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New Horizons in Drug Discovery - Understanding and Advancing Different Types of Kinase Inhibitors: Seven Years in Kinase Inhibitor Research with Impressive Achievements and New Future Prospects

Journal of medicinal chemistry, 2022-01, Vol.65 (2), p.891-892

2022

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Comprehensive analysis of R-groups in medicinal chemistry

Future medicinal chemistry, 2022-01, Vol.14 (1), p.5-7

Bajorath, Jürgen

2022

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Anatomy of Potency Predictions Focusing on Structural Analogues with Increasing Potency Differences Including Activity Cliffs

Journal of chemical information and modeling, 2023-11, Vol.63 (22), p.7032-7044

2023

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Explainable Machine Learning for Property Predictions in Compound Optimization

Journal of medicinal chemistry, 2021-12, Vol.64 (24), p.17744-17752

2021

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Systematic assessment of structure-promiscuity relationships between different types of kinase inhibitors

Bioorganic & medicinal chemistry, 2021-07, Vol.41, p.116226-116226, Article 116226

2021

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

Future science OA, 2021-07, Vol.7 (6), p.FSO702-FSO702

Bajorath, Jürgen

2021

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Docking and scoring in virtual screening for drug discovery: methods and applications

Nature reviews. Drug discovery, 2004-11, Vol.3 (11), p.935-949

2004

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Charting Biologically Relevant Spirocyclic Compound Space

Chemistry : a European journal, 2017-01, Vol.23 (3), p.703-710

2017

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Artificial Intelligence in Drug Discovery: Into the Great Wide Open

Journal of medicinal chemistry, 2020-08, Vol.63 (16), p.8651-8652

2020

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

RSC medicinal chemistry, 2020-01, Vol.11 (1), p.132-141

2020

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

Journal of medicinal chemistry, 2020-08, Vol.63 (16), p.8761-8777

2020

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Advances in Activity Cliff Research

Molecular informatics, 2016-05, Vol.35 (5), p.181-191

2016

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Computational analysis, alignment and extension of analogue series from medicinal chemistry

Future science OA, 2022-08, Vol.8 (7), p.FSO804-FSO804

2022

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Analyzing Promiscuity at the Level of Active Compounds and Targets

Molecular informatics, 2016-12, Vol.35 (11-12), p.583-587

Bajorath, Jürgen

2016

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

Journal of computer-aided molecular design, 2014-08, Vol.28 (8), p.795-802

2014

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Modeling Tanimoto Similarity Value Distributions and Predicting Search Results

Molecular informatics, 2017-07, Vol.36 (7), p.1600131-n/a

2017

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Chemoinformatics for drug discovery

Bajorath, Jürgen

1, 2013

Volltextzugriff (PDF)

Searching for Coordinated Activity Cliffs Using Particle Swarm Optimization

Journal of chemical information and modeling, 2012-04, Vol.52 (4), p.927-934

2012

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

Journal of medicinal chemistry, 2017-05, Vol.60 (9), p.3879-3886

2017

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Exploring Activity Cliffs from a Chemoinformatics Perspective

Molecular informatics, 2014-06, Vol.33 (6-7), p.438-442

Bajorath, Jürgen

2014

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Computational Polypharmacology Analysis of the Heat Shock Protein 90 Interactome

Journal of chemical information and modeling, 2015-03, Vol.55 (3), p.676-686

2015

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A Data Mining Method To Facilitate SAR Transfer

Journal of chemical information and modeling, 2011-08, Vol.51 (8), p.1857-1866

2011

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Designing highly potent compounds using a chemical language model

Scientific reports, 2023-05, Vol.13 (1), p.7412-7412, Article 7412

2023

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Application of Generative Autoencoder in De Novo Molecular Design

Molecular informatics, 2018-01, Vol.37 (1-2), p.n/a

2018

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Systematic Assessment of Molecular Selectivity at the Level of Targets, Bioactive Compounds, and Structural Analogues

ChemMedChem, 2016-06, Vol.11 (12), p.1362-1370

2016

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New Horizons in Drug Discovery - Understanding and Advancing Different Types of Kinase Inhibitors: Seven Years in Kinase Inhibitor Research with Impressive Achievements and New Future Prospects

Comprehensive analysis of R-groups in medicinal chemistry

Anatomy of Potency Predictions Focusing on Structural Analogues with Increasing Potency Differences Including Activity Cliffs

Explainable Machine Learning for Property Predictions in Compound Optimization

Systematic assessment of structure-promiscuity relationships between different types of kinase inhibitors

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

Docking and scoring in virtual screening for drug discovery: methods and applications

Charting Biologically Relevant Spirocyclic Compound Space

Artificial Intelligence in Drug Discovery: Into the Great Wide Open

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

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

Advances in Activity Cliff Research

Computational analysis, alignment and extension of analogue series from medicinal chemistry

Analyzing Promiscuity at the Level of Active Compounds and Targets

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

Modeling Tanimoto Similarity Value Distributions and Predicting Search Results

Chemoinformatics for drug discovery

Searching for Coordinated Activity Cliffs Using Particle Swarm Optimization

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

Exploring Activity Cliffs from a Chemoinformatics Perspective

Computational Polypharmacology Analysis of the Heat Shock Protein 90 Interactome

A Data Mining Method To Facilitate SAR Transfer

Designing highly potent compounds using a chemical language model

Application of Generative Autoencoder in De Novo Molecular Design

Systematic Assessment of Molecular Selectivity at the Level of Targets, Bioactive Compounds, and Structural Analogues

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