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Synthesizability in generative molecular design remains a pressing challenge.
Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules
David Weininger · 1988
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Uff, a full periodic table force field for molecular mechanics and molecular dynamics simulations
Anthony K Rappé, Carla J Casewit, KS Colwell, William A Goddard III, and W Mason Skiff · 1992
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Synopsis: synthesize and optimize system in silico
H Maarten Vinkers, Marc R de Jonge, Frederik FD Daeyaert, Jan Heeres, Lucien MH Koymans, Joop H van Lenthe, Paul J Lewi, Henk Timmerman, Koen Van Aken, and Paul AJ Janssen · 2003
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Estimation of synthetic accessibility score of drug-like molecules based on molecular complexity and fragment contributions
Peter Ertl and Ansgar Schuffenhauer · 2009
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Autodock vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading
Oleg Trott and Arthur J Olson · 2010
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Open babel: An open chemical toolbox
Noel M O’Boyle, Michael Banck, Craig A James, Chris Morley, Tim Vandermeersch, and Geoffrey R Hutchison · 2011
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Dogs: reaction-driven de novo design of bioactive compounds
Markus Hartenfeller, Heiko Zettl, Miriam Walter, Matthias Rupp, Felix Reisen, Ewgenij Proschak, Sascha Weggen, Holger Stark, and Gisbert Schneider · 2012
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Quantifying the chemical beauty of drugs
G Richard Bickerton, Gaia V Paolini, Jérémy Besnard, Sorel Muresan, and Andrew L Hopkins · 2012
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Chembl: a large-scale bioactivity database for drug discovery
Anna Gaulton, Louisa J Bellis, A Patricia Bento, Jon Chambers, Mark Davies, Anne Hersey, Yvonne Light, Shaun McGlinchey, David Michalovich, Bissan Al-Lazikani, et al · 2012
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The influence of lipophilicity in drug discovery and design
John A Arnott and Sonia Lobo Planey · 2012
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Openmm 4: a reusable, extensible, hardware independent library for high performance molecular simulation
Peter Eastman, Mark S Friedrichs, John D Chodera, Randall J Radmer, Christopher M Bruns, Joy P Ku, Kyle A Beauchamp, Thomas J Lane, Lee-Ping Wang, Diwakar Shukla, et al · 2013
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Fast, accurate, and reliable molecular docking with quickvina 2
Amr Alhossary, Stephanus Daniel Handoko, Yuguang Mu, and Chee-Keong Kwoh · 2015
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Zinc 15–ligand discovery for everyone
Teague Sterling and John J Irwin · 2015
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An aggregation advisor for ligand discovery
John J Irwin, Da Duan, Hayarpi Torosyan, Allison K Doak, Kristin T Ziebart, Teague Sterling, Gurgen Tumanian, and Brian K Shoichet · 2015
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Better informed distance geometry: using what we know to improve conformation generation
Sereina Riniker and Gregory A Landrum · 2015
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Computer-assisted synthetic planning: the end of the beginning
Sara Szymkuć, Ewa P Gajewska, Tomasz Klucznik, Karol Molga, Piotr Dittwald, Michał Startek, Michał Bajczyk, and Bartosz A Grzybowski · 2016
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Retrosynthetic reaction prediction using neural sequence-to-sequence models
Bowen Liu, Bharath Ramsundar, Prasad Kawthekar, Jade Shi, Joseph Gomes, Quang Luu Nguyen, Stephen Ho, Jack Sloane, Paul Wender, and Vijay Pande · 2017
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Neural-symbolic machine learning for retrosynthesis and reaction prediction
Marwin HS Segler and Mark P Waller · 2017
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Computer-assisted retrosynthesis based on molecular similarity
Connor W Coley, Luke Rogers, William H Green, and Klavs F Jensen · 2017
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Smiles enumeration as data augmentation for neural network modeling of molecules
Esben Jannik Bjerrum · 2017
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Scscore: synthetic complexity learned from a reaction corpus
Connor W Coley, Luke Rogers, William H Green, and Klavs F Jensen · 2018
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Planning chemical syntheses with deep neural networks and symbolic ai
Marwin HS Segler, Mike Preuss, and Mark P Waller · 2018
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Chematica: a story of computer code that started to think like a chemist
Bartosz A Grzybowski, Sara Szymkuć, Ewa P Gajewska, Karol Molga, Piotr Dittwald, Agnieszka Wołos, and Tomasz Klucznik · 2018
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A robotic platform for flow synthesis of organic compounds informed by ai planning
Connor W Coley, Dale A Thomas III, Justin AM Lummiss, Jonathan N Jaworski, Christopher P Breen, Victor Schultz, Travis Hart, Joshua S Fishman, Luke Rogers, Hanyu Gao, et al · 2019
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A model to search for synthesizable molecules
John Bradshaw, Brooks Paige, Matt J Kusner, Marwin Segler, and José Miguel Hernández-Lobato · 2019
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A retrosynthetic analysis algorithm implementation
Ian A Watson, Jibo Wang, and Christos A Nicolaou · 2019
Cited alongside, same era.
The logic of translating chemical knowledge into machine-processable forms: a modern playground for physical-organic chemistry
Karol Molga, Ewa P Gajewska, Sara Szymkuć, and Bartosz A Grzybowski · 2019
Cited alongside, same era.
A graph-based genetic algorithm and generative model/monte carlo tree search for the exploration of chemical space
Jan H Jensen · 2019
Cited alongside, same era.
Randomized smiles strings improve the quality of molecular generative models
Josep Arús-Pous, Simon Viet Johansson, Oleksii Prykhodko, Esben Jannik Bjerrum, Christian Tyrchan, Jean-Louis Reymond, Hongming Chen, and Ola Engkvist · 2019
Cited alongside, same era.
The synthesizability of molecules proposed by generative models
Wenhao Gao and Connor W Coley · 2020
Cited alongside, same era.
Improving de novo molecular design with curriculum learning
Jeff Guo, Vendy Fialková, Juan Diego Arango, Christian Margreitter, Jon Paul Janet, Kostas Papadopoulos, Ola Engkvist, and Atanas Patronov · 2022
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Fake it until you make it? generative de novo design and virtual screening of synthesizable molecules
Megan Stanley and Marwin Segler · 2023
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Tacogfn: Target conditioned gflownet for drug design
Tony Shen, Mohit Pandey, and Martin Ester · 2023
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Link-invent: generative linker design with reinforcement learning
Jeff Guo, Franziska Knuth, Christian Margreitter, Jon Paul Janet, Kostas Papadopoulos, Ola Engkvist, and Atanas Patronov · 2023
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Mamba: Linear-time sequence modeling with selective state spaces
Albert Gu and Tri Dao · 2023
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Syba: Bayesian estimation of synthetic accessibility of organic compounds
Milan Voršilák, Michal Kolář, Ivan Čmelo, and Daniel Svozil · 2020
Cited alongside, same era.
Datasets and their influence on the development of computer assisted synthesis planning tools in the pharmaceutical domain
Amol Thakkar, Thierry Kogej, Jean-Louis Reymond, Ola Engkvist, and Esben Jannik Bjerrum · 2020
Cited alongside, same era.
Aizynthfinder: a fast, robust and flexible open-source software for retrosynthetic planning
Samuel Genheden, Amol Thakkar, Veronika Chadimová, Jean-Louis Reymond, Ola Engkvist, and Esben Bjerrum · 2020
Cited alongside, same era.
Barking up the right tree: an approach to search over molecule synthesis dags
John Bradshaw, Brooks Paige, Matt J Kusner, Marwin Segler, and José Miguel Hernández-Lobato · 2020
Cited alongside, same era.
Chembo: Bayesian optimization of small organic molecules with synthesizable recommendations
Ksenia Korovina, Sailun Xu, Kirthevasan Kandasamy, Willie Neiswanger, Barnabas Poczos, Jeff Schneider, and Eric Xing · 2020
Cited alongside, same era.
Learning to navigate the synthetically accessible chemical space using reinforcement learning
Sai Krishna Gottipati, Boris Sattarov, Sufeng Niu, Yashaswi Pathak, Haoran Wei, Shengchao Liu, Simon Blackburn, Karam Thomas, Connor Coley, Jian Tang, et al · 2020
Cited alongside, same era.
Molecular design in synthetically accessible chemical space via deep reinforcement learning
Julien Horwood and Emmanuel Noutahi · 2020
Cited alongside, same era.
Critical assessment of synthetic accessibility scores in computer-assisted synthesis planning
Grzegorz Skoraczyński, Mateusz Kitlas, Błażej Miasojedow, and Anna Gambin · 2023
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Fsscore: A machine learning-based synthetic feasibility score leveraging human expertise
Rebecca M Neeser, Bruno Correia, and Philippe Schwaller · 2023
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Extracting medicinal chemistry intuition via preference machine learning
Oh-Hyeon Choung, Riccardo Vianello, Marwin Segler, Nikolaus Stiefl, and José Jiménez-Luna · 2023
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Unbiasing retrosynthesis language models with disconnection prompts
Amol Thakkar, Alain C Vaucher, Andrea Byekwaso, Philippe Schwaller, Alessandra Toniato, and Teodoro Laino · 2023
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Gflownet foundations
Yoshua Bengio, Salem Lahlou, Tristan Deleu, Edward J Hu, Mo Tiwari, and Emmanuel Bengio · 2023
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Vina-gpu 2.1: towards further optimizing docking speed and precision of autodock vina and its derivatives
Shidi Tang, Ji Ding, Xiangyu Zhu, Zheng Wang, Haitao Zhao, and Jiansheng Wu · 2023
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Re-evaluating retrosynthesis algorithms with syntheseus
Krzysztof Maziarz, Austin Tripp, Guoqing Liu, Megan Stanley, Shufang Xie, Piotr Gaiński, Philipp Seidl, and Marwin Segler · 2023
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Machine learning-aided generative molecular design
Yuanqi Du, Arian R Jamasb, Jeff Guo, Tianfan Fu, Charles Harris, Yingheng Wang, Chenru Duan, Pietro Liò, Philippe Schwaller, and Tom L Blundell · 2024
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Aizynthfinder 4.0: developments based on learnings from 3 years of industrial application
Lakshidaa Saigiridharan, Alan Kai Hassen, Helen Lai, Paula Torren-Peraire, Ola Engkvist, and Samuel Genheden · 2024
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Double-ended synthesis planning with goal-constrained bidirectional search
Kevin Yu, Jihye Roh, Ziang Li, Wenhao Gao, Runzhong Wang, and Connor W Coley · 2024
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Generative ai for designing and validating easily synthesizable and structurally novel antibiotics
Kyle Swanson, Gary Liu, Denise B Catacutan, Autumn Arnold, James Zou, and Jonathan M Stokes · 2024
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Synflownet: Towards molecule design with guaranteed synthesis pathways
Miruna Cretu, Charles Harris, Julien Roy, Emmanuel Bengio, and Pietro Liò · 2024
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Rgfn: Synthesizable molecular generation using gflownets
Michał Koziarski, Andrei Rekesh, Dmytro Shevchuk, Almer van der Sloot, Piotr Gaiński, Yoshua Bengio, Cheng-Hao Liu, Mike Tyers, and Robert A Batey · 2024
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Sample efficient reinforcement learning with active learning for molecular design
Michael Dodds, Jeff Guo, Thomas Löhr, Alessandro Tibo, Ola Engkvist, and Jon Paul Janet · 2024
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De novo molecular generation of molecules with consistent synthetic strategy
Albin Ekborg · 2024
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Projecting molecules into synthesizable chemical spaces
Shitong Luo, Wenhao Gao, Zuofan Wu, Jian Peng, Connor W Coley, and Jianzhu Ma · 2024
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Aizynth impact on medicinal chemistry practice at astrazeneca
Jason D Shields, Rachel Howells, Gillian Lamont, Yin Leilei, Andrew Madin, Christopher E Reimann, Hadi Rezaei, Tristan Reuillon, Bryony Smith, Clare Thomson, et al · 2024
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Posebusters: Ai-based docking methods fail to generate physically valid poses or generalise to novel sequences
Martin Buttenschoen, Garrett M Morris, and Charlotte M Deane · 2024
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Generate what you can make: Achieving in-house synthesizability with readily available resources in de novo drug design
Alan Kai Hassen, Martin Sicho, Yorick J van Aalst, Mirjam CW Huizenga, Darcy NR Reynolds, Sohvi Luukkonen, Andrius Bernatavicius, Djork-Arné Clevert, Antonius PA Janssen, Gerard JP van Westen, et al · 2024
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