H-Index & Metrics Best Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Biology and Biochemistry D-index 60 Citations 21,183 344 World Ranking 5303 National Ranking 392

Research.com Recognitions

Awards & Achievements

2015 - Herman Skolnik Award, American Chemical Society (ACS)

Overview

What is he best known for?

The fields of study he is best known for:

  • Enzyme
  • Artificial intelligence
  • Gene

His scientific interests lie mostly in Biochemistry, Virtual screening, Artificial intelligence, Binding site and Computational biology. As a member of one scientific family, Jürgen Bajorath mostly works in the field of Biochemistry, focusing on Stereochemistry and, on occasion, Extracellular. His Virtual screening research integrates issues from Data science and Drug discovery.

His Artificial intelligence research is multidisciplinary, relying on both Machine learning, Data mining and Pattern recognition. His research integrates issues of Plasma protein binding, CD86, Ligand, Molecular biology and CD80 in his study of Binding site. His Molecular biology research incorporates themes from Cell biology and Antigen-presenting cell.

His most cited work include:

  • Docking and scoring in virtual screening for drug discovery: methods and applications. (1922 citations)
  • Human B7-1 (CD80) and B7-2 (CD86) bind with similar avidities but distinct kinetics to CD28 and CTLA-4 receptors (801 citations)
  • The CD40 ligand, gp39, is defective in activated T cells from patients with X-linked hyper-IgM syndrome (778 citations)

What are the main themes of his work throughout his whole career to date?

Jürgen Bajorath spends much of his time researching Artificial intelligence, Computational biology, Virtual screening, Data mining and Combinatorial chemistry. Jürgen Bajorath has researched Artificial intelligence in several fields, including Machine learning and Pattern recognition. His Computational biology study integrates concerns from other disciplines, such as Polypharmacology, Drug discovery, Kinase and Small molecule.

His Data science research extends to the thematically linked field of Drug discovery. His Virtual screening study frequently draws parallels with other fields, such as Cheminformatics. His research links Scaffold with Combinatorial chemistry.

He most often published in these fields:

  • Artificial intelligence (20.56%)
  • Computational biology (19.92%)
  • Virtual screening (13.07%)

What were the highlights of his more recent work (between 2016-2021)?

  • Computational biology (19.92%)
  • Artificial intelligence (20.56%)
  • Drug discovery (10.53%)

In recent papers he was focusing on the following fields of study:

His primary scientific interests are in Computational biology, Artificial intelligence, Drug discovery, Machine learning and Chemical space. His Computational biology study combines topics in areas such as Scaffold and Kinase, Kinome. His Scaffold research is multidisciplinary, incorporating perspectives in Scaffold hopping, Combinatorial chemistry and Nanotechnology.

In his study, Cheminformatics, Feature and Fingerprint is inextricably linked to Pattern recognition, which falls within the broad field of Artificial intelligence. His Drug discovery research includes elements of Data science, Small molecule and Binding site. His Chemical space research incorporates elements of Virtual screening, Visualization, Data mining, Identification and Algorithm.

Between 2016 and 2021, his most popular works were:

  • Application of Generative Autoencoder in De Novo Molecular Design. (139 citations)
  • QSAR without borders (83 citations)
  • Recent Advances in Scaffold Hopping (76 citations)

In his most recent research, the most cited papers focused on:

  • Enzyme
  • Gene
  • Artificial intelligence

His primary scientific interests are in Computational biology, Artificial intelligence, Machine learning, Drug discovery and Promiscuity. His work carried out in the field of Computational biology brings together such families of science as Pan-assay interference compounds, Similarity, Kinome and Bioinformatics. Artificial intelligence is frequently linked to Pattern recognition in his study.

The study incorporates disciplines such as Fingerprint, Feature, Set and Cheminformatics in addition to Pattern recognition. His biological study spans a wide range of topics, including Allosteric regulation and Interpretation. In his study, which falls under the umbrella issue of Drug discovery, Nonspecific binding, Protein family and Ligand is strongly linked to Small molecule.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Best Publications

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

Douglas B. Kitchen;Hélène Decornez;John R. Furr;Jürgen Bajorath.
Nature Reviews Drug Discovery (2004)

3252 Citations

Human B7-1 (CD80) and B7-2 (CD86) bind with similar avidities but distinct kinetics to CD28 and CTLA-4 receptors

Peter S. Linsley;JoAnne L. Greene;William Brady;Jürgen Bajorath.
Immunity (1994)

1092 Citations

Integration of virtual and high-throughput screening.

Jürgen Bajorath;Jürgen Bajorath.
Nature Reviews Drug Discovery (2002)

980 Citations

The CD40 ligand, gp39, is defective in activated T cells from patients with X-linked hyper-IgM syndrome

Alejandro Aruffo;Alejandro Aruffo;Mary Farrington;Diane Hollenbaugh;Xu Li.
Cell (1993)

923 Citations

B7-H4, a molecule of the B7 family, negatively regulates T cell immunity.

Gabriel L. Sica;In Hak Choi;In Hak Choi;Gefeng Zhu;Koji Tamada.
Immunity (2003)

856 Citations

IMMUNE REGULATION BY CD40 AND ITS LIGAND GP39

Teresa M. Foy;Alejandro Aruffo;Jürgen Bajorath;Janet E. Buhlmann.
Annual Review of Immunology (1996)

740 Citations

Polypharmacology: Challenges and Opportunities in Drug Discovery

Andrew Anighoro;Jürgen Bajorath;Giulio Rastelli.
Journal of Medicinal Chemistry (2014)

665 Citations

Rational development of LEA29Y (belatacept), a high-affinity variant of CTLA4-Ig with potent immunosuppressive properties.

Christian P. Larsen;Thomas C. Pearson;Andrew B. Adams;Paul Tso.
American Journal of Transplantation (2005)

654 Citations

Molecular similarity analysis in virtual screening: foundations, limitations and novel approaches.

Hanna Eckert;Jürgen Bajorath.
Drug Discovery Today (2007)

493 Citations

Molecular similarity in medicinal chemistry.

Gerald Maggiora;Martin Vogt;Dagmar Stumpfe;Jürgen Bajorath.
Journal of Medicinal Chemistry (2014)

404 Citations

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