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ORCIDMarwin H. S. Segler
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2020 – today
- 2024
[c11]Ilia Igashov, Arne Schneuing, Marwin H. S. Segler, Michael M. Bronstein, Bruno E. Correia:
RetroBridge: Modeling Retrosynthesis with Markov Bridges. ICLR 2024- [c10]Austin Tripp, Krzysztof Maziarz, Sarah Lewis, Marwin H. S. Segler, José Miguel Hernández-Lobato:
Retro-fallback: retrosynthetic planning in an uncertain world. ICLR 2024 - [i19]Maksym Korablyov, Cheng-Hao Liu, Moksh Jain, Almer M. van der Sloot
, Eric Jolicoeur, Edward Ruediger, Andrei Cristian Nica, Emmanuel Bengio, Kostiantyn Lapchevskyi, Daniel St-Cyr, Doris Alexandra Schuetz, Victor Ion Butoi, Jarrid Rector-Brooks, Simon Blackburn, Leo Feng, Hadi Nekoei, Sai Krishna Gottipati, Priyesh Vijayan, Prateek Gupta, Ladislav Rampásek, Sasikanth Avancha, Pierre-Luc Bacon, William L. Hamilton, Brooks Paige, Sanchit Misra, Stanislaw Kamil Jastrzebski, Bharat Kaul, Doina Precup, José Miguel Hernández-Lobato, Marwin H. S. Segler, Michael M. Bronstein, Anne Marinier, Mike Tyers, Yoshua Bengio:
Generative Active Learning for the Search of Small-molecule Protein Binders. CoRR abs/2405.01616 (2024) - [i18]Piotr Gainski, Michal Koziarski, Krzysztof Maziarz, Marwin H. S. Segler, Jacek Tabor, Marek Smieja:
RetroGFN: Diverse and Feasible Retrosynthesis using GFlowNets. CoRR abs/2406.18739 (2024) - 2023
- [j5]Jessica Lanini, Gianluca Santarossa, Finton Sirockin, Richard A. Lewis, Nikolas Fechner, Hubert Misztela, Sarah Lewis, Krzysztof Maziarz, Megan Stanley, Marwin H. S. Segler, Nikolaus Stiefl, Nadine Schneider:
PREFER: A New Predictive Modeling Framework for Molecular Discovery. J. Chem. Inf. Model. 63(15): 4497-4504 (2023) - [c9]Guoqing Liu, Di Xue, Shufang Xie, Yingce Xia, Austin Tripp, Krzysztof Maziarz, Marwin H. S. Segler, Tao Qin, Zongzhang Zhang, Tie-Yan Liu:
Retrosynthetic Planning with Dual Value Networks. ICML 2023: 22266-22276 - [i17]Guoqing Liu, Di Xue, Shufang Xie, Yingce Xia, Austin Tripp, Krzysztof Maziarz, Marwin H. S. Segler, Tao Qin, Zongzhang Zhang, Tie-Yan Liu:
Retrosynthetic Planning with Dual Value Networks. CoRR abs/2301.13755 (2023) - [i16]Hagen Muenkler, Hubert Misztela, Michal Pikusa, Marwin H. S. Segler, Nadine Schneider, Krzysztof Maziarz:
Are VAEs Bad at Reconstructing Molecular Graphs? CoRR abs/2305.03041 (2023) - [i15]Ilia Igashov, Arne Schneuing, Marwin H. S. Segler, Michael M. Bronstein, Bruno E. Correia:
RetroBridge: Modeling Retrosynthesis with Markov Bridges. CoRR abs/2308.16212 (2023) - [i14]Austin Tripp, Krzysztof Maziarz, Sarah Lewis, Marwin H. S. Segler, José Miguel Hernández-Lobato:
Retro-fallback: retrosynthetic planning in an uncertain world. CoRR abs/2310.09270 (2023) - [i13]Krzysztof Maziarz, Austin Tripp, Guoqing Liu, Megan Stanley, Shufang Xie, Piotr Gainski, Philipp Seidl, Marwin H. S. Segler:
Re-evaluating Retrosynthesis Algorithms with Syntheseus. CoRR abs/2310.19796 (2023) - 2022
- [j4]Philipp Seidl, Philipp Renz, Natalia Dyubankova, Paulo Neves, Jonas Verhoeven, Jörg K. Wegner, Marwin H. S. Segler, Sepp Hochreiter, Günter Klambauer:
Improving Few- and Zero-Shot Reaction Template Prediction Using Modern Hopfield Networks. J. Chem. Inf. Model. 62(9): 2111-2120 (2022) - [j3]Cheng-Hao Liu, Maksym Korablyov, Stanislaw Jastrzebski, Pawel Wlodarczyk-Pruszynski, Yoshua Bengio, Marwin H. S. Segler:
RetroGNN: Fast Estimation of Synthesizability for Virtual Screening and De Novo Design by Learning from Slow Retrosynthesis Software. J. Chem. Inf. Model. 62(10): 2293-2300 (2022) - [c8]Krzysztof Maziarz, Henry Richard Jackson-Flux, Pashmina Cameron, Finton Sirockin, Nadine Schneider, Nikolaus Stiefl, Marwin H. S. Segler, Marc Brockschmidt:
Learning to Extend Molecular Scaffolds with Structural Motifs. ICLR 2022 - 2021
- [c7]Megan Stanley, John Bronskill, Krzysztof Maziarz, Hubert Misztela, Jessica Lanini, Marwin H. S. Segler, Nadine Schneider, Marc Brockschmidt:
FS-Mol: A Few-Shot Learning Dataset of Molecules. NeurIPS Datasets and Benchmarks 2021 - 2020
- [c6]John Bradshaw, Brooks Paige, Matt J. Kusner, Marwin H. S. Segler, José Miguel Hernández-Lobato:
Barking up the right tree: an approach to search over molecule synthesis DAGs. NeurIPS 2020 - [i12]Cheng-Hao Liu, Maksym Korablyov, Stanislaw Jastrzebski, Pawel Wlodarczyk-Pruszynski, Yoshua Bengio, Marwin H. S. Segler:
RetroGNN: Approximating Retrosynthesis by Graph Neural Networks for De Novo Drug Design. CoRR abs/2011.13042 (2020) - [i11]Benedek Fabian, Thomas Edlich, Héléna Gaspar, Marwin H. S. Segler, Joshua Meyers, Marco Fiscato, Mohamed Ahmed:
Molecular representation learning with language models and domain-relevant auxiliary tasks. CoRR abs/2011.13230 (2020) - [i10]John Bradshaw, Brooks Paige, Matt J. Kusner, Marwin H. S. Segler, José Miguel Hernández-Lobato:
Barking up the right tree: an approach to search over molecule synthesis DAGs. CoRR abs/2012.11522 (2020)
2010 – 2019
- 2019
- [j2]Nathan Brown, Marco Fiscato, Marwin H. S. Segler, Alain C. Vaucher:
GuacaMol: Benchmarking Models for de Novo Molecular Design. J. Chem. Inf. Model. 59(3): 1096-1108 (2019) - [c5]John Bradshaw, Matt J. Kusner, Brooks Paige, Marwin H. S. Segler, José Miguel Hernández-Lobato:
A Generative Model For Electron Paths. ICLR (Poster) 2019 - [c4]John Bradshaw, Matt J. Kusner, Brooks Paige, Marwin H. S. Segler, José Miguel Hernández-Lobato:
Generating Molecules via Chemical Reactions. DGS@ICLR 2019 - [c3]John Bradshaw, Brooks Paige, Matt J. Kusner, Marwin H. S. Segler, José Miguel Hernández-Lobato:
A Model to Search for Synthesizable Molecules. NeurIPS 2019: 7935-7947 - [i9]John Bradshaw, Brooks Paige, Matt J. Kusner, Marwin H. S. Segler, José Miguel Hernández-Lobato:
A Model to Search for Synthesizable Molecules. CoRR abs/1906.05221 (2019) - [i8]Marwin H. S. Segler:
World Programs for Model-Based Learning and Planning in Compositional State and Action Spaces. CoRR abs/1912.13007 (2019) - 2018
- [j1]Marwin H. S. Segler, Mike Preuss, Mark P. Waller:
Planning chemical syntheses with deep neural networks and symbolic AI. Nat. 555(7698): 604-610 (2018) - [c2]Daniel Neil, Marwin H. S. Segler, Laura Guasch, Mohamed Ahmed, Dean Plumbley, Matthew Sellwood, Nathan Brown:
Exploring Deep Recurrent Models with Reinforcement Learning for Molecule Design. ICLR (Workshop) 2018 - [i7]John Bradshaw, Matt J. Kusner, Brooks Paige, Marwin H. S. Segler, José Miguel Hernández-Lobato:
Predicting Electron Paths. CoRR abs/1805.10970 (2018) - [i6]Nathan Brown, Marco Fiscato, Marwin H. S. Segler, Alain C. Vaucher:
GuacaMol: Benchmarking Models for De Novo Molecular Design. CoRR abs/1811.09621 (2018) - [i5]Rim Assouel, Mohamed Ahmed, Marwin H. S. Segler, Amir Saffari, Yoshua Bengio:
DEFactor: Differentiable Edge Factorization-based Probabilistic Graph Generation. CoRR abs/1811.09766 (2018) - 2017
- [c1]Marwin H. S. Segler, Mike Preuss, Mark P. Waller:
Towards "AlphaChem": Chemical Synthesis Planning with Tree Search and Deep Neural Network Policies. ICLR (Workshop) 2017 - [i4]Marwin H. S. Segler, Thierry Kogej, Christian Tyrchan, Mark P. Waller:
Generating Focussed Molecule Libraries for Drug Discovery with Recurrent Neural Networks. CoRR abs/1701.01329 (2017) - [i3]Marwin H. S. Segler, Mike Preuß, Mark P. Waller:
Towards "AlphaChem": Chemical Synthesis Planning with Tree Search and Deep Neural Network Policies. CoRR abs/1702.00020 (2017) - [i2]Marwin H. S. Segler, Mike Preuss, Mark P. Waller:
Learning to Plan Chemical Syntheses. CoRR abs/1708.04202 (2017) - 2016
- [i1]Marwin H. S. Segler, Mark P. Waller:
Modelling Chemical Reasoning to Predict Reactions. CoRR abs/1608.07117 (2016)
Coauthor Index
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