Marwin H. S. Segler

Orcid: 0000-0001-8008-0546

According to our database1, Marwin H. S. Segler authored at least 28 papers between 2016 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Bibliography

2024
RetroGFN: Diverse and Feasible Retrosynthesis using GFlowNets.
CoRR, 2024

Generative Active Learning for the Search of Small-molecule Protein Binders.
CoRR, 2024

Retro-fallback: retrosynthetic planning in an uncertain world.
Proceedings of the Twelfth International Conference on Learning Representations, 2024

RetroBridge: Modeling Retrosynthesis with Markov Bridges.
Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023
PREFER: A New Predictive Modeling Framework for Molecular Discovery.
J. Chem. Inf. Model., August, 2023

Re-evaluating Retrosynthesis Algorithms with Syntheseus.
CoRR, 2023

Are VAEs Bad at Reconstructing Molecular Graphs?
CoRR, 2023

Retrosynthetic Planning with Dual Value Networks.
Proceedings of the International Conference on Machine Learning, 2023

2022
Improving Few- and Zero-Shot Reaction Template Prediction Using Modern Hopfield Networks.
J. Chem. Inf. Model., 2022

RetroGNN: Fast Estimation of Synthesizability for Virtual Screening and De Novo Design by Learning from Slow Retrosynthesis Software.
J. Chem. Inf. Model., 2022

Learning to Extend Molecular Scaffolds with Structural Motifs.
Proceedings of the Tenth International Conference on Learning Representations, 2022

2021
FS-Mol: A Few-Shot Learning Dataset of Molecules.
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.
CoRR, 2020

RetroGNN: Approximating Retrosynthesis by Graph Neural Networks for De Novo Drug Design.
CoRR, 2020

Barking up the right tree: an approach to search over molecule synthesis DAGs.
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.
J. Chem. Inf. Model., 2019

World Programs for Model-Based Learning and Planning in Compositional State and Action Spaces.
CoRR, 2019

A Model to Search for Synthesizable Molecules.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Generating Molecules via Chemical Reactions.
Proceedings of the Deep Generative Models for Highly Structured Data, 2019

A Generative Model For Electron Paths.
Proceedings of the 7th International Conference on Learning Representations, 2019

2018
Planning chemical syntheses with deep neural networks and symbolic AI.
Nat., 2018

DEFactor: Differentiable Edge Factorization-based Probabilistic Graph Generation.
CoRR, 2018

Predicting Electron Paths.
CoRR, 2018

Exploring Deep Recurrent Models with Reinforcement Learning for Molecule Design.
Proceedings of the 6th International Conference on Learning Representations, 2018

2017
Learning to Plan Chemical Syntheses.
CoRR, 2017

Generating Focussed Molecule Libraries for Drug Discovery with Recurrent Neural Networks.
CoRR, 2017

Towards "AlphaChem": Chemical Synthesis Planning with Tree Search and Deep Neural Network Policies.
Proceedings of the 5th International Conference on Learning Representations, 2017

2016
Modelling Chemical Reasoning to Predict Reactions.
CoRR, 2016


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