional Correlated Bernoulli Model of Similarity Value Distributions and its Application to the Prospective Prediction of Fingerprint Search Performance

Jürgen Bajorath, Martin Vogt, J. Bajorath, 2011, J. Chem. Inf. Model..

Mathias Wawer, Jürgen Bajorath, J. Bajorath, 2010, J. Chem. Inf. Model..

Hierarchical Clustering in R

Jürgen Bajorath, Martin Vogt, 2017 .

Introducing the ‘active search’ method for iterative virtual screening

Roman Garnett, Thomas Gärtner, Jürgen Bajorath, 2015, Journal of Computer-Aided Molecular Design.

Emerging Chemical Patterns: A New Methodology for Molecular Classification and Compound Selection

Jürgen Bajorath, Jens Auer, 2006, J. Chem. Inf. Model..

Jürgen Bajorath, Martin Vogt, 2013, J. Chem. Inf. Model..

RelACCS‐FP: A Structural Minimalist Approach to Fingerprint Design

Eugen Lounkine, Jürgen Bajorath, José Batista, 2008, Chemical biology & drug design.

Frontiers in Molecular Design and Chemical Information Science - Herman Skolnik Award Symposium 2015: Jürgen Bajorath

Jürgen Bajorath, Veerabahu Shanmugasundaram, Rachelle J. Bienstock, 2016 .

Integrating Structure‐ and Ligand‐Based Virtual Screening: Comparison of Individual, Parallel, and Fused Molecular Docking and Similarity Search Calculations on Multiple Targets

Hanna Geppert, Jürgen Bajorath, Lu Tan, 2008, ChemMedChem.

Ligand Prediction for Orphan Targets Using Support Vector Machines and Various Target-Ligand Kernels Is Dominated by Nearest Neighbor Effects

Anne Mai Wassermann, Hanna Geppert, Jürgen Bajorath, 2009, J. Chem. Inf. Model..

Redundancy in two major compound databases.

Jürgen Bajorath, Martin Vogt, Dilyana Dimova, 2018, Drug discovery today.

CRITICAL ASSESSMENT OF VIRTUAL SCREENING FOR HIT IDENTIFICATION

Jürgen Bajorath, Dagmar Stumpfe, J. Bajorath, 2013 .

Methods for Ligand-Based Virtual Screening

Jürgen Bajorath, J. Bajorath, 2009 .

Design of a Three-Dimensional Multitarget Activity Landscape

Jürgen Bajorath, Antonio de la Vega de León, J. Bajorath, 2012, J. Chem. Inf. Model..

Evidence for the presence of core structure-dependent activity cliffs.

Jürgen Bajorath, Huabin Hu, J. Bajorath, 2020, Future medicinal chemistry.

Assessing the information content of structural and protein–ligand interaction representations for the classification of kinase inhibitor binding modes via machine learning and active learning

Jürgen Bajorath, Raquel Rodríguez-Pérez, Filip Miljković, 2020, Journal of Cheminformatics.

Data Sets of Matching Molecular Series

Jürgen Bajorath, Ye Hu, Antonio de la Vega de León, 2014 .

Visualization of Activity Landscapes and Chemogenomics Data

Jürgen Bajorath, Ye Hu, Dagmar Stumpfe, 2013, Molecular informatics.

Interpretation of machine learning models using shapley values: application to compound potency and multi-target activity predictions

Jürgen Bajorath, Raquel Rodríguez-Pérez, J. Bajorath, 2020, Journal of Computer-Aided Molecular Design.

Computational characterization of SAR microenvironments in high-throughput screening data

Mathias Wawer, Jürgen Bajorath, Su Sun, 2010 .

Jürgen Bajorath, Jens Auer, J. Bajorath, 2008, Methods in molecular biology.

Differential Shannon Entropy Analysis Identifies Molecular Property Descriptors that Predict Aqueous Solubility of Synthetic Compounds with High Accuracy in Binary QSAR Calculations

Jürgen Bajorath, Jeffrey W. Godden, Florence L. Stahura, 2002, J. Chem. Inf. Comput. Sci..

Prediction of Individual Compounds Forming Activity Cliffs Using Emerging Chemical Patterns

Jürgen Bajorath, Preeti Iyer, Vigneshwaran Namasivayam, 2013, J. Chem. Inf. Model..

Profile Scaling Increases the Similarity Search Performance of Molecular Fingerprints Containing Numerical Descriptors and Structural Keys

Jürgen Bajorath, Ling Xue, Jeffrey W. Godden, 2003, J. Chem. Inf. Comput. Sci..

A Hybrid Virtual Screening Protocol Based on Binding Mode Similarity.

Jürgen Bajorath, Andrew Anighoro, 2018, Methods in molecular biology.

Binary Quantitative Structure-Activity Relationship (QSAR) Analysis of Estrogen Receptor Ligands

Paul Labute, Jürgen Bajorath, Christopher I. Williams, 1999, J. Chem. Inf. Comput. Sci..

Design of Multitarget Activity Landscapes That Capture Hierarchical Activity Cliff Distributions

Anne Mai Wassermann, Mathias Wawer, Jürgen Bajorath, 2011, J. Chem. Inf. Model..

Assessing the Confidence Level of Public Domain Compound Activity Data and the Impact of Alternative Potency Measurements on SAR Analysis

Jürgen Bajorath, Dagmar Stumpfe, J. Bajorath, 2011, J. Chem. Inf. Model..

Systematic Extraction of Structure–Activity Relationship Information from Biological Screening Data

Mathias Wawer, Jürgen Bajorath, J. Bajorath, 2009, ChemMedChem.

Neighborhood-Based Prediction of Novel Active Compounds from SAR Matrices

Jürgen Bajorath, Disha Gupta-Ostermann, Veerabahu Shanmugasundaram, 2014, J. Chem. Inf. Model..

Three-Dimensional Similarity in Molecular Docking: Prioritizing Ligand Poses on the Basis of Experimental Binding Modes

Jürgen Bajorath, Andrew Anighoro, J. Bajorath, 2016, J. Chem. Inf. Model..

Large-scale SAR analysis.

Jürgen Bajorath, J. Bajorath, 2013, Drug discovery today. Technologies.

Jürgen Bajorath, Ryo Kunimoto, 2018, ACS omega.

F1000Prime recommendation of Ultra-large library docking for discovering new chemotypes.

Jürgen Bajorath, 2019 .

Combining Horizontal and Vertical Substructure Relationships in Scaffold Hierarchies for Activity Prediction

Jürgen Bajorath, Ye Hu, J. Bajorath, 2011, J. Chem. Inf. Model..

Bayesian Screening for Active Compounds in High‐dimensional Chemical Spaces Combining Property Descriptors and Molecular Fingerprints

Jürgen Bajorath, Martin Vogt, J. Bajorath, 2007, Chemical biology & drug design.

Data Mining Approaches for Compound Selection and Iterative Screening

Jürgen Bajorath, Martin Vogt, J. Bajorath, 2009 .

Modeling Tanimoto Similarity Value Distributions and Predicting Search Results

Jürgen Bajorath, Martin Vogt, J. Bajorath, 2017, Molecular informatics.

Mapping of Activity-Specific Fragment Pathways Isolated from Random Fragment Populations Reveals the Formation of Coherent Molecular Cores

Eugen Lounkine, Jürgen Bajorath, José Batista, 2007, J. Chem. Inf. Model..

From bird’s eye views to molecular communities: two-layered visualization of structure–activity relationships in large compound data sets

Jürgen Bajorath, Alexandre Varnek, Dragos Horvath, 2017, Journal of Computer-Aided Molecular Design.

Application of support vector machine-based ranking strategies to search for target-selective compounds.

Anne Mai Wassermann, Hanna Geppert, Jürgen Bajorath, 2011, Methods in molecular biology.

Design and Evaluation of Bonded Atom Pair Descriptors

Jürgen Bajorath, Martin Vogt, Hany E. A. Ahmed, 2010, J. Chem. Inf. Model..

Design and Exploration of Target-Selective Chemical Space Representations

Jürgen Bajorath, Ingo Vogt, 2008, J. Chem. Inf. Model..

Prediction of Compound Potency Changes in Matched Molecular Pairs Using Support Vector Regression

Jürgen Bajorath, Antonio de la Vega de León, J. Bajorath, 2014, J. Chem. Inf. Model..

Erratum to: Design of chemical space networks using a Tanimoto similarity variant based upon maximum common substructures

Jürgen Bajorath, Martin Vogt, Bijun Zhang, 2015, Journal of Computer-Aided Molecular Design.

Classification of Compounds with Distinct or Overlapping Multi-Target Activities and Diverse Molecular Mechanisms Using Emerging Chemical Patterns

Jürgen Bajorath, Ye Hu, Jenny Balfer, 2013, J. Chem. Inf. Model..

Jürgen Bajorath, Ling Xue, Florence L. Stahura, 2004, J. Chem. Inf. Model..

Shannon Entropy-Based Fingerprint Similarity Search Strategy

Hanna Geppert, Jürgen Bajorath, Yuan Wang, 2009, J. Chem. Inf. Model..

Computationally derived compound profiling matrices

Jürgen Bajorath, Martin Vogt, Swarit Jasial, 2018, Future science OA.

Virtual Screening with Generative Topographic Maps: How Many Maps Are Required?

Jürgen Bajorath, Dragos Horvath, Gilles Marcou, 2018, J. Chem. Inf. Model..

Exploring Alternative Strategies for the Identification of Potent Compounds Using Support Vector Machine and Regression Modeling

Jürgen Bajorath, Kimito Funatsu, Tomoyuki Miyao, 2018, J. Chem. Inf. Model..

Virtual compound screening in drug discovery.

Jürgen Bajorath, Dagmar Stumpfe, Peter Ripphausen, 2012, Future medicinal chemistry.

Jürgen Bajorath, J. Bajorath, 2017, Methods in molecular biology.

Systematic Identification and Classification of Three-Dimensional Activity Cliffs

Jürgen Bajorath, Ye Hu, Norbert Furtmann, 2012, J. Chem. Inf. Model..

Integrating the Structure–Activity Relationship Matrix Method with Molecular Grid Maps and Activity Landscape Models for Medicinal Chemistry Applications

Jürgen Bajorath, Atsushi Yoshimori, Toru Tanoue, 2019, ACS Omega.

Modeling of activity landscapes for drug discovery

Jürgen Bajorath, J. Bajorath, 2012, Expert opinion on drug discovery.

Rationalizing the Role of SAR Tolerance for Ligand-Based Virtual Screening

Mathias Wawer, Jürgen Bajorath, Britta Nisius, 2011, J. Chem. Inf. Model..

Analysis of Biological Screening Compounds with Single- or Multi-Target Activity via Diagnostic Machine Learning

Jürgen Bajorath, Christian Feldmann, Dimitar Yonchev, 2020, Biomolecules.

Development of a Method To Consistently Quantify the Structural Distance between Scaffolds and To Assess Scaffold Hopping Potential

Hanna Geppert, Jürgen Bajorath, Martin Vogt, 2011, J. Chem. Inf. Model..

Systematic computational identification of promiscuity cliff pathways formed by inhibitors of the human kinome

Jürgen Bajorath, Martin Vogt, Filip Miljković, 2019, Journal of Computer-Aided Molecular Design.

Application of Information - Theoretic Concepts in Chemoinformatics

Anne Mai Wassermann, Jürgen Bajorath, Martin Vogt, 2010, Inf..

A computational method to facilitate structure-activity relationship transfer

Anne Mai Wassermann, Jürgen Bajorath, 2012, Journal of Cheminformatics.

Complexity and Heterogeneity of Data for Chemical Information Science

Jürgen Bajorath, J. Bajorath, 2016 .

AnalogExplorer and AnalogExplorer2

Jürgen Bajorath, Ye Hu, Martin Vogt, 2015 .

Activity Profile Sequences: a Concept to Account for the Progression of Compound Activity in Target Space and to Extract SAR Information from Analogue Series with Multiple Target Annotations

Jürgen Bajorath, Ye Hu, J. Bajorath, 2011, ChemMedChem.

Assessment of Molecular Similarity from the Analysis of Randomly Generated Structural Fragment Populations

Jürgen Bajorath, José Batista, Jeffrey W. Godden, 2006, J. Chem. Inf. Model..

Conditional Probabilities of Activity Landscape Features for Individual Compounds

Jürgen Bajorath, Preeti Iyer, Martin Vogt, 2013, J. Chem. Inf. Model..

Systematic design of analogs of active compounds covering more than 1000 targets

Jürgen Bajorath, Dilyana Dimova, J. Bajorath, 2016 .

Faculty Opinions recommendation of Virtual Chemical Libraries.

Jürgen Bajorath, J. Bajorath, 2018, Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature.

Influence of Search Parameters and Criteria on Compound Selection, Promiscuity, and Pan Assay Interference Characteristics

Jürgen Bajorath, Ye Hu, J. Bajorath, 2014, J. Chem. Inf. Model..

Composition and Topology of Activity Cliff Clusters Formed by Bioactive Compounds

Jürgen Bajorath, Dilyana Dimova, Dagmar Stumpfe, 2014, J. Chem. Inf. Model..

Systematic Identification of Scaffolds Representing Compounds Active against Individual Targets and Single or Multiple Target Families

Jürgen Bajorath, Ye Hu, J. Bajorath, 2013, J. Chem. Inf. Model..

SAR Matrix Method for Large-Scale Analysis of Compound Structure-Activity Relationships and Exploration of Multitarget Activity Spaces.

Jürgen Bajorath, Ye Hu, J. Bajorath, 2018, Methods in molecular biology.

Design and Evaluation of a Novel Class-Directed 2D Fingerprint to Search for Structurally Diverse Active Compounds

Jürgen Bajorath, Hanna Eckert, J. Bajorath, 2006, J. Chem. Inf. Model..

Maximum common substructure-based Tversky index: an asymmetric hybrid similarity measure

Jürgen Bajorath, Martin Vogt, Ryo Kunimoto, 2016, Journal of Computer-Aided Molecular Design.

Integration of virtual and high-throughput screening

Jürgen Bajorath, J. Bajorath, 2002, Nature Reviews Drug Discovery.

From Activity Cliffs to Activity Ridges: Informative Data Structures for SAR Analysis

Jürgen Bajorath, Martin Vogt, Yun Huang, 2011, J. Chem. Inf. Model..

Memory-assisted reinforcement learning for diverse molecular de novo design

Thomas Blaschke, Jürgen Bajorath, Ola Engkvist, 2020, Journal of Cheminformatics.

Privileged Structural Motif Detection and Analysis Using Generative Topographic Maps

Jürgen Bajorath, Alexandre Varnek, Dragos Horvath, 2017, J. Chem. Inf. Model..

Current Trends in Ligand-Based Virtual Screening: Molecular Representations, Data Mining Methods, New Application Areas, and Performance Evaluation

Hanna Geppert, Jürgen Bajorath, Martin Vogt, 2010, J. Chem. Inf. Model..

Introduction of Target Cliffs as a Concept To Identify and Describe Complex Molecular Selectivity Patterns

Jürgen Bajorath, Ye Hu, 2013, J. Chem. Inf. Model..

Ask the experts: focus on computational chemistry.

John Mitchell, Andreas Bender, Jürgen Bajorath, 2011, Future medicinal chemistry.

Navigating High-Dimensional Activity Landscapes: Design and Application of the Ligand-Target Differentiation Map

Jürgen Bajorath, Preeti Iyer, Martin Vogt, 2012, J. Chem. Inf. Model..

Compound Optimization through Data Set-Dependent Chemical Transformations

Jürgen Bajorath, Antonio de la Vega de León, 2013, J. Chem. Inf. Model..

Introduction of a Generally Applicable Method to Estimate Retrieval of Active Molecules for Similarity Searching using Fingerprints

Jürgen Bajorath, Martin Vogt, J. Bajorath, 2007, ChemMedChem.

Systematic assessment of scaffold distances in ChEMBL: prioritization of compound data sets for scaffold hopping analysis in virtual screening

Jürgen Bajorath, Ruifang Li, J. Bajorath, 2012, Journal of Computer-Aided Molecular Design.

Mapping Algorithms for Molecular Similarity Analysis and Ligand-Based Virtual Screening: Design of DynaMAD and Comparison with MAD and DMC

Jürgen Bajorath, Ingo Vogt, Hanna Eckert, 2006, J. Chem. Inf. Model..

Chemical space networks: a powerful new paradigm for the description of chemical space

Jürgen Bajorath, Gerald M. Maggiora, G. Maggiora, 2014, Journal of Computer-Aided Molecular Design.

Exploring Peptide-likeness of Active Molecules Using 2D Fingerprint Methods

Jürgen Bajorath, Hanna Eckert, 2007, J. Chem. Inf. Model..

Information entropic functions for molecular descriptor profiling.

Anne Mai Wassermann, Jürgen Bajorath, Britta Nisius, 2012, Methods in molecular biology.

Bit Silencing in Fingerprints Enables the Derivation of Compound Class-Directed Similarity Metrics

Jürgen Bajorath, Yuan Wang, Y. Wang, 2008, J. Chem. Inf. Model..

Molecular Descriptors for Effective Classification of Biologically Active Compounds Based on Principal Component Analysis Identified by a Genetic Algorithm

Jürgen Bajorath, Ling Xue, J. Bajorath, 2000, J. Chem. Inf. Comput. Sci..

Potency-scaled partitioning in descriptor spaces with increasing dimensionality.

Jürgen Bajorath, 2005, Current topics in medicinal chemistry.

Introduction of a Methodology for Visualization and Graphical Interpretation of Bayesian Classification Models

Jürgen Bajorath, Jenny Balfer, J. Bajorath, 2014, J. Chem. Inf. Model..

Chemical Descriptors with Distinct Levels of Information Content and Varying Sensitivity to Differences between Selected Compound Databases Identified by SE-DSE Analysis

Jürgen Bajorath, Jeffrey W. Godden, J. Bajorath, 2002, J. Chem. Inf. Comput. Sci..

Advances in exploring activity cliffs

Jürgen Bajorath, Huabin Hu, Dagmar Stumpfe, 2020, Journal of Computer-Aided Molecular Design.

Design of multi-target activity landscapes that capture hierarchical activity cliff distributions

Jürgen Bajorath, Dilyana Dimova, 2012, Journal of Cheminformatics.

Representation of Multi‐Target Activity Landscapes Through Target Pair‐Based Compound Encoding in Self‐Organizing Maps

Jürgen Bajorath, Preeti Iyer, 2011, Chemical biology & drug design.

Design of chemical space networks using a Tanimoto similarity variant based upon maximum common substructures

Jürgen Bajorath, Martin Vogt, Bijun Zhang, 2015, Journal of Computer-Aided Molecular Design.

Molecular Scaffolds with High Propensity to Form Multi-Target Activity Cliffs

Jürgen Bajorath, Ye Hu, J. Bajorath, 2010, J. Chem. Inf. Model..

Adaptation of formal concept analysis for the systematic exploration of structure-activity and structure-selectivity relationships

Eugen Lounkine, Jürgen Bajorath, 2010, J. Cheminformatics.

Activity-relevant similarity values for fingerprints and implications for similarity searching

Jürgen Bajorath, Ye Hu, Martin Vogt, 2016, F1000Research.

Prediction of Compounds in Different Local Structure-Activity Relationship Environments Using Emerging Chemical Patterns

Kathrin Heikamp, Jürgen Bajorath, Disha Gupta-Ostermann, 2014, J. Chem. Inf. Model..

Identification of Descriptors Capturing Compound Class-Specific Features by Mutual Information Analysis

Anne Mai Wassermann, Jürgen Bajorath, Britta Nisius, 2010, J. Chem. Inf. Model..

Database Searching for Compounds with Similar Biological Activity Using Short Binary Bit String Representations of Molecules

Jürgen Bajorath, Ling Xue, Jeffrey W. Godden, 1999, J. Chem. Inf. Comput. Sci..

Distinguishing between Bioactive and Modeled Compound Conformations through Mining of Emerging Chemical Patterns

Jürgen Bajorath, Jens Auer, 2008, J. Chem. Inf. Model..

Evaluation of different virtual screening strategies on the basis of compound sets with characteristic core distributions and dissimilarity relationships

Jürgen Bajorath, Kimito Funatsu, Tomoyuki Miyao, 2019, Journal of Computer-Aided Molecular Design.

Correction to Identification of Descriptors Capturing Compound Class-Specific Features by Mutual Information Analysis

Anne Mai Wassermann, Jürgen Bajorath, Britta Nisius, 2011, Journal of Chemical Information and Modeling.

Cover Picture: Introduction of a Generally Applicable Method to Estimate Retrieval of Active Molecules for Similarity Searching using Fingerprints (ChemMedChem 9/2007)

Jürgen Bajorath, Martin Vogt, 2007 .

Random Reduction in Fingerprint Bit Density Improves Compound Recall in Search Calculations Using Complex Reference Molecules

Hanna Geppert, Jürgen Bajorath, Yuan Wang, 2008, Chemical biology & drug design.

Programs for chemoinformatics and computational medicinal chemistry

Jürgen Bajorath, Ye Hu, J. Bajorath, 2014 .

Comparison of Confirmed Inactive and Randomly Selected Compounds as Negative Training Examples in Support Vector Machine-Based Virtual Screening

Kathrin Heikamp, Jürgen Bajorath, J. Bajorath, 2013, J. Chem. Inf. Model..

Statistical Methods for Predicting Compound Recovery Rates for Ligand‐Based Virtual Screening and Assessing the Probability of Activity

Jürgen Bajorath, Martin Vogt, J. Bajorath, 2012 .

Searching for Coordinated Activity Cliffs Using Particle Swarm Optimization

Jürgen Bajorath, Vigneshwaran Namasivayam, J. Bajorath, 2012, J. Chem. Inf. Model..

Frequency of Occurrence and Potency Range Distribution of Activity Cliffs in Bioactive Compounds

Jürgen Bajorath, Dagmar Stumpfe, J. Bajorath, 2012, J. Chem. Inf. Model..

Emerging Chemical Patterns - Theory and Applications

Jürgen Bajorath, Martin Vogt, Jens Auer, 2013, Contrast Data Mining.

Evaluation of Descriptors and Mini-Fingerprints for the Identification of Molecules with Similar Activity

Jürgen Bajorath, Ling Xue, Jeffrey W. Godden, 2000, J. Chem. Inf. Comput. Sci..

Repositioning the Chemical Information Science Gateway

Jürgen Bajorath, J. Bajorath, 2019, F1000Research.

Median Partitioning: A Novel Method for the Selection of Representative Subsets from Large Compound Pools

Jürgen Bajorath, Ling Xue, Jeffrey W. Godden, 2002, J. Chem. Inf. Comput. Sci..

Prediction of the three-dimensional structure of the human Fas receptor by comparative molecular modeling

Jürgen Bajorath, Alejandro Aruffo, J. Bajorath, 1997, J. Comput. Aided Mol. Des..

Classification of Biologically Active Compounds by Median Partitioning

Jürgen Bajorath, Ling Xue, Jeffrey W. Godden, 2002, J. Chem. Inf. Comput. Sci..

Visualization of multi-property landscapes for compound selection and optimization

Jürgen Bajorath, Thomas Schultz, Dilyana Dimova, 2015, Journal of Computer-Aided Molecular Design.

Exploration of Target Synergy in Cancer Treatment by Cell-Based Screening Assay and Network Propagation Analysis

Jürgen Bajorath, José L Medina-Franco, J Jesús Naveja, 2019, J. Chem. Inf. Model..

REPROVIS-DB: A Benchmark System for Ligand-Based Virtual Screening Derived from Reproducible Prospective Applications

Anne Mai Wassermann, Jürgen Bajorath, Peter Ripphausen, 2011, J. Chem. Inf. Model..

Statistical analysis of computational docking of large compound data bases to distinct protein binding sites

Jürgen Bajorath, Jeffrey W. Godden, Florence L. Stahura, 1999, J. Comput. Chem..

A Data Mining Method To Facilitate SAR Transfer

Anne Mai Wassermann, Jürgen Bajorath, 2011, J. Chem. Inf. Model..

COMPUTER-AIDED DRUG DISCOVERY: FROM TARGET PROTEINS TO DRUG CANDIDATES

Jürgen Bajorath, Teri E. Klein, Jiri Novotny, 1998 .

Compound optimization through data set-dependent chemical transformations

Jürgen Bajorath, Antonio de la Vega de León, J. Bajorath, 2013, Journal of Cheminformatics.

Predicting the performance of fingerprint similarity searching.

Jürgen Bajorath, Martin Vogt, 2011, Methods in molecular biology.

Compound Pathway Model To Capture SAR Progression: Comparison of Activity Cliff-Dependent and -Independent Pathways

Kathrin Heikamp, Jürgen Bajorath, Dilyana Dimova, 2013, J. Chem. Inf. Model..

Activity cliffs in PubChem confirmatory bioassays taking inactive compounds into account

Jürgen Bajorath, Ye Hu, Gerald M. Maggiora, 2013, Journal of Computer-Aided Molecular Design.

Identification of a Preferred Set of Molecular Descriptors for Compound Classification Based on Principal Component Analysis

Jürgen Bajorath, Ling Xue, Hua Gao, 1999, J. Chem. Inf. Comput. Sci..

Analysis of a High-Throughput Screening Data Set Using Potency-Scaled Molecular Similarity Algorithms

Jürgen Bajorath, Ingo Vogt, 2007, J. Chem. Inf. Model..

Collection Of Analog Series-Based (Asb) Scaffolds

Jürgen Bajorath, Dilyana Dimova, 2017 .

Molecular Formal Concept Analysis for Compound Selectivity Profiling in Biologically Annotated Databases

Eugen Lounkine, Jürgen Bajorath, Dagmar Stumpfe, 2009, J. Chem. Inf. Model..

Evaluation of molecular model-based discovery of ecto-5’-nucleotidase inhibitors on the basis of X-ray structures

Jürgen Bajorath, Norbert Furtmann, 2014, Journal of Cheminformatics.

Design of chemical space networks on the basis of Tversky similarity

Jürgen Bajorath, Martin Vogt, Gerald M. Maggiora, 2015, Journal of Computer-Aided Molecular Design.

SAR Monitoring of Evolving Compound Data Sets Using Activity Landscapes

Jürgen Bajorath, Preeti Iyer, Ye Hu, 2011, Journal of Chemical Information and Modeling.

Extracting Compound Profiling Matrices from Screening Data

Jürgen Bajorath, Martin Vogt, Swarit Jasial, 2018, ACS omega.

Representation and identification of activity cliffs

Jürgen Bajorath, J. Bajorath, 2017, Expert opinion on drug discovery.

Variability of Molecular Descriptors in Compound Databases Revealed by Shannon Entropy Calculations

Jürgen Bajorath, Jeffrey W. Godden, Florence L. Stahura, 2000, J. Chem. Inf. Comput. Sci..

Navigating structure-activity landscapes.

Mathias Wawer, Jürgen Bajorath, Rajarshi Guha, 2009, Drug discovery today.

From Qualitative to Quantitative Analysis of Activity and Property Landscapes.

Jürgen Bajorath, Gerald Maggiora, José L Medina-Franco, 2020, Journal of chemical information and modeling.

Analyzing Promiscuity at the Level of Active Compounds and Targets

Jürgen Bajorath, J. Bajorath, 2016, Molecular informatics.

Design and characterization of chemical space networks for different compound data sets

Jürgen Bajorath, Martin Vogt, Gerald M. Maggiora, 2015, Journal of Computer-Aided Molecular Design.

Quantification and visualization of structure-activity-relationships at multiple levels of detail

Mathias Wawer, Jürgen Bajorath, Lisa Peltason, 2009 .

Knowledge base of two- and three-dimensional activity cliffs

Jürgen Bajorath, Ye Hu, Dagmar Stumpfe, 2015 .

Series of screening compounds with high hit rates for the exploration of multi-target activities and assay interference

Jürgen Bajorath, Dagmar Stumpfe, Erik Gilberg, 2018, Future science OA.

Analysis of Fas–ligand interactions using a molecular model of the receptor–ligand interface

Jürgen Bajorath, J. Bajorath, 1999, J. Comput. Aided Mol. Des..

Eugen Lounkine, Jürgen Bajorath, Lu Tan, 2008, J. Chem. Inf. Model..

Core Trees and Consensus Fragment Sequences for Molecular Representation and Similarity Analysis

Eugen Lounkine, Jürgen Bajorath, J. Bajorath, 2008, J. Chem. Inf. Model..

Prediction of Different Classes of Promiscuous and Nonpromiscuous Compounds Using Machine Learning and Nearest Neighbor Analysis

Thomas Blaschke, Jürgen Bajorath, Filip Miljković, 2019, ACS Omega.

Improving the Search Performance of Extended Connectivity Fingerprints through Activity‐Oriented Feature Filtering and Application of a Bit‐Density‐Dependent Similarity Function

Eugen Lounkine, Jürgen Bajorath, Ye Hu, 2009, ChemMedChem.

Methods for SAR visualization

Jürgen Bajorath, Dagmar Stumpfe, J. Bajorath, 2012 .

Towards Unified Compound Screening Strategies: A Critical Evaluation of Error Sources in Experimental and Virtual High‐Throughput Screening

Jürgen Bajorath, Christian N. Parker, J. Bajorath, 2006 .

Jürgen Bajorath, Ling Xue, Jeffrey W. Godden, 2004, J. Chem. Inf. Model..

Computational Assessment of Chemical Saturation of Analogue Series under Varying Conditions

Jürgen Bajorath, Martin Vogt, Tomoyuki Miyao, 2018, ACS omega.

Activity-relevant similarity values for fingerprints and implications for similarity searching

Jürgen Bajorath, Ye Hu, Martin Vogt, 2016, F1000Research.

Design of Diverse and Focused Compound Libraries

Eugen Lounkine, Jürgen Bajorath, Martin Vogt, 2017 .

Predicting Potent Compounds via Model-Based Global Optimization

Holger Fröhlich, Mohsen Ahmadi, Jürgen Bajorath, 2013, J. Chem. Inf. Model..

Extending the Activity Cliff Concept: Structural Categorization of Activity Cliffs and Systematic Identification of Different Types of Cliffs in the ChEMBL Database

Jürgen Bajorath, Ye Hu, J. Bajorath, 2012, J. Chem. Inf. Model..

Optimization of the MAD algorithm for virtual screening.

Jürgen Bajorath, Hanna Eckert, 2008, Methods in molecular biology.

Identification of Compounds That Interfere with High‐Throughput Screening Assay Technologies

Jürgen Bajorath, Ola Engkvist, Hongming Chen, 2019, Chemmedchem.

Atom-Centered Interacting Fragments and Similarity Search Applications

Jürgen Bajorath, José Batista, Lu Tan, 2010, J. Chem. Inf. Model..

Exploring Activity Cliffs in Chemoinformatics and Medicinal Chemistry

Jürgen Bajorath, J. Bajorath, 2014 .

Identification of Multitarget Activity Ridges in High-Dimensional Bioactivity Spaces

Jürgen Bajorath, Disha Gupta-Ostermann, 2012, J. Chem. Inf. Model..

Jürgen Bajorath, Hanna Eckert, J. Bajorath, 2007, Drug discovery today.

Targeting the Hsp90 interactome using in silico polypharmacology approaches

Jürgen Bajorath, Giulio Rastelli, Dagmar Stumpfe, 2013 .

New methodologies for ligand-based virtual screening.

Jürgen Bajorath, Florence L Stahura, J. Bajorath, 2005, Current pharmaceutical design.

Computational analysis of activity and selectivity cliffs.

Jürgen Bajorath, Lisa Peltason, 2011, Methods in molecular biology.

Drug discovery and development in the era of Big Data.

Jürgen Bajorath, Jeremy L Jenkins, John Overington, 2016, Future medicinal chemistry.

Distinguishing between Natural Products and Synthetic Molecules by Descriptor Shannon Entropy Analysis and Binary QSAR Calculations

Jürgen Bajorath, Ling Xue, Jeffrey W. Godden, 2000, J. Chem. Inf. Comput. Sci..

Prediction of Compounds with Closely Related Activity Profiles Using Weighted Support Vector Machine Linear Combinations

Kathrin Heikamp, Jürgen Bajorath, J. Bajorath, 2013, J. Chem. Inf. Model..

Jürgen Bajorath, Andrew Anighoro, J. Bajorath, 2016, Journal of Computer-Aided Molecular Design.

Comparison of bioactive chemical space networks generated using substructure- and fingerprint-based measures of molecular similarity

Jürgen Bajorath, Martin Vogt, Bijun Zhang, 2015, Journal of Computer-Aided Molecular Design.

Differential Shannon Entropy as a Sensitive Measure of Differences in Database Variability of Molecular Descriptors

Jürgen Bajorath, Jeffrey W. Godden, J. Bajorath, 2001, J. Chem. Inf. Comput. Sci..

Hit Expansion from Screening Data Based upon Conditional Probabilities of Activity Derived from SAR Matrices

Jürgen Bajorath, Disha Gupta-Ostermann, Jenny Balfer, 2015, Molecular informatics.

Identification and validation of therapeutic target proteins

Jürgen Bajorath, 2002 .

Exploring Compound Promiscuity Patterns and Multi-Target Activity Spaces

Jürgen Bajorath, Ye Hu, Disha Gupta-Ostermann, 2014, Computational and structural biotechnology journal.

Three-Dimensional Activity Landscape Models of Different Design and Their Application to Compound Mapping and Potency Prediction

Jürgen Bajorath, Kimito Funatsu, Tomoyuki Miyao, 2019, J. Chem. Inf. Model..

Polypharmacology Directed Compound Data Mining: Identification of Promiscuous Chemotypes with Different Activity Profiles and Comparison to Approved Drugs

Jürgen Bajorath, Ye Hu, J. Bajorath, 2010, J. Chem. Inf. Model..

Exploring ensembles of bioactive or virtual analogs of X-ray ligands for shape similarity searching

Jürgen Bajorath, Tomoyuki Miyao, J. Bajorath, 2018, Journal of Computer-Aided Molecular Design.

A general approach for retrosynthetic molecular core analysis

J. Jesús Naveja, José L. Medina-Franco, Jürgen Bajorath, 2019, Journal of Cheminformatics.

Biological Activity Profiles of Multitarget Ligands from X-ray Structures

Jürgen Bajorath, Christian Feldmann, J. Bajorath, 2020, Molecules.

SAR Transfer across Different Targets

Jürgen Bajorath, Ye Hu, Bijun Zhang, 2013, J. Chem. Inf. Model..

MMP-Cliffs: Systematic Identification of Activity Cliffs on the Basis of Matched Molecular Pairs

Jürgen Bajorath, Ye Hu, Martin Vogt, 2012, J. Chem. Inf. Model..

Data sets for chemoinformatics and computational medicinal chemistry

Jürgen Bajorath, Ye Hu, J. Bajorath, 2014 .

In Silico Screening

Hanna Geppert, Jürgen Bajorath, Dagmar Stumpfe, 2010 .

Molecular Mechanism-Based Network-like Similarity Graphs Reveal Relationships between Different Types of Receptor Ligands and Structural Changes that Determine Agonistic, Inverse-Agonistic, and Antagonistic Effects

Jürgen Bajorath, Preeti Iyer, Dagmar Stumpfe, 2011, J. Chem. Inf. Model..

A Distance Function for Retrieval of Active Molecules from Complex Chemical Space Representations

Jürgen Bajorath, Jeffrey W. Godden, 2006, J. Chem. Inf. Model..

Distribution of randomly generated activity class characteristic substructures in diverse active and database compounds

Jürgen Bajorath, José Batista, 2008, Molecular Diversity.

Potency-directed similarity searching using support vector machines

Kathrin Heikamp, Jürgen Bajorath, Anne M Wassermann, 2011, Journal of Cheminformatics.

Functional Informatics in Drug Discovery. Edited by Sergey Ilyin.

Jürgen Bajorath, 2008 .

Chemoinformatics methods for systematic comparison of molecules from natural and synthetic sources and design of hybrid libraries

Jürgen Bajorath, 2004, Molecular Diversity.

Iterative Shannon Entropy – a Methodology to Quantify the Information Content of Value Range Dependent Data Distributions. Application to Descriptor and Compound Selectivity Profiling

Anne Mai Wassermann, Jürgen Bajorath, Martin Vogt, 2010, Molecular informatics.

Chemoinformatics : concepts, methods, and tools for drug discovery

Jürgen Bajorath, J. Bajorath, 2004 .

Bayesian Interpretation of a Distance Function for Navigating High-Dimensional Descriptor Spaces

Jürgen Bajorath, Martin Vogt, Jeffrey W. Godden, 2007, J. Chem. Inf. Model..

Visualization and Graphical Interpretation of Bayesian Compound Classification Models

Jürgen Bajorath, Jenny Balfer, J. Bajorath, 2014 .

Predicting the similarity search performance of fingerprints and their combination with molecular property descriptors using probabilistic and information theoretic modeling

Jürgen Bajorath, Britta Nisius, Martin Vogt, 2009, Stat. Anal. Data Min..

Topological Fragment Index for the Analysis of Molecular Substructures and Their Topological Environment in Active Compounds

Eugen Lounkine, Jürgen Bajorath, J. Bajorath, 2009, J. Chem. Inf. Model..

Prediction of Activity Cliffs Using Condensed Graphs of Reaction Representations, Descriptor Recombination, Support Vector Machine Classification, and Support Vector Regression

Jürgen Bajorath, Gilles Marcou, Alexandre Varnek, 2016, J. Chem. Inf. Model..

Chemoinformatics methods for systematic comparison of molecules from natural and synthetic sources and design of hybrid libraries

Jürgen Bajorath, 2002, J. Comput. Aided Mol. Des..

Accurate Partitioning of Compounds Belonging to Diverse Activity Classes

Jürgen Bajorath, Ling Xue, 2002, J. Chem. Inf. Comput. Sci..

Rationalization of the Performance and Target Dependence of Similarity Searching Incorporating Protein-Ligand Interaction Information

Jürgen Bajorath, José Batista, Lu Tan, 2010, J. Chem. Inf. Model..

Predicting the similarity search performance of fingerprints and their combination with molecular property descriptors using probabilistic and information theoretic modeling

Jürgen Bajorath, Britta Nisius, Martin Vogt, 2009 .

Application of Generative Autoencoder in De Novo Molecular Design

Thomas Blaschke, Jürgen Bajorath, Ola Engkvist, 2017, Molecular informatics.

State-of-the-art of artificial intelligence in medicinal chemistry

Jürgen Bajorath, J. Bajorath, 2021, Future science OA.

Prediction of activity cliffs on the basis of images using convolutional neural networks

Jürgen Bajorath, Martin Vogt, Javed Iqbal, 2021, Journal of Computer-Aided Molecular Design.

Informatics for Chemistry, Biology, and Biomedical Sciences.

José L. Medina-Franco, Jürgen Bajorath, Edgar López-López, 2020, Journal of chemical information and modeling.

Evaluation of multi-target deep neural network models for compound potency prediction under increasingly challenging test conditions

Jürgen Bajorath, Raquel Rodríguez-Pérez, J. Bajorath, 2021, Journal of Computer-Aided Molecular Design.

Reproducibility, reusability, and community efforts in artificial intelligence research

Jürgen Bajorath, W. Patrick Walters, Connor W. Coley, 2021 .

Introducing artificial intelligence in the life sciences

Jürgen Bajorath, Carolina Horta Andrade, Mingyue Zheng, 2021 .

Structured data sets of compounds with multi-target and corresponding single-target activity from biological assays

Jürgen Bajorath, Dimitar Yonchev, Christian Feldmann, 2021, Future science OA.

Adapting the DeepSARM approach for dual-target ligand design

Jürgen Bajorath, Huabin Hu, Atsushi Yoshimori, 2021, Journal of Computer-Aided Molecular Design.

Compound dataset and custom code for deep generative multi-target compound design

Jürgen Bajorath, Thomas Blaschke, J. Bajorath, 2021, Future science OA.

X-ray-Structure-Based Identification of Compounds with Activity against Targets from Different Families and Generation of Templates for Multitarget Ligand Design

Jürgen Bajorath, Dagmar Stumpfe, Erik Gilberg, 2018, ACS omega.

Exploring structure–selectivity relationships of biogenic amine GPCR antagonists using similarity searching and dynamic compound mapping

Jürgen Bajorath, Jens Auer, Ingo Vogt, 2008, Molecular Diversity.

Machine Learning Distinguishes with High Accuracy between Pan-Assay Interference Compounds That Are Promiscuous or Represent Dark Chemical Matter.

Thomas Blaschke, Jürgen Bajorath, Swarit Jasial, 2018, Journal of medicinal chemistry.

Assessing Scaffold Diversity of Kinase Inhibitors Using Alternative Scaffold Concepts and Estimating the Scaffold Hopping Potential for Different Kinases

Jürgen Bajorath, Dilyana Dimova, J. Bajorath, 2017, Molecules.

Random molecular fragment methods in computational medicinal chemistry.

Eugen Lounkine, Jürgen Bajorath, José Batista, 2008, Current medicinal chemistry.

Design of chemical space networks incorporating compound distance relationships

Jürgen Bajorath, J. Bajorath, Antonio de la Vega de León, 2016, F1000Research.

Systematic Extraction of Analogue Series from Large Compound Collections Using a New Computational Compound–Core Relationship Method

J. Jesús Naveja, José L. Medina-Franco, Jürgen Bajorath, 2019, ACS omega.

Determination and mapping of activity-specific descriptor value ranges for the identification of active compounds.

Jürgen Bajorath, Hanna Eckert, J. Bajorath, 2006, Journal of medicinal chemistry.

Exploring Peptide‐Likeness of Active Molecules Using 2D Fingerprint Methods.

Jürgen Bajorath, Hanna Eckert, 2007 .

Fingerprint Scaling Increases the Probability of Identifying Molecules with Similar Activity in Virtual Screening Calculations.

Jürgen Bajorath, Ling Xue, Jeffrey W. Godden, 2010 .

High-resolution view of compound promiscuity.

Jürgen Bajorath, Ye Hu, 2013, F1000Research.

Structure-Promiscuity Relationship Puzzles—Extensively Assayed Analogs with Large Differences in Target Annotations

Jürgen Bajorath, Ye Hu, Swarit Jasial, 2017, The AAPS Journal.

Cell-based partitioning.

Jürgen Bajorath, Ling Xue, Florence L Stahura, 2004, Methods in molecular biology.

Entering new publication territory in chemoinformatics and chemical information science

Jürgen Bajorath, J. Bajorath, 2015, F1000Research.

Data structures for compound promiscuity analysis: promiscuity cliffs, pathways and promiscuity hubs formed by inhibitors of the human kinome

Jürgen Bajorath, Filip Miljković, J. Bajorath, 2019, Future science OA.

Extraction of SAR information from activity cliff clusters via matching molecular series.

Jürgen Bajorath, Dilyana Dimova, J. Bajorath, 2014, European journal of medicinal chemistry.

Structural and Potency Relationships between Scaffolds of Compounds Active against Human Targets

Jürgen Bajorath, Ye Hu, 2010, ChemMedChem.

Electron redistribution on binding of a substrate to an enzyme: Folate and dihydrofolate reductase

Jürgen Bajorath, Jan Andzelm, George Fitzgerald, 1991, Proteins.

Extending accessible chemical space for the identification of novel leads

Jürgen Bajorath, J. Bajorath, 2016, Expert opinion on drug discovery.

Evaluating the High-Throughput Screening Computations

Jürgen Bajorath, Irwin D Kuntz, I. Kuntz, 2005, Journal of biomolecular screening.

Expanding the chemical information science gateway

Jürgen Bajorath, J. Bajorath, 2017, F1000Research.

New frontiers in kinases: second generation inhibitors.

Jürgen Bajorath, Stefan Laufer, J. Bajorath, 2014, Journal of medicinal chemistry.

Determining the Degree of Promiscuity of Extensively Assayed Compounds

Jürgen Bajorath, Ye Hu, Swarit Jasial, 2016, PloS one.

Methods for compound selection focused on hits and application in drug discovery.

Jürgen Bajorath, Ling Xue, Florence L Stahura, 2002, Journal of molecular graphics & modelling.

A homogeneous fluorescence resonance energy transfer system for monitoring the activation of a protein switch in real time.

Jürgen Bajorath, Michael Famulok, Dagmar Stumpfe, 2011, Journal of the American Chemical Society.

Computational design of new molecular scaffolds for medicinal chemistry, part II: generalization of analog series-based scaffolds

Jürgen Bajorath, Dilyana Dimova, Dagmar Stumpfe, 2017, Future science OA.

How Frequently Are Pan-Assay Interference Compounds Active? Large-Scale Analysis of Screening Data Reveals Diverse Activity Profiles, Low Global Hit Frequency, and Many Consistently Inactive Compounds.

Jürgen Bajorath, Ye Hu, Swarit Jasial, 2017, Journal of medicinal chemistry.

Exploring SAR Continuity in the Vicinity of Activity Cliffs

Jürgen Bajorath, Preeti Iyer, Vigneshwaran Namasivayam, 2012, Chemical biology & drug design.

Many structurally related drugs bind different targets whereas distinct drugs display significant target overlap

Jürgen Bajorath, Ye Hu, J. Bajorath, 2012 .

Do medicinal chemists learn from activity cliffs? A systematic evaluation of cliff progression in evolving compound data sets.

Kathrin Heikamp, Jürgen Bajorath, Dilyana Dimova, 2013, Journal of medicinal chemistry.

Similarity searching

Jürgen Bajorath, Dagmar Stumpfe, J. Bajorath, 2011 .

Rationalizing the Formation of Activity Cliffs in Different Compound Data Sets

Jürgen Bajorath, Huabin Hu, Dagmar Stumpfe, 2018, ACS omega.

Balancing the Influence of Molecular Complexity on Fingerprint Similarity Searching.

Jürgen Bajorath, Yuan Wang, J. Bajorath, 2008 .

Molecular Modeling and Functional Mapping of B7-H1 and B7-DC Uncouple Costimulatory Function from PD-1 Interaction

Jürgen Bajorath, Haidong Dong, Tasuku Honjo, 2003, The Journal of experimental medicine.

Targeting multifunctional proteins by virtual screening: structurally diverse cytohesin inhibitors with differentiated biological functions.

Hanna Geppert, Jürgen Bajorath, Michael Famulok, 2010, ACS chemical biology.

Promiscuity profiles of bioactive compounds: potency range and difference distributions and the relation to target numbers and families

Jürgen Bajorath, Ye Hu, J. Bajorath, 2013 .

Compound Ranking Based on Fuzzy Three-Dimensional Similarity Improves the Performance of Docking into Homology Models of G-Protein-Coupled Receptors

Jürgen Bajorath, Andrew Anighoro, J. Bajorath, 2017, ACS omega.

Towards a systematic assessment of assay interference: Identification of extensively tested compounds with high assay promiscuity

Jürgen Bajorath, Dagmar Stumpfe, Erik Gilberg, 2017, F1000Research.

Collection of analog series-based scaffolds from public compound sources

Jürgen Bajorath, Dilyana Dimova, 2018, Future science OA.

Virtual Screening Leads to the Discovery of an Effective Antagonist of Lymphocyte Function‐Associated Antigen‐1

Jürgen Bajorath, Florence L Stahura, Miyuki Shoda, 2007, ChemMedChem.

Machine Learning Models for Accurate Prediction of Kinase Inhibitors with Different Binding Modes.

Jürgen Bajorath, Raquel Rodríguez-Pérez, Filip Miljković, 2020, Journal of medicinal chemistry.

Directed R-group combination graph: a methodology to uncover structure-activity relationship patterns in a series of analogues.

Anne Mai Wassermann, Jürgen Bajorath, J. Bajorath, 2012, Journal of medicinal chemistry.

Monitoring global growth of activity cliff information over time and assessing activity cliff frequencies and distributions.

Jürgen Bajorath, Dagmar Stumpfe, J. Bajorath, 2015, Future medicinal chemistry.

Inhibitor bias in luciferase-based luminescence assays

Jürgen Bajorath, Dimitar Yonchev, J. Bajorath, 2020, Future science OA.

Identifying Promiscuous Compounds with Activity against Different Target Classes

Jürgen Bajorath, Dimitar Yonchev, Filip Miljković, 2019, Molecules.

Analog series-based scaffolds: computational design and exploration of a new type of molecular scaffolds for medicinal chemistry

Jürgen Bajorath, Ye Hu, Dilyana Dimova, 2016, Future science OA.

Quantifying the Tendency of Therapeutic Target Proteins to Bind Promiscuous or Selective Compounds

Jürgen Bajorath, Ye Hu, J. Bajorath, 2015, PloS one.

Improving data mining strategies for drug design.

Jürgen Bajorath, J. Bajorath, 2014, Future medicinal chemistry.

Mining of Randomly Generated Molecular Fragment Populations Uncovers Activity‐Specific Fragment Hierarchies.

Jürgen Bajorath, José Batista, J. Bajorath, 2007 .

Reconciling Selectivity Trends from a Comprehensive Kinase Inhibitor Profiling Campaign with Known Activity Data

Jürgen Bajorath, Filip Miljković, J. Bajorath, 2018, ACS omega.

Three‐Dimensional Protein–Ligand Interaction Scaling of Two‐Dimensional Fingerprints

Jürgen Bajorath, Martin Vogt, J. Bajorath, 2009, Chemical biology & drug design.

Measure, mine, model, and manipulate: the future for HTS and chemoinformatics?

Christopher P Austin, Jürgen Bajorath, Christian N Parker, 2006, Drug discovery today.

From activity cliffs to promiscuity cliffs

Jürgen Bajorath, J. Bajorath, 2017 .

Progress in computational medicinal chemistry.

Jürgen Bajorath, J. Bajorath, 2012, Journal of medicinal chemistry.

Recent progress in understanding activity cliffs and their utility in medicinal chemistry.

Jürgen Bajorath, Ye Hu, Dilyana Dimova, 2014, Journal of medicinal chemistry.

Identification of Orthologous Target Pairs with Shared Active Compounds and Comparison of Organism‐specific Activity Patterns

Jürgen Bajorath, Dilyana Dimova, Dagmar Stumpfe, 2015, Chemical biology & drug design.

Highly Promiscuous Small Molecules from Biological Screening Assays Include Many Pan-Assay Interference Compounds but Also Candidates for Polypharmacology.

Jürgen Bajorath, Dilyana Dimova, Dagmar Stumpfe, 2016, Journal of medicinal chemistry.

Molecular test systems for computational selectivity studies and systematic analysis of compound selectivity profiles.

Eugen Lounkine, Jürgen Bajorath, Dagmar Stumpfe, 2011, Methods in molecular biology.

Large-scale exploration of bioisosteric replacements on the basis of matched molecular pairs.

Anne Mai Wassermann, Jürgen Bajorath, J. Bajorath, 2011, Future medicinal chemistry.

Data-Driven Exploration of Selectivity and Off-Target Activities of Designated Chemical Probes

Jürgen Bajorath, Filip Miljković, J. Bajorath, 2018, Molecules.

AnalogExplorer: a new method for graphical analysis of analog series and associated structure-activity relationship information.

Jürgen Bajorath, Ye Hu, Bijun Zhang, 2014, Journal of medicinal chemistry.

Exploration of structure-activity relationship determinants in analogue series.

Jürgen Bajorath, Andreas Teckentrup, Nils Weskamp, 2009, Journal of medicinal chemistry.

Assessing the target differentiation potential of imidazole-based protein kinase inhibitors.

Jürgen Bajorath, Stefan Laufer, Preeti Iyer, 2012, Journal of medicinal chemistry.

Identification of Interaction Hot Spots in Structures of Drug Targets on the Basis of Three‐Dimensional Activity Cliff Information

Jürgen Bajorath, Ye Hu, Norbert Furtmann, 2015, Chemical biology & drug design.

Chemoinformatics: recent advances at the interfaces between computer and chemical information sciences, chemistry, and drug discovery.

Jürgen Bajorath, J. Bajorath, 2012, Bioorganic & medicinal chemistry.

Jürgen Bajorath, Ling Xue, Florence L. Stahura, 2005 .

Classification of matching molecular series on the basis of SAR phenotypes and structural relationships

Jürgen Bajorath, Dilyana Dimova, Adhideb Ghosh, 2016 .

Chemoinformatics for Drug Discovery

Jürgen Bajorath, J. Bajorath, 2013 .

Activity Cliff Networks for Medicinal Chemistry

Jürgen Bajorath, Dagmar Stumpfe, J. Bajorath, 2014, Drug development research.

Comprehensive analysis of three-dimensional activity cliffs formed by kinase inhibitors with different binding modes and cliff mapping of structural analogues.

Jürgen Bajorath, Ye Hu, Norbert Furtmann, 2015, Journal of medicinal chemistry.

Statistical analysis of computational docking of large compound data bases to distinct protein binding sites

Jürgen Bajorath, Jeffrey W. Godden, Florence L. Stahura, 1999, J. Comput. Chem..

Exploring activity cliffs in medicinal chemistry.

Jürgen Bajorath, Dagmar Stumpfe, J. Bajorath, 2012, Journal of medicinal chemistry.

Mapping of pharmacological space

Jürgen Bajorath, Britta Nisius, J. Bajorath, 2011, Expert opinion on drug discovery.

Promiscuous Ligands from Experimentally Determined Structures, Binding Conformations, and Protein Family-Dependent Interaction Hotspots

Jürgen Bajorath, Michael Gütschow, Erik Gilberg, 2019, ACS omega.

Support vector machines for drug discovery

Kathrin Heikamp, Jürgen Bajorath, J. Bajorath, 2014, Expert opinion on drug discovery.

Computational Exploration of Molecular Scaffolds in Medicinal Chemistry.

Jürgen Bajorath, Ye Hu, Dagmar Stumpfe, 2016, Journal of medicinal chemistry.

Identification and analysis of the currently available high-confidence three-dimensional activity cliffs

Jürgen Bajorath, Ye Hu, Norbert Furtmann, 2015 .

Understanding chemoinformatics: a unifying approach.

Jürgen Bajorath, J. Bajorath, 2004, Drug Discovery Today.

Eugen Lounkine, Jürgen Bajorath, Lu Tan, 2009 .

Computational Method for the Systematic Identification of Analog Series and Key Compounds Representing Series and Their Biological Activity Profiles.

Jürgen Bajorath, Dilyana Dimova, Dagmar Stumpfe, 2016, Journal of medicinal chemistry.

Computational medicinal chemistry.

Jürgen Bajorath, Martin Stahl, 2011, Journal of medicinal chemistry.

Improving the utility of molecular scaffolds for medicinal and computational chemistry.

Jürgen Bajorath, 2018, Future medicinal chemistry.

Systematic assessment of coordinated activity cliffs formed by kinase inhibitors and detailed characterization of activity cliff clusters and associated SAR information.

Jürgen Bajorath, Dilyana Dimova, Dagmar Stumpfe, 2015, European journal of medicinal chemistry.

Activity artifacts in drug discovery and different facets of compound promiscuity

Jürgen Bajorath, J. Bajorath, 2014, F1000Research.

AnalogExplorer2 – Stereochemistry sensitive graphical analysis of large analog series

Jürgen Bajorath, Ye Hu, Martin Vogt, 2015, F1000Research.

Molecular Fingerprint Recombination: Generating Hybrid Fingerprints for Similarity Searching from Different Fingerprint Types

Jürgen Bajorath, Britta Nisius, J. Bajorath, 2009, ChemMedChem.

Currently available 3D activity cliffs and 2D-analogs of 3D-cliff compounds

Jürgen Bajorath, Ye Hu, Norbert Furtmann, 2015 .

Comparison of binary and 2D QSAR analyses using inhibitors of human carbonic anhydrase II as a test case

Jürgen Bajorath, J. Bajorath, Hua Gao, 1998, Molecular Diversity.

High-resolution view of compound promiscuity

Jürgen Bajorath, Ye Hu, J. Bajorath, 2013, F1000Research.

Method for the evaluation of structure-activity relationship information associated with coordinated activity cliffs.

Jürgen Bajorath, Dilyana Dimova, Dagmar Stumpfe, 2014, Journal of medicinal chemistry.

Mechanism-based bipartite matching molecular series graphs to identify structural modifications of receptor ligands that lead to mechanism hopping

Jürgen Bajorath, Preeti Iyer, J. Bajorath, 2012 .

On the evolving open peer review culture for chemical information science

Jürgen Bajorath, W. Patrick Walters, J. Bajorath, 2015, F1000Research.

Chapter 4:Molecular Similarity Analysis in Virtual Screening

Jürgen Bajorath, Lisa Peltason, 2008 .

Computational Chemical Biology: Identification of Small Molecular Probes that Discriminate between Members of Target Protein Families

Jürgen Bajorath, Dilyana Dimova, J. Bajorath, 2012, Chemical biology & drug design.

Hit Expansion through Computational Selectivity Searching

Jürgen Bajorath, José Batista, Dagmar Stumpfe, 2009, ChemMedChem.

Advances in Computational Medicinal Chemistry: Matched Molecular Pair Analysis

Anne Mai Wassermann, Jürgen Bajorath, Preeti Iyer, 2012 .

Advances in Computational Medicinal Chemistry: A Reflection on the Evolution of the Field and Perspective Going Forward.

Jürgen Bajorath, W. Patrick Walters, Brian K. Shoichet, 2016, Journal of medicinal chemistry.

Evaluation of Kinase Inhibitor Selectivity Using Cell‐based Profiling Data

Jürgen Bajorath, Filip Miljković, J. Bajorath, 2018, Molecular informatics.

Molecular crime scene investigation – dusting for fingerprints

Jürgen Bajorath, J. Bajorath, 2013 .

X-ray Structure-Based Chemoinformatic Analysis Identifies Promiscuous Ligands Binding to Proteins from Different Classes with Varying Shapes

Jürgen Bajorath, Christian Feldmann, J. Bajorath, 2020, International journal of molecular sciences.

Reduction and Recombination of Fingerprints of Different Design Increase Compound Recall and the Structural Diversity of Hits

Jürgen Bajorath, Britta Nisius, J. Bajorath, 2010, Chemical biology & drug design.

From Structure–Activity to Structure–Selectivity Relationships: Quantitative Assessment, Selectivity Cliffs, and Key Compounds

Jürgen Bajorath, Ye Hu, Lisa Peltason, 2009, ChemMedChem.

Matched molecular pairs derived by retrosynthetic fragmentation

Jürgen Bajorath, Antonio de la Vega de León, J. Bajorath, 2014 .

Exploring Activity Cliffs from a Chemoinformatics Perspective

Jürgen Bajorath, J. Bajorath, 2014, Molecular informatics.

Identification of Amino Acid Residues Important for Ligand Binding to Fas

Jürgen Bajorath, Jeffrey A. Ledbetter, Alejandro Aruffo, 1997, The Journal of experimental medicine.

Matched molecular pair-based data sets for computer-aided medicinal chemistry.

Jürgen Bajorath, Ye Hu, Bijun Zhang, 2014, F1000Research.

Identification and analysis of promiscuity cliffs formed by bioactive compounds and experimental implications

Jürgen Bajorath, Dilyana Dimova, Erik Gilberg, 2017 .

Bayesian Interpretation of a Distance Function for Navigating High-Dimensional Descriptor Spaces.

Jürgen Bajorath, Martin Vogt, Jeffrey W. Godden, 2007 .

Both Extracellular Immunoglobin-like Domains of CD80 Contain Residues Critical for Binding T Cell Surface Receptors CTLA-4 and CD28 (*)

Jürgen Bajorath, Peter S. Linsley, Alejandro Aruffo, 1995, The Journal of Biological Chemistry.

Mapping of inhibitors and activity data to the human kinome and exploring promiscuity from a ligand and target perspective

Jürgen Bajorath, Ye Hu, Ryo Kunimoto, 2017, Chemical biology & drug design.

Activity classes from different categories

Jürgen Bajorath, Ye Hu, Martin Vogt, 2016 .

Comparison of 2D Fingerprint Methods for Multiple‐Template Similarity Searching on Compound Activity Classes of Increasing Structural Diversity

Jürgen Bajorath, Hanna Eckert, J. Bajorath, 2007, ChemMedChem.

Jürgen Bajorath, Lisa Peltason, J. Bajorath, 2007, Chemistry & biology.

Computational method for estimating progression saturation of analog series

Jürgen Bajorath, Tomoyuki Miyao, Ryo Kunimoto, 2018, RSC advances.

The Future of Virtual Compound Screening

Kathrin Heikamp, Jürgen Bajorath, J. Bajorath, 2013, Chemical biology & drug design.

Emerging Chemical Patterns: A New Methodology for Molecular Classification and Compound Selection.

Jürgen Bajorath, Jens Auer, 2007 .

Simulation of sequential screening experiments using emerging chemical patterns.

Jürgen Bajorath, Jens Auer, 2008, Medicinal chemistry (Shariqah (United Arab Emirates)).

Methods for Computer‐aided Chemical Biology. Part 1: Design of a Benchmark System for the Evaluation of Compound Selectivity

Jürgen Bajorath, Dagmar Stumpfe, Ingo Vogt, 2007, Chemical biology & drug design.

The 'SAR Matrix' method and its extensions for applications in medicinal chemistry and chemogenomics.

Jürgen Bajorath, Disha Gupta-Ostermann, J. Bajorath, 2014, F1000Research.

ChEMBL20 data sets for multi-property landscape analysis

Jürgen Bajorath, Thomas Schultz, Dilyana Dimova, 2015 .

Prediction of Compound Profiling Matrices, Part II: Relative Performance of Multitask Deep Learning and Random Forest Classification on the Basis of Varying Amounts of Training Data

Jürgen Bajorath, Raquel Rodríguez-Pérez, J. Bajorath, 2018, ACS omega.

Elucidation of structure-activity relationship pathways in biological screening data.

Mathias Wawer, Jürgen Bajorath, Lisa Peltason, 2009, Journal of medicinal chemistry.

Promiscuity of inhibitors of human protein kinases at varying data confidence levels and test frequencies

Jürgen Bajorath, Giulio Rastelli, Annachiara Tinivella, 2017 .

Computational Analysis of Multi‐target Structure–Activity Relationships to Derive Preference Orders for Chemical Modifications toward Target Selectivity

Anne Mai Wassermann, Jürgen Bajorath, Lisa Peltason, 2010, ChemMedChem.

Applied Virtual Screening: Strategies, Recommendations, and Caveats

Jürgen Bajorath, Dagmar Stumpfe, J. Bajorath, 2011 .

Monitoring the Progression of Structure-Activity Relationship Information during Lead Optimization.

Jürgen Bajorath, Dilyana Dimova, Veerabahu Shanmugasundaram, 2016, Journal of medicinal chemistry.

Computational Method to Evaluate Progress in Lead Optimization.

Jürgen Bajorath, Martin Vogt, Dimitar Yonchev, 2018, Journal of medicinal chemistry.

Computational Analysis of Kinase Inhibitors Identifies Promiscuity Cliffs across the Human Kinome

Jürgen Bajorath, Filip Miljković, J. Bajorath, 2018, ACS Omega.

Introducing the LASSO graph for compound data set representation and structure-activity relationship analysis.

Jürgen Bajorath, Ye Hu, Disha Gupta-Ostermann, 2012, Journal of medicinal chemistry.

Computational Methodologies for Compound Database Searching that Utilize Experimental Protein–Ligand Interaction Information

Jürgen Bajorath, José Batista, J. Bajorath, 2010, Chemical biology & drug design.

Rationalizing Structure and Target Relationships between Current Drugs

Jürgen Bajorath, Ye Hu, 2012, The AAPS Journal.

Analysis of structure-based virtual screening studies and characterization of identified active compounds.

Jürgen Bajorath, Dagmar Stumpfe, Peter Ripphausen, 2012, Future medicinal chemistry.

Methods for Computer‐Aided Chemical Biology. Part 4: Selectivity Searching for Ion Channel Ligands and Mapping of Molecular Fragments as Selectivity Markers

Hanna Geppert, Eugen Lounkine, Jürgen Bajorath, 2009, Chemical biology & drug design.

Median Partitioning: A Novel Method for the Selection of Representative Subsets from Large Compound Pools.

Jürgen Bajorath, Ling Xue, Jeffrey W. Godden, 2002 .

Extraction of structure-activity relationship information from high-throughput screening data.

Mathias Wawer, Jürgen Bajorath, 2009, Current medicinal chemistry.

The ‘SAR Matrix’ method and its extensions for applications in medicinal chemistry and chemogenomics

Jürgen Bajorath, Disha Gupta-Ostermann, J. Bajorath, 2014, F1000Research.

Many Approved Drugs Have Bioactive Analogs With Different Target Annotations

Eugen Lounkine, Jürgen Bajorath, Ye Hu, 2014, The AAPS Journal.

Introducing a new category of activity cliffs combining different compound similarity criteria.

Jürgen Bajorath, Huabin Hu, J. Bajorath, 2020, RSC medicinal chemistry.

Formal concept analysis for the identification of molecular fragment combinations specific for active and highly potent compounds.

Eugen Lounkine, Jürgen Bajorath, Jens Auer, 2008, Journal of medicinal chemistry.

Assessment of Molecular Similarity from the Analysis of Randomly Generated Structural Fragment Populations.

Jürgen Bajorath, José Batista, Jeffrey W. Godden, 2006 .

Entering the ‘big data’ era in medicinal chemistry: molecular promiscuity analysis revisited

Jürgen Bajorath, Ye Hu, J. Bajorath, 2017, Future science OA.

POT-DMC: A virtual screening method for the identification of potent hits.

Jürgen Bajorath, Florence L Stahura, J. Bajorath, 2004, Journal of medicinal chemistry.

Fragment Formal Concept Analysis Accurately Classifies Compounds with Closely Related Biological Activities

Eugen Lounkine, Jürgen Bajorath, Felix A. Krüger, 2009, ChemMedChem.

Analyzing compound activity records and promiscuity degrees in light of publication statistics

Jürgen Bajorath, Ye Hu, J. Bajorath, 2016, F1000Research.

Molecular Design

Jürgen Bajorath, Uli Fechner, Hans-Joachim Böhm, 2007 .

Comprehensive Analysis of Single‐ and Multi‐Target Activity Cliffs Formed by Currently Available Bioactive Compounds

Jürgen Bajorath, Dilyana Dimova, Anne M Wassermann, 2011, Chemical biology & drug design.

Chemical space visualization: transforming multidimensional chemical spaces into similarity-based molecular networks.

Jürgen Bajorath, J. Bajorath, Antonio de la Vega de León, 2016, Future medicinal chemistry.

Apparent Asymmetry in Fingerprint Similarity Searching is a Direct Consequence of Differences in Bit Densities and Molecular Size

Jürgen Bajorath, Yuan Wang, Hanna Eckert, 2007, ChemMedChem.

Identifying relationships between unrelated pharmaceutical target proteins on the basis of shared active compounds

Jürgen Bajorath, Ryo Kunimoto, Filip Miljković, 2017, Future science OA.

Matched molecular pair-based data sets for computer-aided medicinal chemistry

Jürgen Bajorath, Ye Hu, Bijun Zhang, 2014, F1000Research.

Pros and cons of virtual screening based on public "Big Data": In silico mining for new bromodomain inhibitors.

Jürgen Bajorath, Dragos Horvath, Alexandre Varnek, 2019, European journal of medicinal chemistry.

Interpretation of Compound Activity Predictions from Complex Machine Learning Models Using Local Approximations and Shapley Values.

Jürgen Bajorath, Raquel Rodríguez-Pérez, J. Bajorath, 2020, Journal of medicinal chemistry.

Methods for Computer‐aided Chemical Biology. Part 2: Evaluation of Compound Selectivity Using 2D Molecular Fingerprints

Jürgen Bajorath, Dagmar Stumpfe, Ingo Vogt, 2007, Chemical biology & drug design.

Matched molecular pair analysis of small molecule microarray data identifies promiscuity cliffs and reveals molecular origins of extreme compound promiscuity.

Jürgen Bajorath, Ye Hu, Dilyana Dimova, 2012, Journal of medicinal chemistry.

Activity profile relationships between structurally similar promiscuous compounds.

Jürgen Bajorath, Ye Hu, J. Bajorath, 2013, European journal of medicinal chemistry.

Hierarchical Analysis of Bioactive Matched Molecular Pairs, Encoded Chemical Transformations, and Associated Substructures

Jürgen Bajorath, Ye Hu, J. Bajorath, 2016, Molecular informatics.

Exploring target-selectivity patterns of molecular scaffolds.

Jürgen Bajorath, Ye Hu, J. Bajorath, 2010, ACS medicinal chemistry letters.

Computational method for the identification of third generation activity cliffs

Jürgen Bajorath, Huabin Hu, Dagmar Stumpfe, 2020, MethodsX.

Recent Progress in Structure-Based Evaluation of Compound Promiscuity

Jürgen Bajorath, Erik Gilberg, J. Bajorath, 2019, ACS Omega.

Computational Methods for Medicinal Chemistry

Jürgen Bajorath, W. Patrick Walters, Hualiang Jiang, 2015 .

Activity profiles of analog series containing pan assay interference compounds

Jürgen Bajorath, Dagmar Stumpfe, Erik Gilberg, 2017 .