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ORCIDJürgen Bajorath
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- affiliation: University of Bonn, Germany
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2020 – today
- 2024
[j197]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)- [j196]Jürgen Bajorath:
Milestones in chemoinformatics: global view of the field. J. Cheminformatics 16(1): 124 (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]