Jürgen Bajorath

ORCID

Publication Activity (10 Years)

Years Active: 1997-2024
Publications (10 Years): 68

Top Venues

J. Comput. Aided Mol. Des.

J. Chem. Inf. Model.

F1000Research

J. Cheminformatics

Publications

Hengwei Chen, Jürgen Bajorath
Generative design of compounds with desired potency from target protein sequences using a multimodal biochemical language model. J. Cheminformatics 16 (1) (2024)
Shunsuke Tamura, Tomoyuki Miyao, Jürgen Bajorath
Large-scale prediction of activity cliffs using machine and deep learning methods of increasing complexity. J. Cheminformatics 15 (1) (2023)
Tiago Janela, Jürgen Bajorath
Anatomy of Potency Predictions Focusing on Structural Analogues with Increasing Potency Differences Including Activity Cliffs. J. Chem. Inf. Model. 63 (22) (2023)
Kohei Umedera, Atsushi Yoshimori, Hengwei Chen, Hiroyuki Kouji, Hiroyuki Nakamura, Jürgen Bajorath
DeepCubist: Molecular Generator for Designing Peptidomimetics based on Complex three-dimensional scaffolds. J. Comput. Aided Mol. Des. 37 (2) (2023)
Nicola Gambacorta, Fulvio Ciriaco, Nicola Amoroso, Cosimo Damiano Altomare, Jürgen Bajorath, Orazio Nicolotti
CIRCE: Web-Based Platform for the Prediction of Cannabinoid Receptor Ligands Using Explainable Machine Learning. J. Chem. Inf. Model. 63 (18) (2023)
Andrea Mastropietro, Giuseppe Pasculli, Jürgen Bajorath
Learning characteristics of graph neural networks predicting protein-ligand affinities. Nat. Mac. Intell. 5 (12) (2023)
Tiago Janela, Jürgen Bajorath
Simple nearest-neighbour analysis meets the accuracy of compound potency predictions using complex machine learning models. Nat. Mac. Intell. 4 (12) (2022)
Jürgen Bajorath, Ana L. Chávez-Hernández, Miquel Duran-Frigola, Eli Fernández-de Gortari, Johann Gasteiger, Edgar López-López, Gerald M. Maggiora, José L. Medina-Franco, Oscar Méndez-Lucio, Jordi Mestres, Ramón Alain Miranda-Quintana, Tudor I. Oprea, Fabien Plisson, Fernando D. Prieto-Martínez, Raquel Rodríguez-Pérez, Paola Rondón-Villarreal, Fernanda I. Saldívar-González, Norberto Sánchez-Cruz, Marilia Valli
Chemoinformatics and artificial intelligence colloquium: progress and challenges in developing bioactive compounds. J. Cheminformatics 14 (1) (2022)
Thomas Blaschke, Jürgen Bajorath
Fine-tuning of a generative neural network for designing multi-target compounds. J. Comput. Aided Mol. Des. 36 (5) (2022)
Raquel Rodríguez-Pérez, Jürgen Bajorath
Evolution of Support Vector Machine and Regression Modeling in Chemoinformatics and Drug Discovery. J. Comput. Aided Mol. Des. 36 (5) (2022)
Veerabahu Shanmugasundaram, Jürgen Bajorath, Ralph E. Christoffersen, James D. Petke, W. Jeffrey Howe, Mark A. Johnson, Dimitris K. Agrafiotis, Pil Lee, Leslie A. Kuhn, Jay T. Goodwin, M. Katharine Holloway, Thompson N. Doman, W. Patrick Walters, Suzanne K. Schreyer, José L. Medina-Franco, Karina Martínez-Mayorga, Linda L. Restifo
Epilogue to the Gerald Maggiora Festschrift: a tribute to an exemplary mentor, colleague, collaborator, and innovator. J. Comput. Aided Mol. Des. 36 (9) (2022)
Raquel Rodríguez-Pérez, Jürgen Bajorath
Evaluation of multi-target deep neural network models for compound potency prediction under increasingly challenging test conditions. J. Comput. Aided Mol. Des. 35 (3) (2021)
José L. Medina-Franco, Karina Martínez-Mayorga, Eli Fernández-de Gortari, Johannes Kirchmair, Jürgen Bajorath
Rationality over fashion and hype in drug design. F1000Research 10 (2021)
Javed Iqbal, Martin Vogt, Jürgen Bajorath
Prediction of activity cliffs on the basis of images using convolutional neural networks. J. Comput. Aided Mol. Des. 35 (12) (2021)
Edgar López-López, Jürgen Bajorath, José L. Medina-Franco
Informatics for Chemistry, Biology, and Biomedical Sciences. J. Chem. Inf. Model. 61 (1) (2021)
Atsushi Yoshimori, Huabin Hu, Jürgen Bajorath
Adapting the DeepSARM approach for dual-target ligand design. J. Comput. Aided Mol. Des. 35 (5) (2021)
Raquel Rodríguez-Pérez, Jürgen Bajorath
Interpretation of machine learning models using shapley values: application to compound potency and multi-target activity predictions. J. Comput. Aided Mol. Des. 34 (10) (2020)
Huabin Hu, Jürgen Bajorath
Simplified activity cliff network representations with high interpretability and immediate access to SAR information. J. Comput. Aided Mol. Des. 34 (9) (2020)
Javed Iqbal, Martin Vogt, Jürgen Bajorath
Activity landscape image analysis using convolutional neural networks. J. Cheminformatics 12 (1) (2020)
Filip Miljkovic, Jürgen Bajorath
Data structures for computational compound promiscuity analysis and exemplary applications to inhibitors of the human kinome. J. Comput. Aided Mol. Des. 34 (1) (2020)
Raquel Rodríguez-Pérez, Filip Miljkovic, Jürgen Bajorath
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. J. Cheminformatics 12 (1) (2020)
Dimitar G. Yonchev, Jürgen Bajorath
DeepCOMO: from structure-activity relationship diagnostics to generative molecular design using the compound optimization monitor methodology. J. Comput. Aided Mol. Des. 34 (12) (2020)
Gerald M. Maggiora, José L. Medina-Franco, Javed Iqbal, Martin Vogt, Jürgen Bajorath
From Qualitative to Quantitative Analysis of Activity and Property Landscapes. J. Chem. Inf. Model. 60 (12) (2020)
Dagmar Stumpfe, Jürgen Bajorath
Current Trends, Overlooked Issues, and Unmet Challenges in Virtual Screening. J. Chem. Inf. Model. 60 (9) (2020)
Dagmar Stumpfe, Huabin Hu, Jürgen Bajorath
Advances in exploring activity cliffs. J. Comput. Aided Mol. Des. 34 (9) (2020)
Martin Vogt, Jürgen Bajorath
ccbmlib - a Python package for modeling Tanimoto similarity value distributions. F1000Research 9 (2020)
Atsushi Yoshimori, Yuichi Horita, Toru Tanoue, Jürgen Bajorath
Method for Systematic Analogue Search Using the Mega SAR Matrix Database. J. Chem. Inf. Model. 59 (9) (2019)
Tomoyuki Miyao, Kimito Funatsu, Jürgen Bajorath
Exploring Alternative Strategies for the Identification of Potent Compounds Using Support Vector Machine and Regression Modeling. J. Chem. Inf. Model. 59 (3) (2019)
Jürgen Bajorath
Repositioning the Chemical Information Science Gateway. F1000Research 8 (2019)
Tomoyuki Miyao, Swarit Jasial, Jürgen Bajorath, Kimito Funatsu
Evaluation of different virtual screening strategies on the basis of compound sets with characteristic core distributions and dissimilarity relationships. J. Comput. Aided Mol. Des. 33 (8) (2019)
J. Jesús Naveja, B. Angélica Pilón-Jiménez, Jürgen Bajorath, José L. Medina-Franco
A general approach for retrosynthetic molecular core analysis. J. Cheminformatics 11 (1) (2019)
Iuri Casciuc, Yuliana Zabolotna, Dragos Horvath, Gilles Marcou, Jürgen Bajorath, Alexandre Varnek
Virtual Screening with Generative Topographic Maps: How Many Maps Are Required? J. Chem. Inf. Model. 59 (1) (2019)
J. Jesús Naveja, Dagmar Stumpfe, José L. Medina-Franco, Jürgen Bajorath
Exploration of Target Synergy in Cancer Treatment by Cell-Based Screening Assay and Network Propagation Analysis. J. Chem. Inf. Model. 59 (6) (2019)
Filip Miljkovic, Martin Vogt, Jürgen Bajorath
Systematic computational identification of promiscuity cliff pathways formed by inhibitors of the human kinome. J. Comput. Aided Mol. Des. 33 (6) (2019)
Oliver Laufkötter, Noé Sturm, Jürgen Bajorath, Hongming Chen, Ola Engkvist
Combining structural and bioactivity-based fingerprints improves prediction performance and scaffold hopping capability. J. Cheminformatics 11 (1) (2019)
Tomoyuki Miyao, Kimito Funatsu, Jürgen Bajorath
Three-Dimensional Activity Landscape Models of Different Design and Their Application to Compound Mapping and Potency Prediction. J. Chem. Inf. Model. 59 (3) (2019)
Ryo Kunimoto, Jürgen Bajorath
Design of a tripartite network for the prediction of drug targets. J. Comput. Aided Mol. Des. 32 (2) (2018)
Tomoyuki Miyao, Jürgen Bajorath
Exploring ensembles of bioactive or virtual analogs of X-ray ligands for shape similarity searching. J. Comput. Aided Mol. Des. 32 (7) (2018)
Jürgen Bajorath
Expanding the chemical information science gateway. F1000Research 6 (2017)
Dilyana Dimova, Jürgen Bajorath
Is scaffold hopping a reliable indicator for the ability of computational methods to identify structurally diverse active compounds? J. Comput. Aided Mol. Des. 31 (7) (2017)
Ryo Kunimoto, Jürgen Bajorath
Exploring sets of molecules from patents and relationships to other active compounds in chemical space networks. J. Comput. Aided Mol. Des. 31 (9) (2017)
Raquel Rodríguez-Pérez, Martin Vogt, Jürgen Bajorath
Influence of Varying Training Set Composition and Size on Support Vector Machine-Based Prediction of Active Compounds. J. Chem. Inf. Model. 57 (4) (2017)
Tomoyuki Miyao, Kimito Funatsu, Jürgen Bajorath
Exploring differential evolution for inverse QSAR analysis. F1000Research 6 (2017)
Thomas Blaschke, Marcus Olivecrona, Ola Engkvist, Jürgen Bajorath, Hongming Chen
Application of generative autoencoder in de novo molecular design. CoRR (2017)
Shilva Kayastha, Ryo Kunimoto, Dragos Horvath, Alexandre Varnek, Jürgen Bajorath
From bird's eye views to molecular communities: two-layered visualization of structure-activity relationships in large compound data sets. J. Comput. Aided Mol. Des. 31 (11) (2017)
Shilva Kayastha, Dragos Horvath, Erik Gilberg, Michael Gütschow, Jürgen Bajorath, Alexandre Varnek
Privileged Structural Motif Detection and Analysis Using Generative Topographic Maps. J. Chem. Inf. Model. 57 (5) (2017)
Andrew Anighoro, Jürgen Bajorath
Binding mode similarity measures for ranking of docking poses: a case study on the adenosine A2A receptor. J. Comput. Aided Mol. Des. 30 (6) (2016)
Ye Hu, Jürgen Bajorath
Analyzing compound activity records and promiscuity degrees in light of publication statistics. F1000Research 5 (2016)
Martin Vogt, Dagmar Stumpfe, Gerald M. Maggiora, Jürgen Bajorath
Lessons learned from the design of chemical space networks and opportunities for new applications. J. Comput. Aided Mol. Des. 30 (3) (2016)
Antonio de la Vega de León, Jürgen Bajorath
Design of chemical space networks incorporating compound distance relationships. F1000Research 5 (2016)
Mengjun Wu, Martin Vogt, Gerald M. Maggiora, Jürgen Bajorath
Design of chemical space networks on the basis of Tversky similarity. J. Comput. Aided Mol. Des. 30 (1) (2016)
Andrew Anighoro, Jürgen Bajorath
Three-Dimensional Similarity in Molecular Docking: Prioritizing Ligand Poses on the Basis of Experimental Binding Modes. J. Chem. Inf. Model. 56 (3) (2016)
Andrew Anighoro, Antonio de la Vega de León, Jürgen Bajorath
Predicting bioactive conformations and binding modes of macrocycles. J. Comput. Aided Mol. Des. 30 (10) (2016)
Ryo Kunimoto, Martin Vogt, Jürgen Bajorath
Maximum common substructure-based Tversky index: an asymmetric hybrid similarity measure. J. Comput. Aided Mol. Des. 30 (7) (2016)
Swarit Jasial, Ye Hu, Jürgen Bajorath
Assessing the Growth of Bioactive Compounds and Scaffolds over Time: Implications for Lead Discovery and Scaffold Hopping. J. Chem. Inf. Model. 56 (2) (2016)
Dragos Horvath, Gilles Marcou, Alexandre Varnek, Shilva Kayastha, Antonio de la Vega de León, Jürgen Bajorath
Prediction of Activity Cliffs Using Condensed Graphs of Reaction Representations, Descriptor Recombination, Support Vector Machine Classification, and Support Vector Regression. J. Chem. Inf. Model. 56 (9) (2016)
Swarit Jasial, Ye Hu, Martin Vogt, Jürgen Bajorath
Activity-relevant similarity values for fingerprints and implications for similarity searching. F1000Research 5 (2016)
Magdalena Zwierzyna, Martin Vogt, Gerald M. Maggiora, Jürgen Bajorath
Design and characterization of chemical space networks for different compound data sets. J. Comput. Aided Mol. Des. 29 (2) (2015)
Disha Gupta-Ostermann, Yoichiro Hirose, Takenao Odagami, Hiroyuki Kouji, Jürgen Bajorath
Follow-up: Prospective compound design using the 'SAR Matrix' method and matrix-derived conditional probabilities of activity. F1000Research 4 (2015)
Antonio de la Vega de León, Shilva Kayastha, Dilyana Dimova, Thomas Schultz, Jürgen Bajorath
Visualization of multi-property landscapes for compound selection and optimization. J. Comput. Aided Mol. Des. 29 (8) (2015)
Bijun Zhang, Martin Vogt, Gerald M. Maggiora, Jürgen Bajorath
Erratum to: Design of chemical space networks using a Tanimoto similarity variant based upon maximum common substructures. J. Comput. Aided Mol. Des. 29 (11) (2015)
Ye Hu, Norbert Furtmann, Dagmar Stumpfe, Jürgen Bajorath
Comprehensive knowledge base of two- and three-dimensional activity cliffs for medicinal and computational chemistry. F1000Research 4 (2015)
Jenny Balfer, Jürgen Bajorath
Visualization and Interpretation of Support Vector Machine Activity Predictions. J. Chem. Inf. Model. 55 (6) (2015)
Bijun Zhang, Martin Vogt, Gerald M. Maggiora, Jürgen Bajorath
Comparison of bioactive chemical space networks generated using substructure- and fingerprint-based measures of molecular similarity. J. Comput. Aided Mol. Des. 29 (7) (2015)
Bijun Zhang, Martin Vogt, Gerald M. Maggiora, Jürgen Bajorath
Design of chemical space networks using a Tanimoto similarity variant based upon maximum common substructures. J. Comput. Aided Mol. Des. 29 (10) (2015)
Roman Garnett, Thomas Gärtner, Martin Vogt, Jürgen Bajorath
Introducing the 'active search' method for iterative virtual screening. J. Comput. Aided Mol. Des. 29 (4) (2015)
Ye Hu, Bijun Zhang, Martin Vogt, Jürgen Bajorath
AnalogExplorer2 - Stereochemistry sensitive graphical analysis of large analog series. F1000Research 4 (2015)
Andrew Anighoro, Dagmar Stumpfe, Kathrin Heikamp, Kristin Beebe, Leonard M. Neckers, Jürgen Bajorath, Giulio Rastelli
Computational Polypharmacology Analysis of the Heat Shock Protein 90 Interactome. J. Chem. Inf. Model. 55 (3) (2015)
Vigneshwaran Namasivayam, Disha Gupta-Ostermann, Jenny Balfer, Kathrin Heikamp, Jürgen Bajorath
Prediction of Compounds in Different Local Structure-Activity Relationship Environments Using Emerging Chemical Patterns. J. Chem. Inf. Model. 54 (5) (2014)
Ye Hu, Jürgen Bajorath
Compound data sets and software tools for chemoinformatics and medicinal chemistry applications: update and data transfer. F1000Research 3 (2014)
Dagmar Stumpfe, Dilyana Dimova, Jürgen Bajorath
Composition and Topology of Activity Cliff Clusters Formed by Bioactive Compounds. J. Chem. Inf. Model. 54 (2) (2014)
Jenny Balfer, Jürgen Bajorath
Introduction of a Methodology for Visualization and Graphical Interpretation of Bayesian Classification Models. J. Chem. Inf. Model. 54 (9) (2014)
Bijun Zhang, Martin Vogt, Jürgen Bajorath
Design of an activity landscape view taking compound-based feature probabilities into account. J. Comput. Aided Mol. Des. 28 (9) (2014)
Ye Hu, Jürgen Bajorath
Influence of Search Parameters and Criteria on Compound Selection, Promiscuity, and Pan Assay Interference Characteristics. J. Chem. Inf. Model. 54 (11) (2014)
Antonio de la Vega de León, Jürgen Bajorath
Compound optimization through data set-dependent chemical transformations. J. Cheminformatics 6 (S-1) (2014)
Norbert Furtmann, Jürgen Bajorath
Evaluation of molecular model-based discovery of ecto-5'-nucleotidase inhibitors on the basis of X-ray structures. J. Cheminformatics 6 (S-1) (2014)
Antonio de la Vega de León, Jürgen Bajorath
Prediction of Compound Potency Changes in Matched Molecular Pairs Using Support Vector Regression. J. Chem. Inf. Model. 54 (10) (2014)
Gerald M. Maggiora, Jürgen Bajorath
Chemical space networks: a powerful new paradigm for the description of chemical space. J. Comput. Aided Mol. Des. 28 (8) (2014)
Shilva Kayastha, Dilyana Dimova, Preeti Iyer, Martin Vogt, Jürgen Bajorath
Large-Scale Assessment of Activity Landscape Feature Probabilities of Bioactive Compounds. J. Chem. Inf. Model. 54 (2) (2014)
Disha Gupta-Ostermann, Veerabahu Shanmugasundaram, Jürgen Bajorath
Neighborhood-Based Prediction of Novel Active Compounds from SAR Matrices. J. Chem. Inf. Model. 54 (3) (2014)
Kathrin Heikamp, Jürgen Bajorath
Comparison of Confirmed Inactive and Randomly Selected Compounds as Negative Training Examples in Support Vector Machine-Based Virtual Screening. J. Chem. Inf. Model. 53 (7) (2013)
Vigneshwaran Namasivayam, Ye Hu, Jenny Balfer, Jürgen Bajorath
Classification of Compounds with Distinct or Overlapping Multi-Target Activities and Diverse Molecular Mechanisms Using Emerging Chemical Patterns. J. Chem. Inf. Model. 53 (6) (2013)
Bijun Zhang, Ye Hu, Jürgen Bajorath
SAR Transfer across Different Targets. J. Chem. Inf. Model. 53 (7) (2013)
Ye Hu, Gerald M. Maggiora, Jürgen Bajorath
Activity cliffs in PubChem confirmatory bioassays taking inactive compounds into account. J. Comput. Aided Mol. Des. 27 (2) (2013)
Martin Vogt, Jürgen Bajorath
Statistical modeling of value distributions of similarity coefficients in virtual screening and its application to predicting fingerprint search performance. J. Cheminformatics 5 (S-1) (2013)
Dagmar Stumpfe, Dilyana Dimova, Kathrin Heikamp, Jürgen Bajorath
Compound Pathway Model To Capture SAR Progression: Comparison of Activity Cliff-Dependent and -Independent Pathways. J. Chem. Inf. Model. 53 (5) (2013)
Dilyana Dimova, Dagmar Stumpfe, Jürgen Bajorath
Quantifying the Fingerprint Descriptor Dependence of Structure-Activity Relationship Information on a Large Scale. J. Chem. Inf. Model. 53 (9) (2013)
Martin Vogt, Preeti Iyer, Gerald M. Maggiora, Jürgen Bajorath
Conditional Probabilities of Activity Landscape Features for Individual Compounds. J. Chem. Inf. Model. 53 (7) (2013)
Disha Gupta-Ostermann, Ye Hu, Jürgen Bajorath
Systematic mining of analog series with related core structures in multi-target activity space. J. Comput. Aided Mol. Des. 27 (8) (2013)
Martin Vogt, Jürgen Bajorath
Similarity Searching for Potent Compounds Using Feature Selection. J. Chem. Inf. Model. 53 (7) (2013)
Ye Hu, Jürgen Bajorath
Systematic Identification of Scaffolds Representing Compounds Active against Individual Targets and Single or Multiple Target Families. J. Chem. Inf. Model. 53 (2) (2013)
Vigneshwaran Namasivayam, Preeti Iyer, Jürgen Bajorath
Prediction of Individual Compounds Forming Activity Cliffs Using Emerging Chemical Patterns. J. Chem. Inf. Model. 53 (12) (2013)
Jenny Balfer, Martin Vogt, Jürgen Bajorath
Searching for Closely Related Ligands with Different Mechanisms of Action Using Machine Learning and Mapping Algorithms. J. Chem. Inf. Model. 53 (9) (2013)
Antonio de la Vega de León, Jürgen Bajorath
Compound Optimization through Data Set-Dependent Chemical Transformations. J. Chem. Inf. Model. 53 (6) (2013)
Mohsen Ahmadi, Martin Vogt, Preeti Iyer, Jürgen Bajorath, Holger Fröhlich
Predicting Potent Compounds via Model-Based Global Optimization. J. Chem. Inf. Model. 53 (3) (2013)
Kathrin Heikamp, Jürgen Bajorath
Prediction of Compounds with Closely Related Activity Profiles Using Weighted Support Vector Machine Linear Combinations. J. Chem. Inf. Model. 53 (4) (2013)
Ye Hu, Jürgen Bajorath
Introduction of Target Cliffs as a Concept To Identify and Describe Complex Molecular Selectivity Patterns. J. Chem. Inf. Model. 53 (3) (2013)
Xiaoying Hu, Ye Hu, Martin Vogt, Dagmar Stumpfe, Jürgen Bajorath
MMP-Cliffs: Systematic Identification of Activity Cliffs on the Basis of Matched Molecular Pairs. J. Chem. Inf. Model. 52 (5) (2012)
Vigneshwaran Namasivayam, Jürgen Bajorath
Multiobjective Particle Swarm Optimization: Automated Identification of Structure-Activity Relationship-Informative Compounds with Favorable Physicochemical Property Distributions. J. Chem. Inf. Model. 52 (11) (2012)
Disha Gupta-Ostermann, Mathias Wawer, Anne Mai Wassermann, Jürgen Bajorath
Graph Mining for SAR Transfer Series. J. Chem. Inf. Model. 52 (4) (2012)

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Publication Activity (10 Years)

Top Topics

Top Venues

Jürgen Bajorath