Marwin H. S. Segler

According to our database, Marwin H. S. Segler authored at least 25 papers between 2016 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2023

PREFER: A New Predictive Modeling Framework for Molecular Discovery.

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

J. Chem. Inf. Model., August, 2023

Re-evaluating Retrosynthesis Algorithms with Syntheseus.

Krzysztof Maziarz, Austin Tripp, Guoqing Liu, Megan Stanley, Shufang Xie, Piotr Gainski, Philipp Seidl, Marwin H. S. Segler

CoRR, 2023

Retro-fallback: retrosynthetic planning in an uncertain world.

Austin Tripp, Krzysztof Maziarz, Sarah Lewis, Marwin H. S. Segler, José Miguel Hernández-Lobato

CoRR, 2023

Are VAEs Bad at Reconstructing Molecular Graphs?

Hagen Muenkler, Hubert Misztela, Michal Pikusa, Marwin H. S. Segler, Nadine Schneider, Krzysztof Maziarz

CoRR, 2023

Retrosynthetic Planning with Dual Value Networks.

Guoqing Liu, Di Xue, Shufang Xie, Yingce Xia, Austin Tripp, Krzysztof Maziarz, Marwin H. S. Segler, Tao Qin, Zongzhang Zhang, Tie-Yan Liu

Proceedings of the International Conference on Machine Learning, 2023

2022

Improving Few- and Zero-Shot Reaction Template Prediction Using Modern Hopfield Networks.

Philipp Seidl, Philipp Renz, Natalia Dyubankova, Paulo Neves, Jonas Verhoeven, Jörg K. Wegner, Marwin H. S. Segler, Sepp Hochreiter, Günter Klambauer

J. Chem. Inf. Model., 2022

RetroGNN: Fast Estimation of Synthesizability for Virtual Screening and De Novo Design by Learning from Slow Retrosynthesis Software.

Cheng-Hao Liu, Maksym Korablyov, Stanislaw Jastrzebski, Pawel Wlodarczyk-Pruszynski, Yoshua Bengio, Marwin H. S. Segler

J. Chem. Inf. Model., 2022

Learning to Extend Molecular Scaffolds with Structural Motifs.

Krzysztof Maziarz, Henry Richard Jackson-Flux, Pashmina Cameron, Finton Sirockin, Nadine Schneider, Nikolaus Stiefl, Marwin H. S. Segler, Marc Brockschmidt

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

FS-Mol: A Few-Shot Learning Dataset of Molecules.

Megan Stanley, John Bronskill, Krzysztof Maziarz, Hubert Misztela, Jessica Lanini, Marwin H. S. Segler, Nadine Schneider, Marc Brockschmidt

Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1, 2021

2020

Molecular representation learning with language models and domain-relevant auxiliary tasks.

Benedek Fabian, Thomas Edlich, Héléna Gaspar, Marwin H. S. Segler, Joshua Meyers, Marco Fiscato, Mohamed Ahmed

CoRR, 2020

RetroGNN: Approximating Retrosynthesis by Graph Neural Networks for De Novo Drug Design.

Cheng-Hao Liu, Maksym Korablyov, Stanislaw Jastrzebski, Pawel Wlodarczyk-Pruszynski, Yoshua Bengio, Marwin H. S. Segler

CoRR, 2020

Barking up the right tree: an approach to search over molecule synthesis DAGs.

John Bradshaw, Brooks Paige, Matt J. Kusner, Marwin H. S. Segler, José Miguel Hernández-Lobato

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

2019

GuacaMol: Benchmarking Models for de Novo Molecular Design.

Nathan Brown, Marco Fiscato, Marwin H. S. Segler, Alain C. Vaucher

J. Chem. Inf. Model., 2019

World Programs for Model-Based Learning and Planning in Compositional State and Action Spaces.

Marwin H. S. Segler

CoRR, 2019

A Model to Search for Synthesizable Molecules.

John Bradshaw, Brooks Paige, Matt J. Kusner, Marwin H. S. Segler, José Miguel Hernández-Lobato

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Generating Molecules via Chemical Reactions.

John Bradshaw, Matt J. Kusner, Brooks Paige, Marwin H. S. Segler, José Miguel Hernández-Lobato

Proceedings of the Deep Generative Models for Highly Structured Data, 2019

A Generative Model For Electron Paths.

John Bradshaw, Matt J. Kusner, Brooks Paige, Marwin H. S. Segler, José Miguel Hernández-Lobato

Proceedings of the 7th International Conference on Learning Representations, 2019

2018

Planning chemical syntheses with deep neural networks and symbolic AI.

Marwin H. S. Segler, Mike Preuss, Mark P. Waller

Nat., 2018

DEFactor: Differentiable Edge Factorization-based Probabilistic Graph Generation.

Rim Assouel, Mohamed Ahmed, Marwin H. S. Segler, Amir Saffari, Yoshua Bengio

CoRR, 2018

Predicting Electron Paths.

John Bradshaw, Matt J. Kusner, Brooks Paige, Marwin H. S. Segler, José Miguel Hernández-Lobato

CoRR, 2018

Exploring Deep Recurrent Models with Reinforcement Learning for Molecule Design.

Daniel Neil, Marwin H. S. Segler, Laura Guasch, Mohamed Ahmed, Dean Plumbley, Matthew Sellwood, Nathan Brown

Proceedings of the 6th International Conference on Learning Representations, 2018

2017

Learning to Plan Chemical Syntheses.

Marwin H. S. Segler, Mike Preuss, Mark P. Waller

CoRR, 2017

Generating Focussed Molecule Libraries for Drug Discovery with Recurrent Neural Networks.

Marwin H. S. Segler, Thierry Kogej, Christian Tyrchan, Mark P. Waller

CoRR, 2017

Towards "AlphaChem": Chemical Synthesis Planning with Tree Search and Deep Neural Network Policies.

Marwin H. S. Segler, Mike Preuss, Mark P. Waller

Proceedings of the 5th International Conference on Learning Representations, 2017

2016

Modelling Chemical Reasoning to Predict Reactions.

Marwin H. S. Segler, Mark P. Waller

CoRR, 2016