Följ
Thomas Blaschke
Thomas Blaschke
Phd student, AstraZeneca/University of Bonn
Verifierad e-postadress på uni-bonn.de
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The rise of deep learning in drug discovery
H Chen, O Engkvist, Y Wang, M Olivecrona, T Blaschke
Drug discovery today 23 (6), 1241-1250, 2018
10942018
Molecular de-novo design through deep reinforcement learning
M Olivecrona, T Blaschke, O Engkvist, H Chen
Journal of cheminformatics 9 (1), 1-14, 2017
7032017
Application of Generative Autoencoder in De Novo Molecular Design
T Blaschke, M Olivecrona, O Engkvist, J Bajorath, H Chen
Molecular informatics 37 (1-2), 1700123, 2018
3112018
Exploring the GDB-13 chemical space using deep generative models
J Arús-Pous, T Blaschke, S Ulander, JL Reymond, H Chen, O Engkvist
Journal of cheminformatics 11 (1), 1-14, 2019
1172019
REINVENT 2.0: an AI tool for de novo drug design
T Blaschke, J Arús-Pous, H Chen, C Margreitter, C Tyrchan, O Engkvist, ...
Journal of chemical information and modeling 60 (12), 5918-5922, 2020
1072020
Memory-assisted reinforcement learning for diverse molecular de novo design
T Blaschke, O Engkvist, J Bajorath, H Chen
Journal of cheminformatics 12 (1), 1-17, 2020
452020
Machine learning distinguishes with high accuracy between pan-assay interference compounds that are promiscuous or represent dark chemical matter
S Jasial, E Gilberg, T Blaschke, J Bajorath
Journal of Medicinal Chemistry 61 (22), 10255-10264, 2018
232018
Prediction of different classes of promiscuous and nonpromiscuous compounds using machine learning and nearest neighbor analysis
T Blaschke, F Miljkovic, J Bajorath
ACS Omega 4 (4), 6883-6890, 2019
162019
Exploring the GDB-13 chemical space using deep generative models. J Cheminform 11: 20
J Arús-Pous, T Blaschke, S Ulander, JL Reymond, H Chen, O Engkvist
132019
Development of Chromen-4-one Derivatives as (Ant) agonists for the Lipid-Activated G Protein-Coupled Receptor GPR55 with Tunable Efficacy
CT Schoeder, A Meyer, AB Mahardhika, D Thimm, T Blaschke, M Funke, ...
ACS Omega 4 (2), 4276-4295, 2019
62019
Fine-tuning of a generative neural network for designing multi-target compounds
T Blaschke, J Bajorath
Journal of Computer-Aided Molecular Design 36 (5), 363-371, 2022
32022
Compound dataset and custom code for deep generative multi-target compound design
T Blaschke, J Bajorath
Future Science OA 7 (6), FSO715, 2021
32021
Prediction of promiscuity cliffs using machine learning
T Blaschke, C Feldmann, J Bajorath
Molecular Informatics 40 (1), 2000196, 2021
22021
Compound Design Using Generative Neural Networks
T Blaschke, J Bajorath
Artificial Intelligence in Drug Discovery 75, 217, 2020
12020
Faster and more diverse de novo molecular optimization with double-loop reinforcement learning using augmented SMILES
EJ Bjerrum, C Margreitter, T Blaschke, RLR de Castro
arXiv preprint arXiv:2210.12458, 2022
2022
Faster and more diverse de novo molecular optimization with double-loop reinforcement learning using augmented SMILES
E Jannik Bjerrum, C Margreitter, T Blaschke, R Lopez-Rios de Castro
arXiv e-prints, arXiv: 2210.12458, 2022
2022
Exploring the GDB-13 chemical space using deep generative models
J Arús-Pous, T Blaschke, S Ulander, JL Reymond, H Chen, O Engkvist
Journal of cheminformatics 11 (1), 1-14, 2019
2019
De novo molecular design using deep reinforcement learning methods
HM Chen, M Olivercrona, T Blaschke, O Engkvist, T Kogej, C Tyrchan
Abstracts of Papers of the American Chemical Society, 2018
2018
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Artiklar 1–18