Thomas Blaschke - Concepedia

Author

Thomas Blaschke

University of Bonn ORCID

Also Known As

Blaschke, Thomas, T. Blaschke, Thomas Blaschke

Publications

4.5K

Citations

H-Index

Concepts

All Affiliations

University of Bonn AstraZeneca (Sweden)University of Bern Guangzhou Regenerative Medicine and Health Guangdong Laboratory AstraZeneca (Canada)

About

Thomas Blaschke is an author at University of Bonn specializing in engineering, medicine, and computational biology.

Top Concepts

#	Concept	H-Index	Publications	Citations
1	Engineering	8	8	639
2	Medicine	8	8	631
3	Natural Sciences	2	2	32
4	Biochemistry	2	2	44
5	Chemical Biology	1	1	12

Thomas Blaschke

Publications

2.8K

Citations

H-Index

Page 1

	Year	Citations
Molecular de-novo design through deep reinforcement learning Marcus Olivecrona, Thomas Blaschke, Ola Engkvist, Journal of Cheminformatics	2017	1.3K
REINVENT 2.0: An AI Tool for De Novo Drug Design Thomas Blaschke, Josep Arús‐Pous, Hongming Chen, Journal of Chemical Information and Modeling Artificial IntelligenceDrug TargetEngineeringMolecular BiologyBiological Computing	2020	413
Application of Generative Autoencoder in <i>De Novo</i> Molecular Design Thomas Blaschke, Marcus Olivecrona, Ola Engkvist, Molecular Informatics	2017	396
Exploring the GDB-13 chemical space using deep generative models Josep Arús‐Pous, Thomas Blaschke, Silas Ulander, Journal of Cheminformatics	2019	193
Memory-assisted reinforcement learning for diverse molecular de novo design Thomas Blaschke, Ola Engkvist, Jürgen Bajorath, Journal of Cheminformatics	2020	121
Application of generative autoencoder in de novo molecular design Thomas Blaschke, Marcus Olivecrona, Ola Engkvist, arXiv (Cornell University) EngineeringAutoencodersMolecular BiologyMolecular DesignMolecular Computing	2017	76
REINVENT 2.0 – an AI Tool for De Novo Drug Design Thomas Blaschke, Josep Arús‐Pous, Hongming Chen, Pharmaceutical ResearchDrug TargetEngineeringMolecular BiologySynthetic Circuit	2020	53
Machine Learning Distinguishes with High Accuracy between Pan-Assay Interference Compounds That Are Promiscuous or Represent Dark Chemical Matter Swarit Jasial, Erik Gilberg, Thomas Blaschke, Journal of Medicinal Chemistry EngineeringMachine LearningChemical AnalysisHit IdentificationMachine Learning Tool	2018	32
Fine-tuning of a generative neural network for designing multi-target compounds Thomas Blaschke, Jürgen Bajorath Journal of Computer-Aided Molecular Design	2021	30
Prediction of Different Classes of Promiscuous and Nonpromiscuous Compounds Using Machine Learning and Nearest Neighbor Analysis Thomas Blaschke, Filip Miljković, Jürgen Bajorath ACS Omega	2019	27

Page 1