Segler, Marwin H. S.

Opportunities and obstacles for deep learning in biology and medicine

Ching, T., Himmelstein, D. S., Beaulieu-Jones, B. K., Kalinin, A. A., Do, B. T., Way, G. P., Ferrero, E., Agapow, P.-M., Zietz, M., Hoffman, M. M., et al. (2018) · Journal of The Royal Society Interface

Machine Learning in HealthcareArtificial Intelligence in Healthcare and Education

Cited by 2,310

Planning chemical syntheses with deep neural networks and symbolic AI

Segler, M. H. S., Preuss, M., & Waller, M. P. (2018) · Nature

Computational Drug Discovery MethodsAI-based Problem Solving and Planning

Cited by 1,847

Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks

Segler, M. H. S., Kogej, T., Tyrchan, C., & Waller, M. P. (2018) · ACS Central Science

Computational Drug Discovery MethodsMachine Learning in Materials Science

Cited by 1,537

GuacaMol: Benchmarking Models for de Novo Molecular Design

Brown, N., Fiscato, M., Segler, M. H. S., & Vaucher, A. C. (2019) · Journal of Chemical Information and Modeling

Computational Drug Discovery MethodsMachine Learning in Materials Science

Cited by 895

Neural‐Symbolic Machine Learning for Retrosynthesis and Reaction Prediction

Segler, M. H. S., & Waller, M. P. (2017) · Chemistry – A European Journal

Computational Drug Discovery MethodsMachine Learning in Materials Science

Cited by 548