Marwin Segler

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Citations	5938	5823
h-index	21	20
i10-index	22	22

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Mark P WallerPending.AIVerified email at pending.ai
Mike PreussUniversiteit LeidenVerified email at liacs.leidenuniv.nl
Nathan BrownDirector of Digital Chemistry, HealxVerified email at healx.io
Christian TyrchanDirector Computational Chemistry, BioPharmaceuticals R&D, Early R&I, AstraZenecaVerified email at astrazeneca.com
Brooks PaigeAssociate Professor, University College LondonVerified email at ucl.ac.uk
Matt J. KusnerAssociate Professor, University College LondonVerified email at ucl.ac.uk
José Miguel Hernández-LobatoProfessor of Machine Learning, University of CambridgeVerified email at cam.ac.uk
Thierry KogejAssociate Principal Scientist, AstraZenecaVerified email at astrazeneca.com
Krzysztof MaziarzMicrosoft ResearchVerified email at microsoft.com
Nadine SchneiderNovartis Institutes for BioMedical ResearchVerified email at novartis.com
Alain C. VaucherIBM ResearchVerified email at zurich.ibm.com
Yoshua BengioProfessor of computer science, University of Montreal, Mila, IVADO, CIFARVerified email at umontreal.ca
Marc BrockschmidtResearch Scientist, Google ResearchVerified email at google.com
Héléna A. GasparSenior Chemoinformatics Data Scientist, BenevolentAI; Visiting Scientist, King's College LondonVerified email at kcl.ac.uk
Thomas EdlichDeepMindVerified email at google.com
Frank GloriusWWU MuensterVerified email at uni-muenster.de
Frederik SandfortPhD Student, Westfälische Wilhelms-Universität MünsterVerified email at uni-muenster.de
Felix Strieth-KalthoffPostdoctoral Researcher, University of TorontoVerified email at utoronto.ca
Günter KlambauerAssoc. Prof., LIT AI Lab & Institute for Machine Learning, Johannes Kepler UniversityVerified email at bioinf.jku.at
Sepp HochreiterInstitute for Machine Learning, Johannes Kepler University LinzVerified email at ml.jku.at

Marwin Segler

Microsoft Research AI4Science

Verified email at microsoft.com

Machine Learning Medicinal Chemistry Organic Chemistry Reinforcement Learning Chemoinformatics


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
Opportunities and obstacles for deep learning in biology and medicine T Ching, DS Himmelstein, BK Beaulieu-Jones, AA Kalinin, BT Do, ... Journal of The Royal Society Interface 15 (141), 20170387, 2018	1581	2018
Planning chemical syntheses with deep neural networks and symbolic AI MHS Segler, M Preuss, MP Waller Nature 555 (7698), 604-610, 2018	1315	2018
Generating focused molecule libraries for drug discovery with recurrent neural networks MHS Segler, T Kogej, C Tyrchan, MP Waller ACS central science 4 (1), 120-131, 2018	1100	2018
GuacaMol: benchmarking models for de novo molecular design N Brown, M Fiscato, MHS Segler, AC Vaucher Journal of chemical information and modeling 59 (3), 1096-1108, 2019	481	2019
Neural‐symbolic machine learning for retrosynthesis and reaction prediction MHS Segler, MP Waller Chemistry–A European Journal 23 (25), 5966-5971, 2017	416	2017
Modelling Chemical Reasoning to Predict and Invent Reactions MHS Segler, MP Waller Chemistry - A European Journal 23 (25), 6118-6128, 2016	178	2016
Machine learning the ropes: principles, applications and directions in synthetic chemistry F Strieth-Kalthoff, F Sandfort, MHS Segler, F Glorius Chemical Society Reviews 49 (17), 6154-6168, 2020	128	2020
Artificial intelligence in drug discovery MA Sellwood, M Ahmed, MHS Segler, N Brown Future medicinal chemistry 10 (17), 2025-2028, 2018	87	2018
A model to search for synthesizable molecules J Bradshaw, B Paige, MJ Kusner, M Segler, JM Hernández-Lobato Advances in Neural Information Processing Systems 32, 2019	84	2019
Exploring deep recurrent models with reinforcement learning for molecule design D Neil, M Segler, L Guasch, M Ahmed, D Plumbley, M Sellwood, N Brown	73	2018
Molecular representation learning with language models and domain-relevant auxiliary tasks B Fabian, T Edlich, H Gaspar, M Segler, J Meyers, M Fiscato, M Ahmed arXiv preprint arXiv:2011.13230, 2020	59	2020
A generative model for electron paths J Bradshaw, MJ Kusner, B Paige, MHS Segler, JM Hernández-Lobato arXiv preprint arXiv:1805.10970, 2018	59*	2018
Defactor: Differentiable edge factorization-based probabilistic graph generation R Assouel, M Ahmed, MH Segler, A Saffari, Y Bengio arXiv preprint arXiv:1811.09766, 2018	55	2018
Improving few-and zero-shot reaction template prediction using modern hopfield networks P Seidl, P Renz, N Dyubankova, P Neves, J Verhoeven, JK Wegner, ... Journal of chemical information and modeling 62 (9), 2111-2120, 2022	45*	2022
Barking up the right tree: an approach to search over molecule synthesis dags J Bradshaw, B Paige, MJ Kusner, M Segler, JM Hernández-Lobato Advances in neural information processing systems 33, 6852-6866, 2020	40	2020
Towards" alphachem": Chemical synthesis planning with tree search and deep neural network policies M Segler, M Preuß, MP Waller arXiv preprint arXiv:1702.00020, 2017	38	2017
Learning to extend molecular scaffolds with structural motifs K Maziarz, H Jackson-Flux, P Cameron, F Sirockin, N Schneider, N Stiefl, ... arXiv preprint arXiv:2103.03864, 2021	35	2021
Fs-mol: A few-shot learning dataset of molecules M Stanley, JF Bronskill, K Maziarz, H Misztela, J Lanini, M Segler, ... Thirty-fifth Conference on Neural Information Processing Systems Datasets …, 2021	33	2021
Silver-catalyzed 1, 3-dipolar cycloaddition of azomethine ylides with β-boryl acrylates A Lopez-Perez, M Segler, J Adrio, JC Carretero The Journal of Organic Chemistry 76 (6), 1945-1948, 2011	30	2011
Retrognn: fast estimation of synthesizability for virtual screening and de novo design by learning from slow retrosynthesis software CH Liu, M Korablyov, S Jastrzębski, P Włodarczyk-Pruszyński, Y Bengio, ... Journal of Chemical Information and Modeling 62 (10), 2293-2300, 2022	23*	2022

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