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Discovering novel drug candidate molecules is one of the most fundamental and critical steps in drug development.
Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings
Christopher A. Lipinski, Franco Lombardo, Beryl W. Dominy, and Paul J. Feeney · 1997
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Molecular properties that influence the oral bioavailability of drug candidates
Daniel F. Veber, Stephen R. Johnson, Hung-Yuan Cheng, Brian R. Smith, Keith W. Ward, and Kenneth D. Kopple · 2002
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Reoptimization of mdl keys for use in drug discovery
Joseph L. Durant, Burton A. Leland, Douglas R. Henry, and James G. Nourse · 2002
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Extra precision glide: Docking and scoring incorporating a model of hydrophobic enclosure for protein-ligand complexes
Richard A. Friesner, Robert B. Murphy, Matthew P. Repasky, Leah L. Frye, Jeremy R. Greenwood, Thomas A. Halgren, Paul C. Sanschagrin, and Daniel T. Mainz · 2006
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Visualizing data using t-sne
Laurens Van der Maaten and Geoffrey Hinton · 2008
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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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How to improve r&d productivity: the pharmaceutical industry’s grand challenge
Steven M. Paul, Daniel S. Mytelka, Christopher T. Dunwiddie, Charles C. Persinger, Bernard H. Munos, Stacy R. Lindborg, and Aaron L. Schacht · 2010
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Extended-connectivity fingerprints
David Rogers and Mathew Hahn · 2010
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New substructure filters for removal of pan assay interference compounds (pains) from screening libraries and for their exclusion in bioassays
Jonathan B. Baell and Georgina A. Holloway · 2010
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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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A generalization of transformer networks to graphs
Vijay Prakash Dwivedi and Xavier Bresson · 2012
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Auto-encoding variational bayes
Diederik P. Kingma and Max Welling · 2013
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Discovery of 4-amino- n -[(1 s )-1-(4-chlorophenyl)-3-hydroxypropyl]-1-(7 h -pyrrolo[2,3- d ]pyrimidin-4-yl)piperidine-4-carboxamide (azd5363), an orally bioavailable, potent inhibitor of akt kinases
Matt Addie, Peter Ballard, David Buttar, Claire Crafter, Gordon Currie, Barry R. Davies, Judit Debreczeni, Hannah Dry, Philippa Dudley, Ryan Greenwood, Paul D. Johnson, Jason G. Kettle, Clare Lane, Gillian Lamont, Andrew Leach, Richard W. A. Luke, Jeff Morris, Donald Ogilvie, Ken Page, Martin Pass, Stuart Pearson, and Linette Ruston · 2013
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Rdkit documentation
Greg Landrum · 2013
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Protein and ligand preparation: parameters, protocols, and influence on virtual screening enrichments
G. Madhavi Sastry, Matvey Adzhigirey, Tyler Day, Ramakrishna Annabhimoju, and Woody Sherman · 2013
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Cyclin-dependent kinase inhibitor dinaciclib interacts with the acetyl-lysine recognition site of bromodomains
Mathew P Martin, Sanne H Olesen, Gunda I Georg, and Ernst Schonbrunn · 2013
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Deep unsupervised learning using nonequilibrium thermodynamics
Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli · 2015
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The pymol molecular graphics system
L. L. C. Schrodinger · 2015
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Semi-supervised classification with graph convolutional networks
Thomas N. Kipf and Max Welling · 2016
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drugan: An advanced generative adversarial autoencoder model for de novo generation of new molecules with desired molecular properties in silico
Artur Kadurin, Sergey Nikolenko, Kuzma Khrabrov, Alex Aliper, and Alex Zhavoronkov · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Unpaired image-to-image translation using cycle-consistent adversarial networks
Jun-Yan Zhu, Taesung Park, Phillip Isola, and Alexei A Efros · 2017
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Learning to discover cross-domain relations with generative adversarial networks
Taeksoo Kim, Moonsu Cha, Hyunsoo Kim, Jung Kwon Lee, and Jiwon Kim · 2017
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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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Wasserstein generative adversarial networks
Martin Arjovsky, Soumith Chintala, and Léon Bottou · 2017
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Improved training of wasserstein gans
Ishaan Gulrajani, Faruk Ahmed, Martin Arjovsky, Vincent Dumoulin, and Aaron C Courville · 2017
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2017
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Virtual chemical libraries: miniperspective
W. Patrick Walters · 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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Molgan: An implicit generative model for small molecular graphs
Nicola De Cao and Thomas Kipf · 2018
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Objective-reinforced generative adversarial networks (organ) for sequence generation models
Gabriel Lima Guimaraes, Benjamin Sanchez-Lengeling, Carlos Outeiral, Pedro Luis Cunha Farias, and Alán Aspuru-Guzik · 2018
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Umap: Uniform manifold approximation and projection for dimension reduction
Leland McInnes, John Healy, and James Melville · 2018
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Drugbank 5.0: a major update to the drugbank database for 2018
David S Wishart, Yannick D Feunang, An C Guo, Elvis J Lo, Ana Marcu, Jason R Grant, Tanvir Sajed, Daniel Johnson, Carin Li, Zinat Sayeeda, Nazanin Assempour, Ithayavani Iynkkaran, Yifeng Liu, Adam Maciejewski, Nicola Gale, Alex Wilson, Lucy Chin, Ryan Cummings, Diana Le, Allison Pon, Craig Knox, and Michael Wilson · 2018
Cited alongside, same era.
Recent applications of deep learning and machine intelligence on in silico drug discovery: methods, tools and databases
Ahmet Sureyya Rifaioglu, Heval Atas, Maria Jesus Martin, Rengul Cetin-Atalay, Volkan Atalay, and Tunca Doğan · 2019
Cited alongside, same era.
Deep learning for molecular design—a review of the state of the art
Daniel C. Elton, Zois Boukouvalas, Mark D. Fuge, and Peter W. Chung · 2019
Cited alongside, same era.
Randomized smiles strings improve the quality of molecular generative models
Inverse design of 3d molecular structures with conditional generative neural networks
Niklas W. A. Gebauer, Michael Gastegger, Stefaan S. P. Hessmann, Klaus-Robert Müller, and Kristof T. Schütt · 2022
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Generative deep learning enables the discovery of a potent and selective ripk1 inhibitor
Yueshan Li, Liting Zhang, Yifei Wang, Jun Zou, Ruicheng Yang, Xinling Luo, Chengyong Wu, Wei Yang, Chenyu Tian, and Haixing Xu · 2022
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Generating 3d molecules for target protein binding
Meng Liu, Youzhi Luo, Kanji Uchino, Koji Maruhashi, and Shuiwang Ji · 2022
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Pocket2drug: an encoder-decoder deep neural network for the target-based drug design
Wentao Shi, Manali Singha, Gopal Srivastava, Limeng Pu, J. Ramanujam, and Michal Brylinski · 2022
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Exploiting pretrained biochemical language models for targeted drug design
Gökçe Uludoğan, Elif Ozkirimli, Kutlu O Ulgen, Nilgün Karalı, and Arzucan Özgür · 2022
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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.
Optimization of molecules via deep reinforcement learning
Zhenpeng Zhou, Steven Kearnes, Li Li, Richard N Zare, and Patrick Riley · 2019
Cited alongside, same era.
Chembl: towards direct deposition of bioassay data
David Mendez, Anna Gaulton, A. Patrícia Bento, Jon Chambers, Marleen De Veij, Eloy Félix, María Paula Magariños, Juan F. Mosquera, Prudence Mutowo, and Michał Nowotka · 2019
Cited alongside, same era.
Analyzing learned molecular representations for property prediction
Kevin Yang, Kyle Swanson, Wengong Jin, Connor Coley, Philipp Eiden, Hua Gao, Angel Guzman-Perez, Timothy Hopper, Brian Kelley, Miriam Mathea, et al · 2019
Cited alongside, same era.
Generative adversarial networks
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2020
Cited alongside, same era.
Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
Cited alongside, same era.
Reinvent 2.0: an ai tool for de novo drug design
Thomas Blaschke, Josep Arús-Pous, Hongming Chen, Christian Margreitter, Christian Tyrchan, Ola Engkvist, Kostas Papadopoulos, and Atanas Patronov · 2020
Cited alongside, same era.
Self-supervised graph transformer on large-scale molecular data
Yu Rong, Yatao Bian, Tingyang Xu, Weiyang Xie, Ying Wei, Wenbing Huang, and Junzhou Huang · 2020
Cited alongside, same era.
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Relation: A deep generative model for structure-based de novo drug design
Mingyang Wang, Chang-Yu Hsieh, Jike Wang, Dong Wang, Gaoqi Weng, Chao Shen, Xiaojun Yao, Zhitong Bing, Honglin Li, Dongsheng Cao, and Tingjun Hou · 2022
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Kpgt: knowledge-guided pre-training of graph transformer for molecular property prediction
Han Li, Dan Zhao, and Jianyang Zeng · 2022
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Digress: Discrete denoising diffusion for graph generation
Clement Vignac, Igor Krawczuk, Antoine Siraudin, Bohan Wang, Volkan Cevher, and Pascal Frossard · 2022
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1: Maestro, Schrödinger, LLC, New York, NY. 2021.[(accessed on 10 December 2021)]
Schrödinger Release · 2022
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Stagan: An approach for improve the stability of molecular graph generation based on generative adversarial networks
Jinping Zou, Jialin Yu, Pengwei Hu, Long Zhao, and Shaoping Shi · 2023
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De novo direct inverse qspr/qsar: Chemical variational autoencoder and gaussian mixture regression models
Kohei Nemoto and Hiromasa Kaneko · 2023
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Helixgan a deep-learning methodology for conditional de novo design of α \alpha -helix structures
Xuezhi Xie, Pedro A. Valiente, and Philip M. Kim · 2023
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Petrans: De novo drug design with protein-specific encoding based on transfer learning
Xun Wang, Changnan Gao, Peifu Han, Xue Li, Wenqi Chen, Alfonso Rodríguez Patón, Shuang Wang, and Pan Zheng · 2023
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Cmgn: a conditional molecular generation net to design target-specific molecules with desired properties
Minjian Yang, Hanyu Sun, Xue Liu, Xi Xue, Yafeng Deng, and Xiaojian Wang · 2023
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Resgen is a pocket-aware 3d molecular generation model based on parallel multiscale modelling
Odin Zhang, Jintu Zhang, Jieyu Jin, Xujun Zhang, RenLing Hu, Chao Shen, Hanqun Cao, Hongyan Du, Yu Kang, Yafeng Deng, et al · 2023
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3d equivariant diffusion for target-aware molecule generation and affinity prediction
Jiaqi Guan, Wesley Wei Qian, Xingang Peng, Yufeng Su, Jian Peng, and Jianzhu Ma · 2023
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3d equivariant diffusion for target-aware molecule generation and affinity prediction
Jiaqi Guan, Wesley Wei Qian, Xingang Peng, Yufeng Su, Jian Peng, and Jianzhu Ma · 2023
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Semi-equivariant conditional normalizing flows, with applications to target-aware molecule generation
Eyal Rozenberg and Daniel Freedman · 2023
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Universal approach to de novo drug design for target proteins using deep reinforcement learning
Yunjiang Zhang, Shuyuan Li, Miaojuan Xing, Qing Yuan, Hong He, and Shaorui Sun · 2023
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De novo drug design by iterative multiobjective deep reinforcement learning with graph-based molecular quality assessment
Yi Fang, Xiaoyong Pan, and Hong-Bin Shen · 2023
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De novo generation of chemical structures of inhibitor and activator candidates for therapeutic target proteins by a transformer-based variational autoencoder and bayesian optimization
Yuki Matsukiyo, Chikashige Yamanaka, and Yoshihiro Yamanishi · 2023
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Multi-objective gflownets
Moksh Jain, Sharath Chandra Raparthy, Alex Hernández-Garcıa, Jarrid Rector-Brooks, Yoshua Bengio, Santiago Miret, and Emmanuel Bengio · 2023
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Fsm-ddtr: End-to-end feedback strategy for multi-objective de novo drug design using transformers
Nelson RC Monteiro, Tiago O Pereira, Ana Catarina D Machado, José L Oliveira, Maryam Abbasi, and Joel P Arrais · 2023
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A review on generative adversarial networks: Algorithms, theory, and applications
Jie Gui, Zhenan Sun, Yonggang Wen, Dacheng Tao, and Jieping Ye · 2023
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Direct generation of protein conformational ensembles via machine learning
Giacomo Janson, Gilberto Valdes-Garcia, Lim Heo, and Michael Feig · 2023
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Selformer: Molecular representation learning via selfies language models
Atakan Yüksel, Erva Ulusoy, Atabey Ünlü, and Tunca Doğan · 2023
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Uncorrupt smiles: a novel approach to de novo design
Linde Schoenmaker, Olivier JM Béquignon, Willem Jespers, and Gerard JP van Westen · 2023
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1: Desmond Molecular Dynamics System, DE Shaw Research, New York, NY, 2021. Maestro-Desmond Interoperability Tools, Schrödinger
Schrödinger Release · 2023
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Multi-objective latent space optimization of generative molecular design models
ANM Nafiz Abeer, Nathan M Urban, M Ryan Weil, Francis J Alexander, and Byung-Jun Yoon · 2024
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Mothra: Multiobjective de novo molecular generation using monte carlo tree search
Takamasa Suzuki, Dian Ma, Nobuaki Yasuo, and Masakazu Sekijima · 2024
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