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We introduce SynFormer, a generative modeling framework designed to efficiently explore and navigate synthesizable chemical space.
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The harvard clean energy project: large-scale computational screening and design of organic photovoltaics on the world community grid
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A collection of robust organic synthesis reactions for in silico molecule design
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Dogs: reaction-driven de novo design of bioactive compounds
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Pyruvate kinase m2 activators promote tetramer formation and suppress tumorigenesis
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What is high-throughput virtual screening? a perspective from organic materials discovery
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Zinc 15–ligand discovery for everyone
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Small-molecule kinase inhibitors: an analysis of fda-approved drugs
Peng Wu, Thomas E Nielsen, and Mads H Clausen · 2016
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Katarzyna Smietana, Marcin Siatkowski, and Martin Møller · 2016
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Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin · 2017
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Grammar variational autoencoder
Matt J Kusner, Brooks Paige, and José Miguel Hernández-Lobato · 2017
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Discovery of a potent, cell penetrant, and selective p300/cbp-associated factor (pcaf)/general control nonderepressible 5 (gcn5) bromodomain chemical probe
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Molecular de-novo design through deep reinforcement learning
Marcus Olivecrona, Thomas Blaschke, Ola Engkvist, and Hongming Chen · 2017
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Inverse molecular design using machine learning: Generative models for matter engineering
Benjamin Sanchez-Lengeling and Alán Aspuru-Guzik · 2018
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Automatic chemical design using a data-driven continuous representation of molecules
Rafael Gómez-Bombarelli, Jennifer N Wei, David Duvenaud, José Miguel Hernández-Lobato, Benjamín Sánchez-Lengeling, Dennis Sheberla, Jorge Aguilera-Iparraguirre, Timothy D Hirzel, Ryan P Adams, and Alán Aspuru-Guzik · 2018
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Junction tree variational autoencoder for molecular graph generation
Wengong Jin, Regina Barzilay, and Tommi Jaakkola · 2018
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Virtual chemical libraries: miniperspective
W Patrick Walters · 2018
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Automating drug discovery
Gisbert Schneider · 2018
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Multi-objective de novo drug design with conditional graph generative model
Yibo Li, Liangren Zhang, and Zhenming Liu · 2018
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Ultra-large library docking for discovering new chemotypes
Jiankun Lyu, Sheng Wang, Trent E Balius, Isha Singh, Anat Levit, Yurii S Moroz, Matthew J O’Meara, Tao Che, Enkhjargal Algaa, Kateryna Tolmachova, et al · 2019
Comparative study of deep generative models on chemical space coverage
Jie Zhang, Rocío Mercado, Ola Engkvist, and Hongming Chen · 2021
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A 3d generative model for structure-based drug design
Shitong Luo, Jiaqi Guan, Jianzhu Ma, and Jian Peng · 2021
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Flow network based generative models for non-iterative diverse candidate generation
Emmanuel Bengio, Moksh Jain, Maksym Korablyov, Doina Precup, and Yoshua Bengio · 2021
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Therapeutics data commons: Machine learning datasets and tasks for drug discovery and development
Kexin Huang, Tianfan Fu, Wenhao Gao, Yue Zhao, Yusuf Roohani, Jure Leskovec, Connor W Coley, Cao Xiao, Jimeng Sun, and Marinka Zitnik · 2021
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Improved denoising diffusion probabilistic models
Alexander Quinn Nichol and Prafulla Dhariwal · 2021
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Electrochemical methods for carbon dioxide separations
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Drug repurposing: progress, challenges and recommendations
Sudeep Pushpakom, Francesco Iorio, Patrick A Eyers, K Jane Escott, Shirley Hopper, Andrew Wells, Andrew Doig, Tim Guilliams, Joanna Latimer, Christine McNamee, et al · 2019
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Variational bayes under model misspecification
Yixin Wang and David Blei · 2019
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A graph-based genetic algorithm and generative model/monte carlo tree search for the exploration of chemical space
Jan H Jensen · 2019
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On failure modes in molecule generation and optimization
Philipp Renz, Dries Van Rompaey, Jörg Kurt Wegner, Sepp Hochreiter, and Günter Klambauer · 2019
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Idea2data: toward a new paradigm for drug discovery
Christos A Nicolaou, Christine Humblet, Hong Hu, Eva M Martin, Frank C Dorsey, Thomas M Castle, Keith Ian Burton, Haitao Hu, Jorg Hendle, Michael J Hickey, et al · 2019
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The next level in chemical space navigation: going far beyond enumerable compound libraries
Torsten Hoffmann and Marcus Gastreich · 2019
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Kyle M Diederichsen, Rezvan Sharifian, Jin Soo Kang, Yayuan Liu, Seoni Kim, Betar M Gallant, David Vermaas, and T Alan Hatton · 2022
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Human-and machine-centred designs of molecules and materials for sustainability and decarbonization
Jiayu Peng, Daniel Schwalbe-Koda, Karthik Akkiraju, Tian Xie, Livia Giordano, Yang Yu, C John Eom, Jaclyn R Lunger, Daniel J Zheng, Reshma R Rao, et al · 2022
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Retrognn: fast estimation of synthesizability for virtual screening and de novo design by learning from slow retrosynthesis software
Cheng-Hao Liu, Maksym Korablyov, Stanisław Jastrzebski, Paweł Włodarczyk-Pruszynski, Yoshua Bengio, and Marwin Segler · 2022
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Sample efficiency matters: a benchmark for practical molecular optimization
Wenhao Gao, Tianfan Fu, Jimeng Sun, and Connor Coley · 2022
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Virtual screening - hit finding and screening services, September 2024
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Synthon-based ligand discovery in virtual libraries of over 11 billion compounds
Arman A Sadybekov, Anastasiia V Sadybekov, Yongfeng Liu, Christos Iliopoulos-Tsoutsouvas, Xi-Ping Huang, Julie Pickett, Blake Houser, Nilkanth Patel, Ngan K Tran, Fei Tong, et al · 2022
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Synthesis-aware generation of structural analogues
Uschi Dolfus, Hans Briem, and Matthias Rarey · 2022
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Artificial intelligence foundation for therapeutic science
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Generative models as an emerging paradigm in the chemical sciences
Dylan M Anstine and Olexandr Isayev · 2023
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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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Autonomous, multiproperty-driven molecular discovery: From predictions to measurements and back
Brent A Koscher, Richard B Canty, Matthew A McDonald, Kevin P Greenman, Charles J McGill, Camille L Bilodeau, Wengong Jin, Haoyang Wu, Florence H Vermeire, Brooke Jin, et al · 2023
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Computational evolution of new catalysts for the morita–baylis–hillman reaction
Julius Seumer, Jonathan Kirschner Solberg Hansen, Mogens Brøndsted Nielsen, and Jan H Jensen · 2023
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Relevance of the trillion-sized chemical space “explore” as a source for drug discovery
Alexander Neumann, Lester Marrison, and Raphael Klein · 2023
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Neural scaling of deep chemical models
Nathan C Frey, Ryan Soklaski, Simon Axelrod, Siddharth Samsi, Rafael Gomez-Bombarelli, Connor W Coley, and Vijay Gadepally · 2023
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Benchmarking generated poses: How rational is structure-based drug design with generative models?
Charles Harris, Kieran Didi, Arian R Jamasb, Chaitanya K Joshi, Simon V Mathis, Pietro Lio, and Tom Blundell · 2023
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Computer-aided evaluation and exploration of chemical spaces constrained by reaction pathways
Itai Levin, Michael E Fortunato, Kian L Tan, and Connor W Coley · 2023
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Genetic algorithms are strong baselines for molecule generation
Austin Tripp and José Miguel Hernández-Lobato · 2023
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Artificial intelligence for natural product drug discovery
Michael W Mullowney, Katherine R Duncan, Somayah S Elsayed, Neha Garg, Justin JJ van der Hooft, Nathaniel I Martin, David Meijer, Barbara R Terlouw, Friederike Biermann, Kai Blin, et al · 2023
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Closing the execution gap in generative ai for chemicals and materials: Freeways or safeguards
Akshay Subramanian, Wenhao Gao, Regina Barzilay, Jeffrey C Grossman, Tommi Jaakkola, Stefanie Jegelka, Mingda Li, Ju Li, Wojciech Matusik, Elsa Olivetti, et al · 2024
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