Jürgen Bajorath

Computational Theory and Mathematics · 2002–2020

A researcher in the Science corpus: 15 papers; trusts 26 researchers and is trusted by 46 researchers here, as inferred from the citation graph.

Subfields

• Computational Theory and Mathematics — 156 citations into it; #822 of 12,182 by trust here, #182 by citations
• Molecular Biology — 109 citations into it; #920 of 17,773 by trust here, #1,941 by citations
• Materials Chemistry — 88 citations into it
• Computational Chemistry & Drug Discovery — 80 citations into it; #113 of 425 by trust here, #98 by citations
• Pharmacology — 37 citations into it
• Artificial Intelligence — 31 citations into it
• Spectroscopy — 30 citations into it
• Organic Chemistry — 19 citations into it

Whom Jürgen Bajorath trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Robert P. Sheridan
2. Lloyd S. Shapley
3. Irwin D. Kuntz
4. Tudor I. Oprea
5. Scott M. Lundberg
6. Bradley Efron
7. Diederik P. Kingma
8. Martin J. Osborne
9. Mark A. Murcko
10. Trevor J. Hastie

Who trusts Jürgen Bajorath overall

1. Kalyan Immadisetty
2. Lars Rosenbaum
3. Mukund Deshpande
4. M. Hann
5. Antonio Lavecchia
6. Laura M. Geffert
7. Georg Hinselmann
8. Sean Ekins
9. Gregory Sliwoski
10. Hanna Eckert

Papers from Jürgen Bajorath's perspective

Ordered by how strongly the researchers Jürgen Bajorath trusts endorse each paper. Same corpus, a different order for every researcher.

1. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
2. Long Short-Term Memory
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. ImageNet classification with deep convolutional neural networks
5. Gradient-based learning applied to document recognition
6. Auto-Encoding Variational Bayes (International Conference on Learning Representations)
7. Attention is All you Need (Neural Information Processing Systems)
8. Learning Multiple Layers of Features from Tiny Images
9. ImageNet: A large-scale hierarchical image database
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Docking and scoring in virtual screening for drug discovery: methods and applications (2004) — 3,738 citations
• Application of Generative Autoencoder in De Novo Molecular Design (2017) — 375 citations
• Integration of virtual and high-throughput screening (2002) — 837 citations
• Current Trends in Ligand-Based Virtual Screening: Molecular Representations, Data Mining Methods, New Application Areas, and Performance Evaluation (2010) — 305 citations
• Molecular similarity analysis in virtual screening: foundations, limitations and novel approaches. (2007) — 432 citations
• QSAR without borders (2020) — 546 citations
• Virtual screening methods that complement HTS. (2004) — 108 citations
• Molecular similarity in medicinal chemistry. (2014) — 576 citations
• Interpretation of machine learning models using shapley values: application to compound potency and multi-target activity predictions (2020) — 617 citations
• State-of-the-art in ligand-based virtual screening. (2011) — 226 citations
• Polypharmacology: challenges and opportunities in drug discovery. (2014) — 932 citations
• Interpretation of Compound Activity Predictions from Complex Machine Learning Models Using Local Approximations and Shapley Values. (2020) — 428 citations
• Large-scale exploration of bioisosteric replacements on the basis of matched molecular pairs. (2011) — 50 citations
• Rational Development of LEA29Y (belatacept), a High‐Affinity Variant of CTLA4‐Ig with Potent Immunosuppressive Properties (2005) — 711 citations
• B7-H4, a molecule of the B7 family, negatively regulates T cell immunity. (2003) — 724 citations