Marwin H. S. Segler

Pawel Wlodarczyk-Pruszynski

Proceedings of the Twelfth International Conference on Learning Representations, 2024

RetroBridge: Modeling Retrosynthesis with Markov Bridges.

[BibT_eX]

[DOI]

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

PREFER: A New Predictive Modeling Framework for Molecular Discovery.

[BibT_eX]

[DOI]

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

Re-evaluating Retrosynthesis Algorithms with Syntheseus.

[BibT_eX]

[DOI]

CoRR, 2023

Are VAEs Bad at Reconstructing Molecular Graphs?

[BibT_eX]

[DOI]

CoRR, 2023

Retrosynthetic Planning with Dual Value Networks.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Machine Learning, 2023

2022

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

[BibT_eX]

[DOI]

J. Chem. Inf. Model., 2022

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

[BibT_eX]

[DOI]

Cheng-Hao Liu

Maksym Korablyov

Stanislaw Jastrzebski

Yoshua Bengio

Henry Richard Jackson-Flux

J. Chem. Inf. Model., 2022

Learning to Extend Molecular Scaffolds with Structural Motifs.

[BibT_eX]

[DOI]

Krzysztof Maziarz

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

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

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

CoRR, 2020

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

[BibT_eX]

[DOI]

Cheng-Hao Liu

Maksym Korablyov

Stanislaw Jastrzebski

Pawel Wlodarczyk-Pruszynski

Yoshua Bengio

CoRR, 2020

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

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

J. Chem. Inf. Model., 2019

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

[BibT_eX]

[DOI]

CoRR, 2019

A Model to Search for Synthesizable Molecules.

[BibT_eX]

[DOI]

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

Generating Molecules via Chemical Reactions.

[BibT_eX]

[DOI]

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

A Generative Model For Electron Paths.

[BibT_eX]

[DOI]

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

2018

Planning chemical syntheses with deep neural networks and symbolic AI.

[BibT_eX]

[DOI]

Mike Preuss

Nat., 2018

DEFactor: Differentiable Edge Factorization-based Probabilistic Graph Generation.

[BibT_eX]

[DOI]

CoRR, 2018

Predicting Electron Paths.

[BibT_eX]

[DOI]

CoRR, 2018

Exploring Deep Recurrent Models with Reinforcement Learning for Molecule Design.

[BibT_eX]

[DOI]

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

2017

Learning to Plan Chemical Syntheses.

[BibT_eX]

[DOI]

Mike Preuss

CoRR, 2017

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

[BibT_eX]

[DOI]

CoRR, 2017

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

[BibT_eX]

[DOI]

Mike Preuss

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

2016

Modelling Chemical Reasoning to Predict Reactions.

[BibT_eX]

[DOI]