Fetching the paper…
Reading the bibliography…
Generative models in drug discovery have recently gained attention as efficient alternatives to brute-force virtual screening.
The generation of a unique machine description for chemical structures-a technique developed at chemical abstracts service
Harry L Morgan · 1965
Earlier work this paper cites.
Simple statistical gradient-following algorithms for connectionist reinforcement learning
Ronald J Williams · 1992
Earlier work this paper cites.
The properties of known drugs. 1. molecular frameworks
Guy W Bemis and Mark A Murcko · 1996
Earlier work this paper cites.
Reoptimization of mdl keys for use in drug discovery
Joseph L Durant, Burton A Leland, Douglas R Henry, and James G Nourse · 2002
Earlier work this paper cites.
Consideration of molecular weight during compound selection in virtual target-based database screening
Yongping Pan, Niu Huang, Sam Cho, and Alexander D Mackerell · 2003
Earlier work this paper cites.
Estimation of synthetic accessibility score of drug-like molecules based on molecular complexity and fragment contributions
Peter Ertl and Ansgar Schuffenhauer · 2009
Earlier work this paper cites.
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
Earlier work this paper cites.
Open babel: An open chemical toolbox
Noel M O’Boyle, Michael Banck, Craig A James, Chris Morley, Tim Vandermeersch, and Geoffrey R Hutchison · 2011
Earlier work this paper cites.
Quantifying the chemical beauty of drugs
G Richard Bickerton, Gaia V Paolini, Jérémy Besnard, Sorel Muresan, and Andrew L Hopkins · 2012
Earlier work this paper cites.
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
Earlier work this paper cites.
Using autodock 4 and autodock vina with autodocktools: a tutorial
Ruth Huey, Garrett M Morris, Stefano Forli, et al · 2012
Earlier work this paper cites.
Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling
Greg Landrum et al · 2013
Earlier work this paper cites.
Distributed representations of words and phrases and their compositionality, 2013
Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg Corrado, and Jeffrey Dean · 2013
Earlier work this paper cites.
Fast, accurate, and reliable molecular docking with quickvina 2
Amr Alhossary, Stephanus Daniel Handoko, Yuguang Mu, and Chee-Keong Kwoh · 2015
Earlier work this paper cites.
Deep reinforcement learning in large discrete action spaces
Gabriel Dulac-Arnold, Richard Evans, Hado van Hasselt, Peter Sunehag, Timothy Lillicrap, Jonathan Hunt, Timothy Mann, Theophane Weber, Thomas Degris, and Ben Coppin · 2015
Earlier work this paper cites.
Pdb-wide collection of binding data: current status of the pdbbind database
Zhihai Liu, Yan Li, Li Han, Jie Li, Jie Liu, Zhixiong Zhao, Wei Nie, Yuchen Liu, and Renxiao Wang · 2015
Earlier work this paper cites.
Chemical reactions from US patents (1976-Sep2016), 6 2017
Daniel Lowe · 2017
Earlier work this paper cites.
Scscore: synthetic complexity learned from a reaction corpus
Connor W Coley, Luke Rogers, William H Green, and Klavs F Jensen · 2018
Earlier work this paper cites.
A model to search for synthesizable molecules
John Bradshaw, Brooks Paige, Matt J Kusner, Marwin Segler, and José Miguel Hernández-Lobato · 2019
Earlier work this paper cites.
Automated de novo molecular design by hybrid machine intelligence and rule-driven chemical synthesis
Alexander Button, Daniel Merk, Jan A Hiss, and Gisbert Schneider · 2019
Earlier work this paper cites.
Deep learning enables rapid identification of potent ddr1 kinase inhibitors
Alex Zhavoronkov, Yan A Ivanenkov, Alex Aliper, Mark S Veselov, Vladimir A Aladinskiy, Anastasiya V Aladinskaya, Victor A Terentiev, Daniil A Polykovskiy, Maksim D Kuznetsov, Arip Asadulaev, et al · 2019
Earlier work this paper cites.
Three-dimensional convolutional neural networks and a cross-docked data set for structure-based drug design
Paul G Francoeur, Tomohide Masuda, Jocelyn Sunseri, Andrew Jia, Richard B Iovanisci, Ian Snyder, and David R Koes · 2020
Cited alongside, same era.
The synthesizability of molecules proposed by generative models
Wenhao Gao and Connor W Coley · 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.
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.
Generating multibillion chemical space of readily accessible screening compounds
Oleksandr O Grygorenko, Dmytro S Radchenko, Igor Dziuba, Alexander Chuprina, Kateryna E Gubina, and Yurii S Moroz · 2020
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
Later among the works it cites.
Graph transformer networks: Learning meta-path graphs to improve gnns
Seongjun Yun, Minbyul Jeong, Sungdong Yoo, Seunghun Lee, S Yi Sean, Raehyun Kim, Jaewoo Kang, and Hyunwoo J Kim · 2022
Later among the works it cites.
Gflownet foundations
Yoshua Bengio, Salem Lahlou, Tristan Deleu, Edward J Hu, Mo Tiwari, and Emmanuel Bengio · 2023
Later among the works it cites.
Multi-objective gflownets
Moksh Jain, Sharath Chandra Raparthy, Alex Hernández-Garcıa, Jarrid Rector-Brooks, Yoshua Bengio, Santiago Miret, and Emmanuel Bengio · 2023
Later among the works it cites.
Dfrscore: deep learning-based scoring of synthetic complexity with drug-focused retrosynthetic analysis for high-throughput virtual screening
Hyeongwoo Kim, Kyunghoon Lee, Chansu Kim, Jaechang Lim, and Woo Youn Kim · 2023
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
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.
Learning from protein structure with geometric vector perceptrons
Bowen Jing, Stephan Eismann, Patricia Suriana, Raphael John Lamarre Townshend, and Ron Dror · 2020
Cited alongside, same era.
Lit-pcba: an unbiased data set for machine learning and virtual screening
Viet-Khoa Tran-Nguyen, Célien Jacquemard, and Didier Rognan · 2020
Cited alongside, same era.
Improving conformer generation for small rings and macrocycles based on distance geometry and experimental torsional-angle preferences
Shuzhe Wang, Jagna Witek, Gregory A Landrum, and Sereina Riniker · 2020
Cited alongside, same era.
Flow network based generative models for non-iterative diverse candidate generation
Emmanuel Bengio, Moksh Jain, Maksym Korablyov, Doina Precup, and Yoshua Bengio · 2021
Cited alongside, same era.
Accelerating high-throughput virtual screening through molecular pool-based active learning
David E Graff, Eugene I Shakhnovich, and Connor W Coley · 2021
Cited alongside, same era.
Structure-based screening of novel lichen compounds against sars coronavirus main protease (mpro) as potentials inhibitors of covid-19
Tanuja Joshi, Priyanka Sharma, Tushar Joshi, Hemlata Pundir, Shalini Mathpal, and Subhash Chandra · 2021
Cited alongside, same era.
Exploring chemical space with score-based out-of-distribution generation
Seul Lee, Jaehyeong Jo, and Sung Ju Hwang · 2023
Later among the works it cites.
Hierarchical gflownet for crystal structure generation
Tri Minh Nguyen, Sherif Abdulkader Tawfik, Truyen Tran, Sunil Gupta, Santu Rana, and Svetha Venkatesh · 2023
Later among the works it cites.
Structure-based drug design with equivariant diffusion models, 2023
Arne Schneuing, Yuanqi Du, Charles Harris, Arian Jamasb, Ilia Igashov, Weitao Du, Tom Blundell, Pietro Lió, Carla Gomes, Max Welling, Michael Bronstein, and Bruno Correia · 2023
Later among the works it cites.
Molecular generative model via retrosynthetically prepared chemical building block assembly
Seonghwan Seo, Jaechang Lim, and Woo Youn Kim · 2023
Later among the works it cites.
TacoGFN: Target-conditioned GFlowNet for Structure-based Drug Design
Tony Shen, Seonghwan Seo, Grayson Lee, Mohit Pandey, Jason R Smith, Artem Cherkasov, Woo Youn Kim, and Martin Ester · 2023
Later among the works it cites.
Uni-dock: Gpu-accelerated docking enables ultralarge virtual screening
Yuejiang Yu, Chun Cai, Jiayue Wang, Zonghua Bo, Zhengdan Zhu, and Hang Zheng · 2023
Later among the works it cites.
Phylogfn: Phylogenetic inference with generative flow networks
Mingyang Zhou, Zichao Yan, Elliot Layne, Nikolay Malkin, Dinghuai Zhang, Moksh Jain, Mathieu Blanchette, and Yoshua Bengio · 2023
Later among the works it cites.
Synflownet: Towards molecule design with guaranteed synthesis pathways
Miruna Cretu, Charles Harris, Julien Roy, Emmanuel Bengio, and Pietro Lio · 2024
Closest in time.
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
Closest in time.
Projecting molecules into synthesizable chemical spaces
Shitong Luo, Wenhao Gao, Zuofan Wu, Jian Peng, Connor W. Coley, and Jianzhu Ma · 2024
Closest in time.
MolCRAFT: Structure-based drug design in continuous parameter space
Yanru Qu, Keyue Qiu, Yuxuan Song, Jingjing Gong, Jiawei Han, Mingyue Zheng, Hao Zhou, and Wei-Ying Ma · 2024
Closest in time.
Evosbdd: Latent evolution for accurate and efficient structure-based drug design
Danny Reidenbach · 2024
Closest in time.
Amortizing intractable inference in diffusion models for vision, language, and control
Siddarth Venkatraman, Moksh Jain, Luca Scimeca, Minsu Kim, Marcin Sendera, Mohsin Hasan, Luke Rowe, Sarthak Mittal, Pablo Lemos, Emmanuel Bengio, et al · 2024
Closest in time.
3d molecular generative framework for interaction-guided drug design
Wonho Zhung, Hyeongwoo Kim, and Woo Youn Kim · 2024
Closest in time.
PharmacoNet: deep learning-guided pharmacophore modeling for ultra-large-scale virtual screening
Seonghwan Seo and Woo Youn Kim · 2041
Closest in time.