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Chemistry (Weinheim an Der Bergstrasse, Germany)|January 31, 2017
Neural-Symbolic Machine Learning for Retrosynthesis and Reaction PredictionMarwin H S Segler, Mark P WallerChemistry (Weinheim an Der Bergstrasse, Germany)|November 19, 2016
Modelling Chemical Reasoning to Predict and Invent ReactionsMarwin H S Segler, Mark P WallerNature|March 30, 2018
Planning chemical syntheses with deep neural networks and symbolic AIMarwin H S Segler, Mike Preuss, Mark P WallerJournal of Chemical Information and Modeling|March 20, 2019
GuacaMol: Benchmarking Models for de Novo Molecular DesignNathan Brown, Marco Fiscato, Marwin H S Segler, et al.Chemical Society Reviews|July 17, 2020
Machine learning the ropes: principles, applications and directions in synthetic chemistryFelix Strieth-Kalthoff, Frederik Sandfort, Marwin H S Segler, et al.ACS Central Science|February 3, 2018
Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural NetworksMarwin H S Segler, Thierry Kogej, Christian Tyrchan, et al.ACS Central Science|April 28, 2025
Challenging Reaction Prediction Models to Generalize to Novel ChemistryJohn Bradshaw, Anji Zhang, Babak Mahjour, et al.Faraday Discussions|November 1, 2024
Re-evaluating retrosynthesis algorithms with SyntheseusKrzysztof Maziarz, Austin Tripp, Guoqing Liu, et al.Journal of the Royal Society, Interface|April 6, 2018
Opportunities and obstacles for deep learning in biology and medicineTravers Ching, Daniel S Himmelstein, Brett K Beaulieu-Jones, et al.Pageof 1