Thomas Blaschke

Computer Science · Physical Sciences

14h-index4.5kcitations30works1.02yr avg
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Research Topics

Computational Drug Discovery Methods(25), Machine Learning in Materials Science(22), Protein Structure and Dynamics(5), Genetics, Bioinformatics, and Biomedical Research(5), Microbial Natural Products and Biosynthesis(3)

Publications30 total

Deep Learning Meets OBIA: Tasks, Challenges, Strategies, and Perspectives
arXiv (Cornell University)·2024Open Access
Faster and more diverse de novo molecular optimization with double-loop reinforcement learning using augmented SMILES
Journal of Computer-Aided Molecular Design·2023· 18 cited
Fine-tuning of a generative neural network for designing multi-target compounds
Journal of Computer-Aided Molecular Design·2021· 30 citedOpen Access
Compound Dataset and Custom Code for Deep Generative multi-target Compound Design
Future Science OA·2021· 6 citedOpen Access
Memory-assisted reinforcement learning for diverse molecular de novo design
Journal of Cheminformatics·2020· 121 citedOpen Access
Memory-assisted reinforcement learning for diverse molecular de novo design
Research Square (Research Square)·2020· 7 citedOpen Access
REINVENT 2.0: An AI Tool for De Novo Drug Design
Journal of Chemical Information and Modeling·2020· 416 cited
Prediction of Promiscuity Cliffs Using Machine Learning
Molecular Informatics·2020· 9 citedOpen Access
REINVENT 2.0 – an AI Tool for De Novo Drug Design
2020· 1 citedOpen Access
Memory-assisted Reinforcement Learning for Diverse Molecular De Novo Design
Research Square (Research Square)·2020· 6 citedOpen Access
Memory-Assisted Reinforcement Learning for Diverse Molecular De Novo Design
2020· 1 citedOpen Access
Memory-Assisted Reinforcement Learning for Diverse Molecular De Novo Design
2020· 5 citedOpen Access
REINVENT 2.0 – an AI Tool for De Novo Drug Design
2020· 14 citedOpen Access
REINVENT 2.0 – an AI Tool for De Novo Drug Design
2020· 13 citedOpen Access
REINVENT 2.0 – an AI Tool for De Novo Drug Design
2020· 53 citedOpen Access
Chapter 10. Compound Design Using Generative Neural Networks
RSC drug discovery/RSC drug discovery series·2020
Prediction of Different Classes of Promiscuous and Nonpromiscuous Compounds Using Machine Learning and Nearest Neighbor Analysis
ACS Omega·2019· 27 citedOpen Access
Exploring the GDB-13 chemical space using deep generative models
Journal of Cheminformatics·2019· 193 citedOpen Access
Development of Chromen-4-one Derivatives as (Ant)agonists for the Lipid-Activated G Protein-Coupled Receptor GPR55 with Tunable Efficacy
ACS Omega·2019· 12 citedOpen Access
Machine Learning Distinguishes with High Accuracy between Pan-Assay Interference Compounds That Are Promiscuous or Represent Dark Chemical Matter
Journal of Medicinal Chemistry·2018· 32 cited
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Frequent Co-authors

Ola Engkvist(20), Hongming Chen(18), Jürgen Bajorath(14), Josep Arús‐Pous(9), Christian Margreitter(6), Christian Tyrchan(6), Marcus Olivecrona(5), Kostas Papadopoulos(5), Atanas Patronov(5), Jean‐Louis Reymond(4), Silas Ulander(3), Hongming Chen(2), Swarit Jasial(2), Erik Gilberg(2), Yinhai Wang(1), Filip Miljković(1), Esben Jannik Bjerrum(1), Simona Kolarova(1), Raquel López-Ríos de Castro(1), Clara T. Schoeder(1)