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2020 – today
- 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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