Jürgen Bajorath

发表

Searching for Target-Selective Compounds Using Different Combinations of Multiclass Support Vector Machine Ranking Methods, Kernel Functions, and Fingerprint Descriptors

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

Growth of Ligand-Target Interaction Data in ChEMBL Is Associated with Increasing and Activity Measurement-Dependent Compound Promiscuity

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

Introduction of an Information-Theoretic Method to Predict Recovery Rates of Active Compounds for Bayesian in Silico Screening: Theory and Screening Trials

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

Faculty Opinions recommendation of KLIFS: a knowledge-based structural database to navigate kinase-ligand interaction space.

Jürgen Bajorath, 2013 .

Systematic Exploration of Activity Cliffs Containing Privileged Substructures.

Jürgen Bajorath, Huabin Hu, J. Bajorath, 2020, Molecular pharmaceutics.

Machine learning and similarity-based virtual screening techniques

Jürgen Bajorath, 2013 .

Influence of Varying Training Set Composition and Size on Support Vector Machine-Based Prediction of Active Compounds

Jürgen Bajorath, Martin Vogt, Raquel Rodríguez-Pérez, 2017, J. Chem. Inf. Model..

Lessons Learned from Molecular Scaffold Analysis

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

Quantitative Comparison of Three-Dimensional Activity Landscapes of Compound Data Sets Based upon Topological Features

Jürgen Bajorath, Martin Vogt, Javed Iqbal, 2020, ACS omega.

Support Vector Machine Classification and Regression Prioritize Different Structural Features for Binary Compound Activity and Potency Value Prediction

Jürgen Bajorath, Martin Vogt, Raquel Rodríguez-Pérez, 2017, ACS omega.

Scaffold Distributions in Bioactive Molecules, Clinical Trials Compounds, and Drugs

Jürgen Bajorath, Ye Hu, J. Bajorath, 2010, ChemMedChem.

Faculty Opinions recommendation of Accurate and reliable prediction of relative ligand binding potency in prospective drug discovery by way of a modern free-energy calculation protocol and force field.

Jürgen Bajorath, 2015 .

Target Family-Directed Exploration of Scaffolds with Different SAR Profiles

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

Deep SAR matrix: SAR matrix expansion for advanced analog design using deep learning architectures

Jürgen Bajorath, Atsushi Yoshimori, J. Bajorath, 2020 .

Compound optimization monitor (COMO) method for computational evaluation of progress in medicinal chemistry projects

Jürgen Bajorath, Martin Vogt, Dimitar Yonchev, 2019 .

Prediction of Compound Profiling Matrices Using Machine Learning

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

Filtering and Counting of Extended Connectivity Fingerprint Features Maximizes Compound Recall and the Structural Diversity of Hits

Jürgen Bajorath, Eugen Lounkine, Ye Hu, 2009, Chemical biology & drug design.

Faculty Opinions recommendation of Assessing the impact of generative AI on medicinal chemistry.

Jürgen Bajorath, 2020 .

Development of a Fingerprint Reduction Approach for Bayesian Similarity Searching Based on Kullback-Leibler Divergence Analysis

Jürgen Bajorath, Britta Nisius, Martin Vogt, 2009, J. Chem. Inf. Model..

Balancing the Influence of Molecular Complexity on Fingerprint Similarity Searching

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

Design and Evaluation of a Molecular Fingerprint Involving the Transformation of Property Descriptor Values into a Binary Classification Scheme

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

Graph Mining for SAR Transfer Series

Anne Mai Wassermann, Jürgen Bajorath, Mathias Wawer, 2012, J. Chem. Inf. Model..

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

Faculty Opinions recommendation of Rules for identifying potentially reactive or promiscuous compounds.

Jürgen Bajorath, 2012 .

Large-Scale Assessment of Activity Landscape Feature Probabilities of Bioactive Compounds

Jürgen Bajorath, Preeti Iyer, Dilyana Dimova, 2014, J. Chem. Inf. Model..

Computational chemistry in pharmaceutical research: at the crossroads

Jürgen Bajorath, 2011, Journal of Computer-Aided Molecular Design.

Compounds with multitarget activity: structure-based analysis and machine learning

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

Faculty Opinions recommendation of Public domain databases for medicinal chemistry.

Jürgen Bajorath, 2012 .

Rationalizing Three-Dimensional Activity Landscapes and the Influence of Molecular Representations on Landscape Topology and the Formation of Activity Cliffs

Jürgen Bajorath, Preeti Iyer, Lisa Sach-Peltason, 2010, J. Chem. Inf. Model..

Systematic extraction of structure-activity relationship information from biological screening data

Jürgen Bajorath, Mathias Wawer, Lisa Sach-Peltason, 2010, J. Cheminformatics.

Structural Interpretation of Activity Cliffs Revealed by Systematic Analysis of Structure-Activity Relationships in Analog Series

Jürgen Bajorath, Mihiret Tekeste Sisay, Lisa Sach-Peltason, 2009, J. Chem. Inf. Model..

Faculty Opinions recommendation of Quantifying, visualizing, and monitoring lead optimization.

Jürgen Bajorath, 2015 .

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