Marwin Segler
Marwin Segler
Microsoft Research
Verified email at microsoft.com
Title
Cited by
Cited by
Year
Opportunities and obstacles for deep learning in biology and medicine
T Ching, DS Himmelstein, BK Beaulieu-Jones, AA Kalinin, BT Do, ...
Journal of The Royal Society Interface 15 (141), 20170387, 2018
10032018
Planning chemical syntheses with deep neural networks and symbolic AI
MHS Segler, M Preuss, MP Waller
Nature 555 (7698), 604-610, 2018
8122018
Generating focused molecule libraries for drug discovery with recurrent neural networks
MHS Segler, T Kogej, C Tyrchan, MP Waller
ACS central science 4 (1), 120-131, 2018
6802018
Neural‐symbolic machine learning for retrosynthesis and reaction prediction
MHS Segler, MP Waller
Chemistry–A European Journal 23 (25), 5966-5971, 2017
2742017
GuacaMol: benchmarking models for de novo molecular design
N Brown, M Fiscato, MHS Segler, AC Vaucher
Journal of chemical information and modeling 59 (3), 1096-1108, 2019
2182019
Modelling Chemical Reasoning to Predict and Invent Reactions
MHS Segler, MP Waller
Chemistry - A European Journal 23 (25), 6118-6128, 2016
139*2016
Artificial intelligence in drug discovery
MA Sellwood, M Ahmed, MHS Segler, N Brown
Future medicinal chemistry 10 (17), 2025-2028, 2018
492018
A model to search for synthesizable molecules
J Bradshaw, B Paige, MJ Kusner, MHS Segler, JM Hernández-Lobato
arXiv preprint arXiv:1906.05221, 2019
432019
Exploring deep recurrent models with reinforcement learning for molecule design
D Neil, M Segler, L Guasch, M Ahmed, D Plumbley, M Sellwood, N Brown
412018
A generative model for electron paths
J Bradshaw, MJ Kusner, B Paige, MHS Segler, JM Hernández-Lobato
arXiv preprint arXiv:1805.10970, 2018
38*2018
Machine learning the ropes: principles, applications and directions in synthetic chemistry
F Strieth-Kalthoff, F Sandfort, MHS Segler, F Glorius
Chemical Society Reviews 49 (17), 6154-6168, 2020
372020
Defactor: Differentiable edge factorization-based probabilistic graph generation
R Assouel, M Ahmed, MH Segler, A Saffari, Y Bengio
arXiv preprint arXiv:1811.09766, 2018
282018
Towards" alphachem": Chemical synthesis planning with tree search and deep neural network policies
M Segler, M Preuß, MP Waller
arXiv preprint arXiv:1702.00020, 2017
232017
Silver-catalyzed 1, 3-dipolar cycloaddition of azomethine ylides with β-boryl acrylates
A Lopez-Perez, M Segler, J Adrio, JC Carretero
The Journal of organic chemistry 76 (6), 1945-1948, 2011
232011
Learning to plan chemical syntheses
MHS Segler, M Preuss, MP Waller
arXiv preprint arXiv:1708.04202, 2017
212017
Dehydrogenative TEMPO‐Mediated Formation of Unstable Nitrones: Easy Access to N‐Carbamoyl Isoxazolines
A Gini, M Segler, D Kellner, OG Mancheño
Chemistry–A European Journal 21 (34), 12053-12060, 2015
202015
Molecular representation learning with language models and domain-relevant auxiliary tasks
B Fabian, T Edlich, H Gaspar, M Segler, J Meyers, M Fiscato, M Ahmed
arXiv preprint arXiv:2011.13230, 2020
112020
Retrognn: Approximating retrosynthesis by graph neural networks for de novo drug design
CH Liu, M Korablyov, S Jastrzębski, P Włodarczyk-Pruszyński, Y Bengio, ...
arXiv preprint arXiv:2011.13042, 2020
62020
Barking up the right tree: an approach to search over molecule synthesis dags
J Bradshaw, B Paige, MJ Kusner, MHS Segler, JM Hernández-Lobato
arXiv preprint arXiv:2012.11522, 2020
52020
Opportunities and obstacles for deep learning in biology and medicine. JR Soc Interface. 2018 Apr; 15 (141) doi: 10.1098/rsif. 2017.0387
T Ching, DS Himmelstein, BK Beaulieu-Jones, AA Kalinin, BT Do, ...
5
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