Modelling Chemical Reasoning to Predict Reactions. - Marwin H. S. Segler; Mark P. Waller (CoRR, 2016)

Towards "AlphaChem": Chemical Synthesis Planning with Tree Search and Deep Neural Network Policies. - Marwin H. S. Segler; Mike Preuss; Mark P. Waller (ICLR (Workshop), 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)

Learning to Plan Chemical Syntheses. - Marwin H. S. Segler; Mike Preuss; Mark P. Waller (CoRR, 2017)

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 (ICLR (Workshop), 2018)

Predicting Electron Paths. - John Bradshaw; Matt J. Kusner; Brooks Paige; Marwin H. S. Segler; José Miguel Hernández-Lobato (CoRR, 2018)

DEFactor: Differentiable Edge Factorization-based Probabilistic Graph Generation. - Rim Assouel; Mohamed Ahmed; Marwin H. S. Segler; Amir Saffari; Yoshua Bengio (CoRR, 2018)

Planning chemical syntheses with deep neural networks and symbolic AI. - Marwin H. S. Segler; Mike Preuss; Mark P. Waller (Nat., 2018)

A Generative Model For Electron Paths. - John Bradshaw; Matt J. Kusner; Brooks Paige; Marwin H. S. Segler; José Miguel Hernández-Lobato (ICLR (Poster), 2019)

Generating Molecules via Chemical Reactions. - John Bradshaw; Matt J. Kusner; Brooks Paige; Marwin H. S. Segler; José Miguel Hernández-Lobato (DGS@ICLR, 2019)

A Model to Search for Synthesizable Molecules. - John Bradshaw; Brooks Paige; Matt J. Kusner; Marwin H. S. Segler; José Miguel Hernández-Lobato (NeurIPS, 2019)

World Programs for Model-Based Learning and Planning in Compositional State and Action Spaces. - Marwin H. S. Segler (CoRR, 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)

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 (NeurIPS, 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)

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)

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 (NeurIPS Datasets and Benchmarks, 2021)

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 (ICLR, 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)

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)

Retrosynthetic Planning with Dual Value Networks. - Guoqing Liu; Di Xue; Shufang Xie 003; Yingce Xia; Austin Tripp; Krzysztof Maziarz; Marwin H. S. Segler; Tao Qin 001; Zongzhang Zhang; Tie-Yan Liu (ICML, 2023)

Are VAEs Bad at Reconstructing Molecular Graphs? - Hagen Münkler; Hubert Misztela; Michal Pikusa; Marwin H. S. Segler; Nadine Schneider; Krzysztof Maziarz (CoRR, 2023)

Re-evaluating Retrosynthesis Algorithms with Syntheseus. - Krzysztof Maziarz; Austin Tripp; Guoqing Liu; Megan Stanley; Shufang Xie 003; Piotr Gainski; Philipp Seidl; Marwin H. S. Segler (CoRR, 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., 2023)

RetroBridge: Modeling Retrosynthesis with Markov Bridges. - Ilia Igashov; Arne Schneuing; Marwin H. S. Segler; Michael M. Bronstein; Bruno E. Correia (ICLR, 2024)

Retro-fallback: retrosynthetic planning in an uncertain world. - Austin Tripp; Krzysztof Maziarz; Sarah Lewis; Marwin H. S. Segler; José Miguel Hernández-Lobato (ICLR, 2024)

Generative Active Learning for the Search of Small-molecule Protein Binders. - 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; et al. (CoRR, 2024)

RetroGFN: Diverse and Feasible Retrosynthesis using GFlowNets. - Piotr Gainski; Michal Koziarski; Krzysztof Maziarz; Marwin H. S. Segler; Jacek Tabor; Marek Smieja (CoRR, 2024)

Chimera: Accurate retrosynthesis prediction by ensembling models with diverse inductive biases. - Krzysztof Maziarz; Guoqing Liu; Hubert Misztela; Aleksei Kornev; Piotr Gainski; Holger Hoefling; Mike Fortunato; Rishi Gupta; Marwin H. S. Segler (CoRR, 2024)

SynFlowNet: Design of Diverse and Novel Molecules with Synthesis Constraints. - Miruna T. Cretu; Charles Harris; Ilia Igashov; Arne Schneuing; Marwin H. S. Segler; Bruno E. Correia; Julien Roy; Emmanuel Bengio; Pietro Lio (ICLR, 2025)

Diverse and feasible retrosynthesis using GFlowNets. - Piotr Gainski; Michal Koziarski; Krzysztof Maziarz; Marwin H. S. Segler; Jacek Tabor; Marek Smieja (Inf. Sci., 2025)

Challenging reaction prediction models to generalize to novel chemistry. - John Bradshaw; Anji Zhang; Babak A. Mahjour; David E. Graff; Marwin H. S. Segler; Connor W. Coley (CoRR, 2025)

NatureLM: Deciphering the Language of Nature for Scientific Discovery. - Yingce Xia; Peiran Jin; Shufang Xie 003; Liang He 010; Chuan Cao; Renqian Luo; Guoqing Liu; Yue Wang 017; Zequn Liu; Yuan-Jyue Chen; Zekun Guo; Yeqi Bai; et al. (CoRR, 2025)

UniGenX: Unified Generation of Sequence and Structure with Autoregressive Diffusion. - Gongbo Zhang; Yanting Li; Renqian Luo; Pipi Hu; Zeru Zhao; Lingbo Li; Guoqing Liu; Zun Wang; Ran Bi; Kaiyuan Gao; Liya Guo; Yu Xie; et al. (CoRR, 2025)