Fetching the paper…
Reading the bibliography…
Structure-based drug design aims at generating high affinity ligands with prior knowledge of 3D target structures.
Optimization by simulated annealing
Scott Kirkpatrick, C Daniel Gelatt Jr, and Mario P Vecchi · 1983
Earlier work this paper cites.
Reinforcement learning: A survey
Leslie Pack Kaelbling, Michael L Littman, and Andrew W Moore · 1996
Earlier work this paper cites.
Integration of virtual and high-throughput screening
Jürgen Bajorath · 2002
Earlier work this paper cites.
Autogrow: a novel algorithm for protein inhibitor design
Jacob D Durrant, Rommie E Amaro, and J Andrew McCammon · 2009
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.
Autodock4 and autodocktools4: Automated docking with selective receptor flexibility
Garrett M Morris, Ruth Huey, William Lindstrom, Michel F Sanner, Richard K Belew, David S Goodsell, and Arthur J Olson · 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.
Molecular docking: a powerful approach for structure-based drug discovery
Xuan-Yu Meng, Hong-Xing Zhang, Mihaly Mezei, and Meng Cui · 2011
Earlier work this paper cites.
Multi-objective optimization methods in de novo drug design
C A Nicolaou, C Kannas, and Erika Loizidou · 2012
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.
Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2013
Earlier work this paper cites.
Evolutionary algorithms for de novo drug design–a survey
R Vasundhara Devi, S Siva Sathya, and Mohane Selvaraj Coumar · 2015
Earlier work this paper cites.
Molecular docking and structure-based drug design strategies
Leonardo G Ferreira, Ricardo N Dos Santos, Glaucius Oliva, and Adriano D Andricopulo · 2015
Earlier work this paper cites.
Trust region policy optimization
John Schulman, Sergey Levine, Pieter Abbeel, Michael Jordan, and Philipp Moritz · 2015
Earlier work this paper cites.
Asynchronous methods for deep reinforcement learning
Volodymyr Mnih, Adria Puigdomenech Badia, Mehdi Mirza, Alex Graves, Timothy Lillicrap, Tim Harley, David Silver, and Koray Kavukcuoglu · 2016
Earlier work this paper cites.
The chembl database in 2017
Anna Gaulton, Anne Hersey, Michał Nowotka, A. Patrícia Bento, Jon Chambers, David Mendez, Prudence Mutowo, Francis Atkinson, Louisa J. Bellis, Elena Cibrián-Uhalte, Mark Davies, Nathan Dedman, Anneli Karlsson, María Paula Magariños, John P. Overington, George Papadatos, Ines Smit, and Andrew R. Leach · 2017
Earlier work this paper cites.
Forging the basis for developing protein–ligand interaction scoring functions
Zhihai Liu, Minyi Su, Li Han, Jie Liu, Qifan Yang, Yan Li, and Renxiao Wang · 2017
Earlier work this paper cites.
Proximal policy optimization algorithms, 2017
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
Earlier work this paper cites.
Molgan: An implicit generative model for small molecular graphs
Nicola De Cao and Thomas Kipf · 2018
Earlier work this paper cites.
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
Cited alongside, same era.
Junction tree variational autoencoder for molecular graph generation
Wengong Jin, Regina Barzilay, and Tommi Jaakkola · 2018
Cited alongside, same era.
Deep reinforcement learning for de novo drug design
Mariya Popova, Olexandr Isayev, and Alexander Tropsha · 2018
Cited alongside, same era.
Graph convolutional policy network for goal-directed molecular graph generation
Jiaxuan You, Bowen Liu, Zhitao Ying, Vijay Pande, and Jure Leskovec · 2018
Cited alongside, same era.
A structure-based drug discovery paradigm
Maria Batool, Bilal Ahmad, and Sangdun Choi · 2019
Cited alongside, same era.
Graphaf: a flow-based autoregressive model for molecular graph generation
Chence Shi, Minkai Xu*, Zhaocheng Zhu, Weinan Zhang, Ming Zhang, and Jian Tang · 2020
Later among the works it cites.
A generative model for molecular distance geometry
Gregor Simm and Jose Miguel Hernandez-Lobato · 2020
Later among the works it cites.
Autogrow4: an open-source genetic algorithm for de novo drug design and lead optimization
Jacob O Spiegel and Jacob D Durrant · 2020
Later among the works it cites.
Assessing the impact of generative ai on medicinal chemistry
W Patrick Walters and Mark Murcko · 2020
Later among the works it cites.
Autodock vina 1.2.0: New docking methods, expanded force field, and python bindings
Jerome Eberhardt, Diogo Santos-Martins, Andreas F Tillack, and Stefano Forli · 2021
Later among the works it cites.
Mimosa: Multi-constraint molecule sampling for molecule optimization
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Jan H Jensen · 2019
Cited alongside, same era.
Molecular geometry prediction using a deep generative graph neural network
Elman Mansimov, Omar Mahmood, Seokho Kang, and Kyunghyun Cho · 2019
Cited alongside, same era.
Molecularrnn: Generating realistic molecular graphs with optimized properties
Mariya Popova, Mykhailo Shvets, Junier Oliva, and Olexandr Isayev · 2019
Cited alongside, same era.
Drugcentral 2018: an update
Oleg Ursu, Jayme Holmes, Cristian G Bologa, Jeremy J Yang, Stephen L Mathias, Vasileios Stathias, Dac-Trung Nguyen, Stephan Schürer, and Tudor Oprea · 2019
Cited alongside, same era.
Efficient multi-objective molecular optimization in a continuous latent space
Robin Winter, Floriane Montanari, Andreas Steffen, Hans Briem, Frank Noé, and Djork-Arné Clevert · 2019
Cited alongside, same era.
Guiding deep molecular optimization with genetic exploration
Sung-Soo Ahn, Junsu Kim, Hankook Lee, and Jinwoo Shin · 2020
Cited alongside, same era.
Geom: Energy-annotated molecular conformations for property prediction and molecular generation
Simon Axelrod and Rafael Gomez-Bombarelli · 2020
Cited alongside, same era.
Tianfan Fu, Cao Xiao, Xinhao Li, Lucas M Glass, and Jimeng Sun · 2021
Later among the works it cites.
Geomol: Torsional geometric generation of molecular 3d conformer ensembles
Octavian-Eugen Ganea, Lagnajit Pattanaik, Connor W Coley, Regina Barzilay, Klavs F Jensen, William H Green, and Tommi S Jaakkola · 2021
Later among the works it cites.
De novo structure-based drug design using deep learning
Sowmya Ramaswamy Krishnan, Navneet Bung, Sarveswara Rao Vangala, Rajgopal Srinivasan, Gopalakrishnan Bulusu, and Arijit Roy · 2021
Later among the works it cites.
A 3d generative model for structure-based drug design
Shitong Luo, Jiaqi Guan, Jianzhu Ma, and Jian Peng · 2021
Later among the works it cites.
Learning gradient fields for molecular conformation generation
Chence Shi, Shitong Luo, Minkai Xu, and Jian Tang · 2021
Later among the works it cites.
Mars: Markov molecular sampling for multi-objective drug discovery
Yutong Xie, Chence Shi, Hao Zhou, Yuwei Yang, Weinan Zhang, Yong Yu, and Lei Li · 2021
Later among the works it cites.
Learning neural generative dynamics for molecular conformation generation
Minkai Xu, Shitong Luo, Yoshua Bengio, Jian Peng, and Jian Tang · 2021
Later among the works it cites.
Optimization of molecules via deep reinforcement learning
Zhenpeng Zhou, Steven Kearnes, Li Li, Richard N Zare, and Patrick Riley · 2021
Later among the works it cites.
Reinforced genetic algorithm for structure-based drug design
Tianfan Fu, Wenhao Gao, Connor Coley, and Jimeng Sun · 2022
Later among the works it cites.
Generating 3D molecules for target protein binding
Meng Liu, Youzhi Luo, Kanji Uchino, Koji Maruhashi, and Shuiwang Ji · 2022
Later among the works it cites.
Zero-shot 3d drug design by sketching and generating
Siyu Long, Yi Zhou, Xinyu Dai, and Hao Zhou · 2022
Later among the works it cites.
Pocket2mol: Efficient molecular sampling based on 3d protein pockets
Xingang Peng, Shitong Luo, Jiaqi Guan, Qi Xie, Jian Peng, and Jianzhu Ma · 2022
Later among the works it cites.
RDKit: Open-source cheminformatics software
Greg Landrum · 2023
Later among the works it cites.