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ORCIDJürgen Bajorath
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- affiliation: University of Bonn, Germany
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2020 – today
- 2024
[j198]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): 55 (2024)- [j197]Jürgen Bajorath:
Milestones in chemoinformatics: global view of the field. J. Cheminformatics 16(1): 124 (2024) - [j196]Hengwei Chen
, Jürgen Bajorath:
Combining a Chemical Language Model and the Structure-Activity Relationship Matrix Formalism for Generative Design of Potent Compounds with Core Structure and Substituent Modifications. J. Chem. Inf. Model. 64(23): 8784-8795 (2024) - 2023
- [j195]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): 107-115 (2023) - [j194]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): 4 (2023) - [j193]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): 5916-5926 (2023) - [j192]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): 7032-7044 (2023) - [j191]Andrea Mastropietro, Giuseppe Pasculli, Jürgen Bajorath:
Learning characteristics of graph neural networks predicting protein-ligand affinities. Nat. Mac. Intell. 5(12): 1427-1436 (2023) - [d28]Hengwei Chen, Jürgen Bajorath:
Designing highly potent compounds using a chemical language model. Zenodo, 2023
- [d27]Elena Xerxa, Oliver Laufkötter, Jürgen Bajorath:
Allosteric kinase inhibitors. Zenodo, 2023 - [d26]Elena Xerxa, Filip Miljkovic, Jürgen Bajorath:
Protein kinase inhibitors. Zenodo, 2023 - 2022
- [j190]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): 355-362 (2022) - [j189]Thomas Blaschke, Jürgen Bajorath:
Fine-tuning of a generative neural network for designing multi-target compounds. J. Comput. Aided Mol. Des. 36(5): 363-371 (2022) - [j188]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): 623-638 (2022) - [j187]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): 82 (2022) - [j186]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): 1246-1255 (2022) - [d25]Hengwei Chen, Martin Vogt, Jürgen Bajorath:
DeepAC - Conditional transformer-based chemical language model for the prediction of activity cliffs formed by bioactive compounds. Zenodo, 2022 - [d24]Christian Feldmann, Jürgen Bajorath:
Calculation of Exact Shapley Values for Support Vector Machines with Tanimoto Kernel Enables Model Interpretation. Version 1. Zenodo, 2022 [all versions] - [d23]Christian Feldmann, Jürgen Bajorath:
Calculation of Exact Shapley Values for Support Vector Machines with Tanimoto Kernel Enables Model Interpretation. Version 2. Zenodo, 2022 [all versions] - [d22]Friederike Maite Siemers, Christian Feldmann, Jürgen Bajorath:
Minimal Data Requirements for Accurate Compound Activity Prediction Using Machine Learning Methods of Different Complexity. Zenodo, 2022 - [d21]Atsushi Yoshimori, Filip Miljkovic, Jürgen Bajorath:
Candidate compounds from the design of covalent Bruton's tyrosine kinase (BTK) inhibitors via focused deep generative modeling. Zenodo, 2022 - 2021
- [j185]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): 285-295 (2021) - [j184]Atsushi Yoshimori, Huabin Hu, Jürgen Bajorath:
Adapting the DeepSARM approach for dual-target ligand design. J. Comput. Aided Mol. Des. 35(5): 587-600 (2021) - [j183]Javed Iqbal, Martin Vogt, Jürgen Bajorath:
Prediction of activity cliffs on the basis of images using convolutional neural networks. J. Comput. Aided Mol. Des. 35(12): 1157-1164 (2021) - [j182]Edgar López-López, Jürgen Bajorath, José L. Medina-Franco:
Informatics for Chemistry, Biology, and Biomedical Sciences. J. Chem. Inf. Model. 61(1): 26-35 (2021) - [d20]Dagmar Stumpfe, Alexander Hoch, Jürgen Bajorath:
Metacores. Zenodo, 2021 - [i14]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: 397 (2021) - 2020
- [j181]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): 1-10 (2020) - [j180]Dagmar Stumpfe, Huabin Hu, Jürgen Bajorath:
Advances in exploring activity cliffs. J. Comput. Aided Mol. Des. 34(9): 929-942 (2020) - [j179]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): 943-952 (2020) - [j178]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): 1013-1026 (2020) - [j177]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): 1207-1218 (2020) - [j176]Javed Iqbal, Martin Vogt, Jürgen Bajorath:
Activity landscape image analysis using convolutional neural networks. J. Cheminformatics 12(1): 34 (2020) - [j175]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): 36 (2020) - [j174]Dagmar Stumpfe, Jürgen Bajorath:
Current Trends, Overlooked Issues, and Unmet Challenges in Virtual Screening. J. Chem. Inf. Model. 60(9): 4112-4115 (2020) - [j173]Gerald M. Maggiora, José L. Medina-Franco, Javed Iqbal, Martin Vogt, Jürgen Bajorath:
From Qualitative to Quantitative Analysis of Activity and Property Landscapes. J. Chem. Inf. Model. 60(12): 5873-5880 (2020) - [d19]Christian Feldmann, Dimitar G. Yonchev, Jürgen Bajorath:
Data sets for compound promiscuity analysis and predictions. Zenodo, 2020 - [d18]Christian Feldmann, Dimitar G. Yonchev, Dagmar Stumpfe, Jürgen Bajorath:
Systematic Data Analysis and Diagnostic Machine Learning Reveal Differences between Compounds with Single- and Multitarget Activity. Zenodo, 2020 - [d17]Kosuke Takeuchi, Ryo Kunimoto, Jürgen Bajorath:
Substituents isolated from analog series. Zenodo, 2020 - [d16]Martin Vogt, Jürgen Bajorath:
ccbmlib - a Python Package for Modeling Tanimoto Coefficient Distributions for Molecular Fingerprints. Version v1.0. Zenodo, 2020 [all versions] - [d15]Martin Vogt, Jürgen Bajorath:
ccbmlib - a Python Package for Modeling Tanimoto Coefficient Distributions for Molecular Fingerprints. Version v1.1. Zenodo, 2020 [all versions] - [i13]Martin Vogt, Jürgen Bajorath:
ccbmlib - a Python package for modeling Tanimoto similarity value distributions. F1000Research 9: 100 (2020)
2010 – 2019
- 2019
- [j172]Filip Miljkovic, Martin Vogt, Jürgen Bajorath:
Systematic computational identification of promiscuity cliff pathways formed by inhibitors of the human kinome. J. Comput. Aided Mol. Des. 33(6): 559-572 (2019) - [j171]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): 729-743 (2019) - [j170]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): 54:1-54:14 (2019) - [j169]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): 61:1-61:9 (2019) - [j168]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): 564-572 (2019) - [j167]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): 983-992 (2019) - [j166]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): 993-1004 (2019) - [j165]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): 3072-3079 (2019) - [j164]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): 3727-3734 (2019) - [d14]Christian Feldmann, Filip Miljkovic, Dimitar G. Yonchev, Jürgen Bajorath:
Promiscuous compounds with activity against different target classes. Zenodo, 2019 - [d13]Filip Miljkovic, Jürgen Bajorath:
Promiscuity cliffs (PCs), promiscuity cliff pathways (PCPs), and promiscuity hubs (PHs) formed by inhibitors of human kinases. Zenodo, 2019 - [d12]Filip Miljkovic, Raquel Rodríguez-Pérez, Jürgen Bajorath:
Machine Learning Models for Predicting Kinase Inhibitors with Different Binding Modes. Version 1. Zenodo, 2019 [all versions] - [d11]Filip Miljkovic, Raquel Rodríguez-Pérez, Jürgen Bajorath:
Machine Learning Models for Predicting Kinase Inhibitors with Different Binding Modes. Version 2. Zenodo, 2019 [all versions] - [d10]Raquel Rodríguez-Pérez, Jürgen Bajorath:
Compound activity classes from ChEMBL for machine learning analysis. Version 1. Zenodo, 2019 [all versions] - [d9]Raquel Rodríguez-Pérez, Jürgen Bajorath:
Compound activity classes from ChEMBL for machine learning analysis. Version 2. Zenodo, 2019 [all versions] - [i12]Jürgen Bajorath:
Repositioning the Chemical Information Science Gateway. F1000Research 8: 976 (2019) - 2018
- [j163]Ryo Kunimoto, Jürgen Bajorath:
Design of a tripartite network for the prediction of drug targets. J. Comput. Aided Mol. Des. 32(2): 321-330 (2018) - [j162]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): 759-767 (2018) - [d8]Erik Gilberg, Michael Gütschow, Jürgen Bajorath:
X-Ray Structures of Target-Ligand Complexes Containing Compounds with Assay Interference Potential. Version 1. Zenodo, 2018 [all versions] - [d7]Erik Gilberg, Michael Gütschow, Jürgen Bajorath:
X-Ray Structures of Target-Ligand Complexes Containing Compounds with Assay Interference Potential. Version 2. Zenodo, 2018 [all versions] - [d6]Huabin Hu, Dagmar Stumpfe, Jürgen Bajorath:
Target set-dependent activity cliffs. Zenodo, 2018 - 2017
- [j161]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): 603-608 (2017) - [j160]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): 779-788 (2017) - [j159]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): 961-977 (2017) - [j158]Raquel Rodríguez-Pérez, Martin Vogt, Jürgen Bajorath:
Influence of Varying Training Set Composition and Size on Support Vector Machine-Based Prediction of Active Compounds. J. Chem. Inf. Model. 57(4): 710-716 (2017) - [j157]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): 1218-1232 (2017) - [d5]Carmen Cerchia, Dilyana Dimova, Antonio Lavecchia, Jürgen Bajorath:
Collection of analog series-based (ASB) scaffolds shared between ZINC, ChEMBL, and PubChem. Zenodo, 2017 - [d4]Dilyana Dimova, Jürgen Bajorath:
Collection of analog series-based (ASB) scaffolds. Zenodo, 2017 - [d3]Erik Gilberg, Dagmar Stumpfe, Jürgen Bajorath:
Analog Series of Compounds with High Frequency of Activity in Screening Assays. Version 1. Zenodo, 2017 [all versions] - [d2]Erik Gilberg, Dagmar Stumpfe, Jürgen Bajorath:
Compounds with multi-target activity from X-ray structures, corresponding analog series, and associated scaffolds. Zenodo, 2017 - [d1]Dagmar Stumpfe, Erik Gilberg, Jürgen Bajorath:
Analog Series of Compounds with High Frequency of Activity in Screening Assays. Version 2. Zenodo, 2017 [all versions] - [i11]Thomas Blaschke, Marcus Olivecrona, Ola Engkvist, Jürgen Bajorath, Hongming Chen:
Application of generative autoencoder in de novo molecular design. CoRR abs/1711.07839 (2017) - [i10]Tomoyuki Miyao, Kimito Funatsu, Jürgen Bajorath:
Exploring differential evolution for inverse QSAR analysis. F1000Research 6: 1285- (2017) - [i9]Jürgen Bajorath:
Expanding the chemical information science gateway. F1000Research 6: 1764- (2017) - 2016
- [j156]Mengjun Wu, Martin Vogt, Gerald M. Maggiora, Jürgen Bajorath:
Design of chemical space networks on the basis of Tversky similarity. J. Comput. Aided Mol. Des. 30(1): 1-12 (2016) - [j155]Martin Vogt, Dagmar Stumpfe, Gerald M. Maggiora, Jürgen Bajorath:
Lessons learned from the design of chemical space networks and opportunities for new applications. J. Comput. Aided Mol. Des. 30(3): 191-208 (2016) - [j154]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): 447-456 (2016) - [j153]Ryo Kunimoto, Martin Vogt, Jürgen Bajorath:
Maximum common substructure-based Tversky index: an asymmetric hybrid similarity measure. J. Comput. Aided Mol. Des. 30(7): 523-531 (2016) - [j152]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): 841-849 (2016) - [j151]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): 300-307 (2016) - [j150]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): 580-587 (2016) - [j149]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): 1631-1640 (2016) - [i8]Swarit Jasial, Ye Hu, Martin Vogt, Jürgen Bajorath:
Activity-relevant similarity values for fingerprints and implications for similarity searching. F1000Research 5: 591 (2016) - [i7]Ye Hu, Jürgen Bajorath:
Analyzing compound activity records and promiscuity degrees in light of publication statistics. F1000Research 5: 1227 (2016) - [i6]Antonio de la Vega de León, Jürgen Bajorath:
Design of chemical space networks incorporating compound distance relationships. F1000Research 5: 2634 (2016) - 2015
- [j148]Magdalena Zwierzyna, Martin Vogt, Gerald M. Maggiora, Jürgen Bajorath:
Design and characterization of chemical space networks for different compound data sets. J. Comput. Aided Mol. Des. 29(2): 113-125 (2015) - [j147]Roman Garnett, Thomas Gärtner, Martin Vogt, Jürgen Bajorath:
Introducing the 'active search' method for iterative virtual screening. J. Comput. Aided Mol. Des. 29(4): 305-314 (2015) - [j146]Bijun Zhang, Martin Vogt, Gerald M. Maggiora, Jürgen Bajorath:
Comparison of bioactive chemical space networks generated using substructure- and fingerprint-based measures of molecular similarity. J. Comput. Aided Mol. Des. 29(7): 595-608 (2015) - [j145]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): 695-705 (2015) - [j144]Bijun Zhang, Martin Vogt, Gerald M. Maggiora, Jürgen Bajorath:
Design of chemical space networks using a Tanimoto similarity variant based upon maximum common substructures. J. Comput. Aided Mol. Des. 29(10): 937-950 (2015) - [j143]Bijun Zhang, Martin Vogt, Gerald M. Maggiora, Jürgen Bajorath:
Erratum to: Design of chemical space networks using a Tanimoto similarity variant based upon maximum common substructures. J. Comput. Aided Mol. Des. 29(11): 1071-1072 (2015) - [j142]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): 676-686 (2015) - [j141]Jenny Balfer, Jürgen Bajorath:
Visualization and Interpretation of Support Vector Machine Activity Predictions. J. Chem. Inf. Model. 55(6): 1136-1147 (2015) - [i5]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: 75 (2015) - [i4]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: 168 (2015) - [i3]Ye Hu, Bijun Zhang, Martin Vogt, Jürgen Bajorath:
AnalogExplorer2 - Stereochemistry sensitive graphical analysis of large analog series. F1000Research 4: 1031 (2015) - 2014
- [j140]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): 795-802 (2014) - [j139]Bijun Zhang, Martin Vogt, Jürgen Bajorath:
Design of an activity landscape view taking compound-based feature probabilities into account. J. Comput. Aided Mol. Des. 28(9): 919-926 (2014) - [j138]Antonio de la Vega de León, Jürgen Bajorath:
Compound optimization through data set-dependent chemical transformations. J. Cheminformatics 6(S-1): 5 (2014) - [j137]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): 13 (2014) - [j136]Shilva Kayastha, Dilyana Dimova, Preeti Iyer, Martin Vogt, Jürgen Bajorath:
Large-Scale Assessment of Activity Landscape Feature Probabilities of Bioactive Compounds. J. Chem. Inf. Model. 54(2): 442-450 (2014) - [j135]Dagmar Stumpfe, Dilyana Dimova, Jürgen Bajorath:
Composition and Topology of Activity Cliff Clusters Formed by Bioactive Compounds. J. Chem. Inf. Model. 54(2): 451-461 (2014) - [j134]Disha Gupta-Ostermann, Veerabahu Shanmugasundaram, Jürgen Bajorath:
Neighborhood-Based Prediction of Novel Active Compounds from SAR Matrices. J. Chem. Inf. Model. 54(3): 801-809 (2014) - [j133]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): 1301-1310 (2014) - [j132]Jenny Balfer, Jürgen Bajorath:
Introduction of a Methodology for Visualization and Graphical Interpretation of Bayesian Classification Models. J. Chem. Inf. Model. 54(9): 2451-2468 (2014) - [j131]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): 2654-2663 (2014) - [j130]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): 3056-3066 (2014) - [i2]Ye Hu, Jürgen Bajorath:
Compound data sets and software tools for chemoinformatics and medicinal chemistry applications: update and data transfer. F1000Research 3: 69 (2014) - 2013
- [j129]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): 115-124 (2013) - [j128]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): 665-674 (2013) - [j127]Martin Vogt, Jürgen Bajorath:
Statistical modeling of value distributions of similarity coefficients in virtual screening and its application to predicting fingerprint search performance. J. Cheminformatics 5(S-1): 5 (2013) - [j126]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): 312-326 (2013) - [j125]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): 545-552 (2013) - [j124]Mohsen Ahmadi, Martin Vogt, Preeti Iyer, Jürgen Bajorath, Holger Fröhlich:
Predicting Potent Compounds via Model-Based Global Optimization. J. Chem. Inf. Model. 53(3): 553-559 (2013) - [j123]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): 791-801 (2013) - [j122]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): 1067-1072 (2013) - [j121]Antonio de la Vega de León, Jürgen Bajorath:
Compound Optimization through Data Set-Dependent Chemical Transformations. J. Chem. Inf. Model. 53(6): 1263-1271 (2013) - [j120]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): 1272-1281 (2013) - [j119]Bijun Zhang, Ye Hu, Jürgen Bajorath:
SAR Transfer across Different Targets. J. Chem. Inf. Model. 53(7): 1589-1594 (2013) - [j118]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): 1595-1601 (2013) - [j117]Martin Vogt, Preeti Iyer, Gerald M. Maggiora, Jürgen Bajorath:
Conditional Probabilities of Activity Landscape Features for Individual Compounds. J. Chem. Inf. Model. 53(7): 1602-1612 (2013) - [j116]Martin Vogt, Jürgen Bajorath:
Similarity Searching for Potent Compounds Using Feature Selection. J. Chem. Inf. Model. 53(7): 1613-1619 (2013) - [j115]Jenny Balfer, Martin Vogt, Jürgen Bajorath:
Searching for Closely Related Ligands with Different Mechanisms of Action Using Machine Learning and Mapping Algorithms. J. Chem. Inf. Model. 53(9): 2252-2274 (2013) - [j114]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): 2275-2281 (2013) - [j113]Vigneshwaran Namasivayam, Preeti Iyer, Jürgen Bajorath:
Prediction of Individual Compounds Forming Activity Cliffs Using Emerging Chemical Patterns. J. Chem. Inf. Model. 53(12): 3131-3139 (2013) - [p1]Jens Auer, Martin Vogt, Jürgen Bajorath:
Emerging Chemical Patterns - Theory and Applications. Contrast Data Mining 2013: 253-268 - 2012
- [j112]Jürgen Bajorath:
Computational chemistry in pharmaceutical research: at the crossroads. J. Comput. Aided Mol. Des. 26(1): 11-12 (2012) - [j111]Ruifang Li, Jürgen Bajorath:
Systematic assessment of scaffold distances in ChEMBL: prioritization of compound data sets for scaffold hopping analysis in virtual screening. J. Comput. Aided Mol. Des. 26(10): 1101-1109 (2012) - [j110]Anne Mai Wassermann, Jürgen Bajorath:
A computational method to facilitate structure-activity relationship transfer. J. Cheminformatics 4(S-1): 3 (2012) - [j109]Dilyana Dimova, Jürgen Bajorath:
Design of multi-target activity landscapes that capture hierarchical activity cliff distributions. J. Cheminformatics 4(S-1): 4 (2012) - [j108]Kathrin Heikamp, Anne Mai Wassermann, Jürgen Bajorath:
Potency-directed similarity searching using support vector machines. J. Cheminformatics 4(S-1): 12 (2012) - [j107]Ye Hu, Jürgen Bajorath:
Exploration of 3D Activity Cliffs on the Basis of Compound Binding Modes and Comparison of 2D and 3D Cliffs. J. Chem. Inf. Model. 52(3): 670-677 (2012) - [j106]Vigneshwaran Namasivayam, Jürgen Bajorath:
Searching for Coordinated Activity Cliffs Using Particle Swarm Optimization. J. Chem. Inf. Model. 52(4): 927-934 (2012) - [j105]Disha Gupta-Ostermann, Mathias Wawer, Anne Mai Wassermann, Jürgen Bajorath:
Graph Mining for SAR Transfer Series. J. Chem. Inf. Model. 52(4): 935-942 (2012) - [j104]Xiaoying Hu, Ye Hu, Martin Vogt, Dagmar Stumpfe, Jürgen Bajorath:
MMP-Cliffs: Systematic Identification of Activity Cliffs on the Basis of Matched Molecular Pairs. J. Chem. Inf. Model. 52(5): 1138-1145 (2012) - [j103]Ye Hu, Norbert Furtmann, Michael Gütschow, Jürgen Bajorath:
Systematic Identification and Classification of Three-Dimensional Activity Cliffs. J. Chem. Inf. Model. 52(6): 1490-1498 (2012) - [j102]Anne Mai Wassermann, Peter Haebel, Nils Weskamp, Jürgen Bajorath:
SAR Matrices: Automated Extraction of Information-Rich SAR Tables from Large Compound Data Sets. J. Chem. Inf. Model. 52(7): 1769-1776 (2012) - [j101]Ye Hu, Jürgen Bajorath:
Extending the Activity Cliff Concept: Structural Categorization of Activity Cliffs and Systematic Identification of Different Types of Cliffs in the ChEMBL Database. J. Chem. Inf. Model. 52(7): 1806-1811 (2012) - [j100]Preeti Iyer, Dilyana Dimova, Martin Vogt, Jürgen Bajorath:
Navigating High-Dimensional Activity Landscapes: Design and Application of the Ligand-Target Differentiation Map. J. Chem. Inf. Model. 52(8): 1962-1969 (2012) - [j99]Dagmar Stumpfe, Jürgen Bajorath:
Frequency of Occurrence and Potency Range Distribution of Activity Cliffs in Bioactive Compounds. J. Chem. Inf. Model. 52(9): 2348-2353 (2012) - [j98]Kathrin Heikamp, Xiaoying Hu, Aixia Yan, Jürgen Bajorath:
Prediction of Activity Cliffs Using Support Vector Machines. J. Chem. Inf. Model. 52(9): 2354-2365 (2012) - [j97]Ye Hu, Jürgen Bajorath:
Growth of Ligand-Target Interaction Data in ChEMBL Is Associated with Increasing and Activity Measurement-Dependent Compound Promiscuity. J. Chem. Inf. Model. 52(10): 2550-2558 (2012) - [j96]Disha Gupta-Ostermann, Jürgen Bajorath:
Identification of Multitarget Activity Ridges in High-Dimensional Bioactivity Spaces. J. Chem. Inf. Model. 52(10): 2579-2586 (2012) - [j95]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): 2848-2855 (2012) - [j94]Antonio de la Vega de León, Jürgen Bajorath:
Design of a Three-Dimensional Multitarget Activity Landscape. J. Chem. Inf. Model. 52(11): 2876-2883 (2012) - [j93]Bijun Zhang, Anne Mai Wassermann, Martin Vogt, Jürgen Bajorath:
Systematic Assessment of Compound Series with SAR Transfer Potential. J. Chem. Inf. Model. 52(12): 3138-3143 (2012) - [i1]Ye Hu, Jürgen Bajorath:
Freely available compound data sets and software tools for chemoinformatics and computational medicinal chemistry applications. F1000Research 1: 11 (2012) - 2011
- [j92]Ye Hu, Jürgen Bajorath:
Combining Horizontal and Vertical Substructure Relationships in Scaffold Hierarchies for Activity Prediction. J. Chem. Inf. Model. 51(2): 248-257 (2011) - [j91]Dilyana Dimova, Mathias Wawer, Anne Mai Wassermann, Jürgen Bajorath:
Design of Multitarget Activity Landscapes That Capture Hierarchical Activity Cliff Distributions. J. Chem. Inf. Model. 51(2): 258-266 (2011) - [j90]Anne Mai Wassermann, Britta Nisius, Martin Vogt, Jürgen Bajorath:
Correction to Identification of Descriptors Capturing Compound Class-Specific Features by Mutual Information Analysis. J. Chem. Inf. Model. 51(2): 508-509 (2011) - [j89]Preeti Iyer, Ye Hu, Jürgen Bajorath:
SAR Monitoring of Evolving Compound Data Sets Using Activity Landscapes. J. Chem. Inf. Model. 51(3): 532-540 (2011) - [j88]Peter Ripphausen, Britta Nisius, Mathias Wawer, Jürgen Bajorath:
Rationalizing the Role of SAR Tolerance for Ligand-Based Virtual Screening. J. Chem. Inf. Model. 51(4): 837-842 (2011) - [j87]Preeti Iyer, Dagmar Stumpfe, Jürgen Bajorath:
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. J. Chem. Inf. Model. 51(6): 1281-1286 (2011) - [j86]Vigneshwaran Namasivayam, Preeti Iyer, Jürgen Bajorath:
Extraction of Discontinuous Structure-Activity Relationships from Compound Data Sets through Particle Swarm Optimization. J. Chem. Inf. Model. 51(7): 1545-1551 (2011) - [j85]Ye Hu, Dagmar Stumpfe, Jürgen Bajorath:
Lessons Learned from Molecular Scaffold Analysis. J. Chem. Inf. Model. 51(8): 1742-1753 (2011) - [j84]Kathrin Heikamp, Jürgen Bajorath:
Large-Scale Similarity Search Profiling of ChEMBL Compound Data Sets. J. Chem. Inf. Model. 51(8): 1831-1839 (2011) - [j83]Martin Vogt, Yun Huang, Jürgen Bajorath:
From Activity Cliffs to Activity Ridges: Informative Data Structures for SAR Analysis. J. Chem. Inf. Model. 51(8): 1848-1856 (2011) - [j82]Anne Mai Wassermann, Jürgen Bajorath:
A Data Mining Method To Facilitate SAR Transfer. J. Chem. Inf. Model. 51(8): 1857-1866 (2011) - [j81]Kathrin Heikamp, Jürgen Bajorath:
How Do 2D Fingerprints Detect Structurally Diverse Active Compounds? Revealing Compound Subset-Specific Fingerprint Features through Systematic Selection. J. Chem. Inf. Model. 51(9): 2254-2265 (2011) - [j80]Peter Ripphausen, Anne Mai Wassermann, Jürgen Bajorath:
REPROVIS-DB: A Benchmark System for Ligand-Based Virtual Screening Derived from Reproducible Prospective Applications. J. Chem. Inf. Model. 51(10): 2467-2473 (2011) - [j79]Martin Vogt, Jürgen Bajorath:
Introduction of the Conditional Correlated Bernoulli Model of Similarity Value Distributions and its Application to the Prospective Prediction of Fingerprint Search Performance. J. Chem. Inf. Model. 51(10): 2496-2506 (2011) - [j78]Ruifang Li, Dagmar Stumpfe, Martin Vogt, Hanna Geppert, Jürgen Bajorath:
Development of a Method To Consistently Quantify the Structural Distance between Scaffolds and To Assess Scaffold Hopping Potential. J. Chem. Inf. Model. 51(10): 2507-2514 (2011) - [j77]Preeti Iyer, Ye Hu, Jürgen Bajorath:
SAR Monitoring of Evolving Compound Data Sets Using Activity Landscapes. J. Chem. Inf. Model. 51(11): 3026 (2011) - [j76]Dagmar Stumpfe, Jürgen Bajorath:
Assessing the Confidence Level of Public Domain Compound Activity Data and the Impact of Alternative Potency Measurements on SAR Analysis. J. Chem. Inf. Model. 51(12): 3131-3137 (2011) - [j75]Ye Hu, Jürgen Bajorath:
Target Family-Directed Exploration of Scaffolds with Different SAR Profiles. J. Chem. Inf. Model. 51(12): 3138-3148 (2011) - 2010
- [j74]Martin Vogt, Anne Mai Wassermann, Jürgen Bajorath:
Application of Information - Theoretic Concepts in Chemoinformatics. Inf. 1(2): 60-73 (2010) - [j73]Eugen Lounkine, Jürgen Bajorath:
Adaptation of formal concept analysis for the systematic exploration of structure-activity and structure-selectivity relationships. J. Cheminformatics 2(S-1): 21 (2010) - [j72]Mathias Wawer, Lisa Peltason, Jürgen Bajorath:
Systematic extraction of structure-activity relationship information from biological screening data. J. Cheminformatics 2(S-1): 22 (2010) - [j71]Eugen Lounkine, Mathias Wawer, Anne Mai Wassermann, Jürgen Bajorath:
SARANEA: A Freely Available Program To Mine Structure-Activity and Structure-Selectivity Relationship Information in Compound Data Sets. J. Chem. Inf. Model. 50(1): 68-78 (2010) - [j70]José Batista, Lu Tan, Jürgen Bajorath:
Atom-Centered Interacting Fragments and Similarity Search Applications. J. Chem. Inf. Model. 50(1): 79-86 (2010) - [j69]Hanna Geppert, Martin Vogt, Jürgen Bajorath:
Current Trends in Ligand-Based Virtual Screening: Molecular Representations, Data Mining Methods, New Application Areas, and Performance Evaluation. J. Chem. Inf. Model. 50(2): 205-216 (2010) - [j68]Hany E. A. Ahmed, Martin Vogt, Jürgen Bajorath:
Design and Evaluation of Bonded Atom Pair Descriptors. J. Chem. Inf. Model. 50(4): 487-499 (2010) - [j67]Ye Hu, Jürgen Bajorath:
Molecular Scaffolds with High Propensity to Form Multi-Target Activity Cliffs. J. Chem. Inf. Model. 50(4): 500-510 (2010) - [j66]Lisa Peltason, Preeti Iyer, Jürgen Bajorath:
Rationalizing Three-Dimensional Activity Landscapes and the Influence of Molecular Representations on Landscape Topology and the Formation of Activity Cliffs. J. Chem. Inf. Model. 50(6): 1021-1033 (2010) - [j65]Lu Tan, José Batista, Jürgen Bajorath:
Rationalization of the Performance and Target Dependence of Similarity Searching Incorporating Protein-Ligand Interaction Information. J. Chem. Inf. Model. 50(6): 1042-1052 (2010) - [j64]Anne Mai Wassermann, Jürgen Bajorath:
Chemical Substitutions That Introduce Activity Cliffs Across Different Compound Classes and Biological Targets. J. Chem. Inf. Model. 50(7): 1248-1256 (2010) - [j63]Mathias Wawer, Jürgen Bajorath:
Similarity-Potency Trees: A Method to Search for SAR Information in Compound Data Sets and Derive SAR Rules. J. Chem. Inf. Model. 50(8): 1395-1409 (2010) - [j62]Anne Mai Wassermann, Britta Nisius, Martin Vogt, Jürgen Bajorath:
Identification of Descriptors Capturing Compound Class-Specific Features by Mutual Information Analysis. J. Chem. Inf. Model. 50(11): 1935-1940 (2010) - [j61]Ye Hu, Jürgen Bajorath:
Polypharmacology Directed Compound Data Mining: Identification of Promiscuous Chemotypes with Different Activity Profiles and Comparison to Approved Drugs. J. Chem. Inf. Model. 50(12): 2112-2118 (2010)
2000 – 2009
- 2009
- [j60]Eugen Lounkine, Jürgen Bajorath:
Topological Fragment Index for the Analysis of Molecular Substructures and Their Topological Environment in Active Compounds. J. Chem. Inf. Model. 49(2): 162-168 (2009) - [j59]Eugen Lounkine, Ye Hu, José Batista, Jürgen Bajorath:
Relevance of Feature Combinations for Similarity Searching Using General or Activity Class-Directed Molecular Fingerprints. J. Chem. Inf. Model. 49(3): 561-570 (2009) - [j58]Anne Mai Wassermann, Hanna Geppert, Jürgen Bajorath:
Searching for Target-Selective Compounds Using Different Combinations of Multiclass Support Vector Machine Ranking Methods, Kernel Functions, and Fingerprint Descriptors. J. Chem. Inf. Model. 49(3): 582-592 (2009) - [j57]Hanna Geppert, Jens Humrich, Dagmar Stumpfe, Thomas Gärtner, Jürgen Bajorath:
Ligand Prediction from Protein Sequence and Small Molecule Information Using Support Vector Machines and Fingerprint Descriptors. J. Chem. Inf. Model. 49(4): 767-779 (2009) - [j56]Britta Nisius, Martin Vogt, Jürgen Bajorath:
Development of a Fingerprint Reduction Approach for Bayesian Similarity Searching Based on Kullback-Leibler Divergence Analysis. J. Chem. Inf. Model. 49(6): 1347-1358 (2009) - [j55]Eugen Lounkine, Dagmar Stumpfe, Jürgen Bajorath:
Molecular Formal Concept Analysis for Compound Selectivity Profiling in Biologically Annotated Databases. J. Chem. Inf. Model. 49(6): 1359-1368 (2009) - [j54]Yuan Wang, Jürgen Bajorath:
Development of a Compound Class-Directed Similarity Coefficient That Accounts for Molecular Complexity Effects in Fingerprint Searching. J. Chem. Inf. Model. 49(6): 1369-1376 (2009) - [j53]Yuan Wang, Hanna Geppert, Jürgen Bajorath:
Shannon Entropy-Based Fingerprint Similarity Search Strategy. J. Chem. Inf. Model. 49(7): 1687-1691 (2009) - [j52]Anne Mai Wassermann, Hanna Geppert, Jürgen Bajorath:
Ligand Prediction for Orphan Targets Using Support Vector Machines and Various Target-Ligand Kernels Is Dominated by Nearest Neighbor Effects. J. Chem. Inf. Model. 49(10): 2155-2167 (2009) - [j51]Mihiret Tekeste Sisay, Lisa Peltason, Jürgen Bajorath:
Structural Interpretation of Activity Cliffs Revealed by Systematic Analysis of Structure-Activity Relationships in Analog Series. J. Chem. Inf. Model. 49(10): 2179-2189 (2009) - [j50]Martin Vogt, Britta Nisius, Jürgen Bajorath:
Predicting the similarity search performance of fingerprints and their combination with molecular property descriptors using probabilistic and information theoretic modeling. Stat. Anal. Data Min. 2(2): 123-134 (2009) - 2008
- [j49]Yuan Wang, Jürgen Bajorath:
Balancing the Influence of Molecular Complexity on Fingerprint Similarity Searching. J. Chem. Inf. Model. 48(1): 75-84 (2008) - [j48]Martin Vogt, Jürgen Bajorath:
Bayesian Similarity Searching in High-Dimensional Descriptor Spaces Combined with Kullback-Leibler Descriptor Divergence Analysis. J. Chem. Inf. Model. 48(2): 247-255 (2008) - [j47]Hanna Geppert, Tamás Horváth, Thomas Gärtner, Stefan Wrobel, Jürgen Bajorath:
Support-Vector-Machine-Based Ranking Significantly Improves the Effectiveness of Similarity Searching Using 2D Fingerprints and Multiple Reference Compounds. J. Chem. Inf. Model. 48(4): 742-746 (2008) - [j46]Eugen Lounkine, Jürgen Bajorath:
Core Trees and Consensus Fragment Sequences for Molecular Representation and Similarity Analysis. J. Chem. Inf. Model. 48(6): 1161-1166 (2008) - [j45]Ingo Vogt, Jürgen Bajorath:
Design and Exploration of Target-Selective Chemical Space Representations. J. Chem. Inf. Model. 48(7): 1389-1395 (2008) - [j44]Jens Auer, Jürgen Bajorath:
Distinguishing between Bioactive and Modeled Compound Conformations through Mining of Emerging Chemical Patterns. J. Chem. Inf. Model. 48(9): 1747-1753 (2008) - [j43]Yuan Wang, Jürgen Bajorath:
Bit Silencing in Fingerprints Enables the Derivation of Compound Class-Directed Similarity Metrics. J. Chem. Inf. Model. 48(9): 1754-1759 (2008) - [j42]Thomas J. Crisman, Mihiret Tekeste Sisay, Jürgen Bajorath:
Ligand-Target Interaction-Based Weighting of Substructures for Virtual Screening. J. Chem. Inf. Model. 48(10): 1955-1964 (2008) - [j41]Lu Tan, Eugen Lounkine, Jürgen Bajorath:
Similarity Searching Using Fingerprints of Molecular Fragments Involved in Protein-Ligand Interactions. J. Chem. Inf. Model. 48(12): 2308-2312 (2008) - 2007
- [j40]Martin Vogt, Jeffrey W. Godden, Jürgen Bajorath:
Bayesian Interpretation of a Distance Function for Navigating High-Dimensional Descriptor Spaces. J. Chem. Inf. Model. 47(1): 39-46 (2007) - [j39]José Batista, Jürgen Bajorath:
Chemical Database Mining through Entropy-Based Molecular Similarity Assessment of Randomly Generated Structural Fragment Populations. J. Chem. Inf. Model. 47(1): 59-68 (2007) - [j38]Martin Vogt, Jürgen Bajorath:
Introduction of an Information-Theoretic Method to Predict Recovery Rates of Active Compounds for Bayesian in Silico Screening: Theory and Screening Trials. J. Chem. Inf. Model. 47(2): 337-341 (2007) - [j37]Ingo Vogt, Jürgen Bajorath:
Analysis of a High-Throughput Screening Data Set Using Potency-Scaled Molecular Similarity Algorithms. J. Chem. Inf. Model. 47(2): 367-375 (2007) - [j36]Hanna Eckert, Jürgen Bajorath:
Exploring Peptide-likeness of Active Molecules Using 2D Fingerprint Methods. J. Chem. Inf. Model. 47(4): 1366-1378 (2007) - [j35]José Batista, Jürgen Bajorath:
Mining of Randomly Generated Molecular Fragment Populations Uncovers Activity-Specific Fragment Hierarchies. J. Chem. Inf. Model. 47(4): 1405-1413 (2007) - [j34]Eugen Lounkine, José Batista, Jürgen Bajorath:
Mapping of Activity-Specific Fragment Pathways Isolated from Random Fragment Populations Reveals the Formation of Coherent Molecular Cores. J. Chem. Inf. Model. 47(6): 2133-2139 (2007) - 2006
- [j33]Jeffrey W. Godden, Jürgen Bajorath:
A Distance Function for Retrieval of Active Molecules from Complex Chemical Space Representations. J. Chem. Inf. Model. 46(3): 1094-1097 (2006) - [j32]Hanna Eckert, Ingo Vogt, Jürgen Bajorath:
Mapping Algorithms for Molecular Similarity Analysis and Ligand-Based Virtual Screening: Design of DynaMAD and Comparison with MAD and DMC. J. Chem. Inf. Model. 46(4): 1623-1634 (2006) - [j31]José Batista, Jeffrey W. Godden, Jürgen Bajorath:
Assessment of Molecular Similarity from the Analysis of Randomly Generated Structural Fragment Populations. J. Chem. Inf. Model. 46(5): 1937-1944 (2006) - [j30]Jens Auer, Jürgen Bajorath:
Emerging Chemical Patterns: A New Methodology for Molecular Classification and Compound Selection. J. Chem. Inf. Model. 46(6): 2502-2514 (2006) - [j29]Hanna Eckert, Jürgen Bajorath:
Design and Evaluation of a Novel Class-Directed 2D Fingerprint to Search for Structurally Diverse Active Compounds. J. Chem. Inf. Model. 46(6): 2515-2526 (2006) - 2005
- [j28]Jeffrey W. Godden, Florence L. Stahura, Jürgen Bajorath:
Anatomy of Fingerprint Search Calculations on Structurally Diverse Sets of Active Compounds. J. Chem. Inf. Model. 45(6): 1812-1819 (2005) - 2004
- [j27]Jeffrey W. Godden, John R. Furr, Ling Xue, Florence L. Stahura, Jürgen Bajorath:
Molecular Similarity Analysis and Virtual Screening by Mapping of Consensus Positions in Binary-Transformed Chemical Descriptor Spaces with Variable Dimensionality. J. Chem. Inf. Model. 44(1): 21-29 (2004) - [j26]Ling Xue, Jeffrey W. Godden, Florence L. Stahura, Jürgen Bajorath:
Similarity Search Profiles as a Diagnostic Tool for the Analysis of Virtual Screening Calculations. J. Chem. Inf. Model. 44(4): 1275-1281 (2004) - [j25]Ling Xue, Florence L. Stahura, Jürgen Bajorath:
Similarity Search Profiling Reveals Effects of Fingerprint Scaling in Virtual Screening. J. Chem. Inf. Model. 44(6): 2032-2039 (2004) - 2003
- [j24]Jeffrey W. Godden, John R. Furr, Jürgen Bajorath:
Recursive Median Partitioning for Virtual Screening of Large Databases. J. Chem. Inf. Comput. Sci. 43(1): 182-188 (2003) - [j23]Ling Xue, Jeffrey W. Godden, Florence L. Stahura, Jürgen Bajorath:
Design and Evaluation of a Molecular Fingerprint Involving the Transformation of Property Descriptor Values into a Binary Classification Scheme. J. Chem. Inf. Comput. Sci. 43(4): 1151-1157 (2003) - [j22]Ling Xue, Jeffrey W. Godden, Florence L. Stahura, Jürgen Bajorath:
Profile Scaling Increases the Similarity Search Performance of Molecular Fingerprints Containing Numerical Descriptors and Structural Keys. J. Chem. Inf. Comput. Sci. 43(4): 1218-1225 (2003) - 2002
- [j21]Jürgen Bajorath:
Chemoinformatics methods for systematic comparison of molecules from natural and synthetic sources and design of hybrid libraries. J. Comput. Aided Mol. Des. 16(5-6): 431-439 (2002) - [j20]Jeffrey W. Godden, Jürgen Bajorath:
Chemical Descriptors with Distinct Levels of Information Content and Varying Sensitivity to Differences between Selected Compound Databases Identified by SE-DSE Analysis. J. Chem. Inf. Comput. Sci. 42(1): 87-93 (2002) - [j19]Florence L. Stahura, Jeffrey W. Godden, Jürgen Bajorath:
Differential Shannon Entropy Analysis Identifies Molecular Property Descriptors that Predict Aqueous Solubility of Synthetic Compounds with High Accuracy in Binary QSAR Calculations. J. Chem. Inf. Comput. Sci. 42(3): 550-558 (2002) - [j18]Ling Xue, Jürgen Bajorath:
Accurate Partitioning of Compounds Belonging to Diverse Activity Classes. J. Chem. Inf. Comput. Sci. 42(3): 757-764 (2002) - [j17]Jeffrey W. Godden, Ling Xue, Douglas B. Kitchen, Florence L. Stahura, E. James Schermerhorn, Jürgen Bajorath:
Median Partitioning: A Novel Method for the Selection of Representative Subsets from Large Compound Pools. J. Chem. Inf. Comput. Sci. 42(4): 885-893 (2002) - [j16]Jeffrey W. Godden, Ling Xue, Jürgen Bajorath:
Classification of Biologically Active Compounds by Median Partitioning. J. Chem. Inf. Comput. Sci. 42(5): 1263-1269 (2002) - 2001
- [j15]Jürgen Bajorath:
Selected Concepts and Investigations in Compound Classification, Molecular Descriptor Analysis, and Virtual Screening. J. Chem. Inf. Comput. Sci. 41(2): 233-245 (2001) - [j14]Ling Xue, Florence L. Stahura, Jeffrey W. Godden, Jürgen Bajorath:
Mini-fingerprints Detect Similar Activity of Receptor Ligands Previously Recognized Only by Three-Dimensional Pharmacophore-Based Methods. J. Chem. Inf. Comput. Sci. 41(2): 394-401 (2001) - [j13]Ling Xue, Florence L. Stahura, Jeffrey W. Godden, Jürgen Bajorath:
Fingerprint Scaling Increases the Probability of Identifying Molecules with Similar Activity in Virtual Screening Calculations. J. Chem. Inf. Comput. Sci. 41(3): 746-753 (2001) - [j12]Jeffrey W. Godden, Jürgen Bajorath:
Differential Shannon Entropy as a Sensitive Measure of Differences in Database Variability of Molecular Descriptors. J. Chem. Inf. Comput. Sci. 41(4): 1060-1066 (2001) - 2000
- [j11]Jeffrey W. Godden, Ling Xue, Jürgen Bajorath:
Combinatorial Preferences Affect Molecular Similarity/Diversity Calculations Using Binary Fingerprints and Tanimoto Coefficients. J. Chem. Inf. Comput. Sci. 40(1): 163-166 (2000) - [j10]Jeffrey W. Godden, Florence L. Stahura, Jürgen Bajorath:
Variability of Molecular Descriptors in Compound Databases Revealed by Shannon Entropy Calculations. J. Chem. Inf. Comput. Sci. 40(3): 796-800 (2000) - [j9]Ling Xue, Jürgen Bajorath:
Molecular Descriptors for Effective Classification of Biologically Active Compounds Based on Principal Component Analysis Identified by a Genetic Algorithm. J. Chem. Inf. Comput. Sci. 40(3): 801-809 (2000) - [j8]Ling Xue, Jeffrey W. Godden, Jürgen Bajorath:
Evaluation of Descriptors and Mini-Fingerprints for the Identification of Molecules with Similar Activity. J. Chem. Inf. Comput. Sci. 40(5): 1227-1234 (2000) - [j7]Florence L. Stahura, Jeffrey W. Godden, Ling Xue, Jürgen Bajorath:
Distinguishing between Natural Products and Synthetic Molecules by Descriptor Shannon Entropy Analysis and Binary QSAR Calculations. J. Chem. Inf. Comput. Sci. 40(5): 1245-1252 (2000)
1990 – 1999
- 1999
- [j6]Jürgen Bajorath:
Analysis of Fas-ligand interactions using a molecular model of the receptor-ligand interface. J. Comput. Aided Mol. Des. 13(4): 409-418 (1999) - [j5]Jeffrey W. Godden, Florence L. Stahura, Jürgen Bajorath:
Statistical analysis of computational docking of large compound data bases to distinct protein binding sites. J. Comput. Chem. 20(15): 1634-1643 (1999) - [j4]Hua Gao, Christopher I. Williams, Paul Labute, Jürgen Bajorath:
Binary Quantitative Structure-Activity Relationship (QSAR) Analysis of Estrogen Receptor Ligands. J. Chem. Inf. Comput. Sci. 39(1): 164-168 (1999) - [j3]Ling Xue, Jeffrey W. Godden, Hua Gao, Jürgen Bajorath:
Identification of a Preferred Set of Molecular Descriptors for Compound Classification Based on Principal Component Analysis. J. Chem. Inf. Comput. Sci. 39(4): 699-704 (1999) - [j2]Ling Xue, Jeffrey W. Godden, Jürgen Bajorath:
Database Searching for Compounds with Similar Biological Activity Using Short Binary Bit String Representations of Molecules. J. Chem. Inf. Comput. Sci. 39(5): 881-886 (1999) - [c1]Jürgen Bajorath, Teri E. Klein, Terry P. Lybrand, Jiri Novotny:
Computer-Aided Drug Design - Session Introduction. Pacific Symposium on Biocomputing 1999: 413-414 - 1997
- [j1]Jürgen Bajorath, Alejandro Aruffo:
Prediction of the three-dimensional structure of the human Fas receptor by comparative molecular modeling. J. Comput. Aided Mol. Des. 11(1): 3-8 (1997)
Coauthor Index
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Citation data
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load citations from opencitations.net
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OpenAlex data
Load additional information about publications from 
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last updated on 2025-01-09 19:36 CET by the dblp team
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