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Searching the vast chemical space for drug-like molecules that bind with a protein pocket is a challenging task in drug discovery.
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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Glossary of terms used in medicinal chemistry (iupac recommendations 1998)
Camille-Georges Wermuth, CR Ganellin, Per Lindberg, and LA Mitscher · 1998
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A knowledge-based approach in designing combinatorial or medicinal chemistry libraries for drug discovery. 1. a qualitative and quantitative characterization of known drug databases
Arup K Ghose, Vellarkad N Viswanadhan, and John J Wendoloski · 1999
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Consideration of molecular weight during compound selection in virtual target-based database screening
Yongping Pan, Niu Huang, Sam Cho, and Alexander D. MacKerell · 2002
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Conformational analysis of drug-like molecules bound to proteins: An extensive study of ligand reorganization upon binding
Emanuele Perola and Paul S. Charifson · 2004
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Rdkit: Open-source cheminformatics, 2006
Greg Landrum et al · 2006
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On the art of compiling and using “drug-like” chemical fragment spaces
Jörg Degen, Christof Wegscheid-Gerlach, Andrea Zaliani, and Matthias Rarey · 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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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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Pharmacophore modeling and applications in drug discovery: challenges and recent advances
Sheng-Yong Yang · 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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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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Using autodock 4 and autodock vina with autodocktools: a tutorial
Ruth Huey, Garrett M Morris, and Stefano Forli · 2012
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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 · 2014
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How drug-like are ‘ugly’ drugs: do drug-likeness metrics predict adme behaviour in humans?
Timothy J. Ritchie and Simon J.F. Macdonald · 2014
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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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Fast r-cnn
Ross Girshick · 2015
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Adam: A method for stochastic optimization, 2017
Diederik P. Kingma and Jimmy Ba · 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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Graph classification via deep learning with virtual nodes, 2017
Trang Pham, Truyen Tran, Hoa Dam, and Svetha Venkatesh · 2017
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Mmseqs2 enables sensitive protein sequence searching for the analysis of massive data sets
Martin Steinegger and Johannes Söding · 2017
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Junction tree variational autoencoder for molecular graph generation
Wengong Jin, Regina Barzilay, and Tommi S. Jaakkola · 2018
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2018
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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, Alan Norris, Philippe Sanseau, David Cavalla, and Munir Pirmohamed · 2018
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Most ligand-based classification benchmarks reward memorization rather than generalization
Izhar Wallach and Abraham Heifets · 2018
Cited alongside, same era.
Fast graph representation learning with PyTorch Geometric
Matthias Fey and Jan E. Lenssen · 2019
Cited alongside, same era.
Strategies for pre-training graph neural networks
Weihua Hu, Bowen Liu, Joseph Gomes, Marinka Zitnik, Percy Liang, Vijay Pande, and Jure Leskovec · 2019
Cited alongside, same era.
Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala · 2019
Cited alongside, same era.
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, Yury Volkov, Artem Zholus, Rim R. Shayakhmetov, Alexander Zhebrak, Lidiya I. Minaeva, Bogdan A. Zagribelnyy, Lennart H. Lee, Richard Soll, David Madge, Li Xing, Tao Guo, and Alán Aspuru-Guzik · 2019
Generating 3d molecules for target protein binding
Meng Liu, Youzhi Luo, Kanji Uchino, Koji Maruhashi, and Shuiwang Ji · 2022
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Zero-shot 3d drug design by sketching and generating, 2022
Siyu Long, Yi Zhou, Xinyu Dai, and Hao Zhou · 2022
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Tankbind: Trigonometry-aware neural networks for drug-protein binding structure prediction
Wei Lu, Qifeng Wu, Jixian Zhang, Jiahua Rao, Chengtao Li, and Shuangjia Zheng · 2022
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Ligand binding prediction using protein structure graphs and residual graph attention networks
Mohit Pandey, Mariia Radaeva, Hazem Mslati, Olivia Garland, Michael Fernandez, Martin Ester, and Artem Cherkasov · 2022
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Pocket2mol: Efficient molecular sampling based on 3d protein pockets
Xingang Peng, Shitong Luo, Jiaqi Guan, Qi Xie, Jian Peng, and Jianzhu Ma · 2022
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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.
Defining and exploring chemical spaces
Connor W. Coley · 2020
Cited alongside, same era.
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.
Deep docking: a deep learning platform for augmentation of structure based drug discovery
Francesco Gentile, Vibudh Agrawal, Michael Hsing, Anh-Tien Ton, Fuqiang Ban, Ulf Norinder, Martin E Gleave, and Artem Cherkasov · 2020
Cited alongside, same era.
Zinc20—a free ultralarge-scale chemical database for ligand discovery
John J Irwin, Khanh G Tang, Jennifer Young, Chinzorig Dandarchuluun, Benjamin R Wong, Munkhzul Khurelbaatar, Yurii S Moroz, John Mayfield, and Roger A Sayle · 2020
Cited alongside, same era.
Autonomous molecule generation using reinforcement learning and docking to develop potential novel inhibitors
Woosung Jeon and Dongsup Kim · 2020
Cited alongside, same era.
Monn: a multi-objective neural network for predicting compound-protein interactions and affinities
Shuya Li, Fangping Wan, Hantao Shu, Tao Jiang, Dan Zhao, and Jianyang Zeng · 2020
Cited alongside, same era.
Matthew Ragoza, Tomohide Masuda, and David Ryan Koes · 2022
Later among the works it cites.
Mapping information-rich genotype-phenotype landscapes with genome-scale perturb-seq
Joseph M. Replogle, Reuben A. Saunders, Angela N. Pogson, Jeffrey A. Hussmann, Alexander Lenail, Alina Guna, Lauren Mascibroda, Eric J. Wagner, Karen Adelman, Gila Lithwick-Yanai, Nika Iremadze, Florian Oberstrass, Doron Lipson, Jessica L. Bonnar, Marco Jost, Thomas M. Norman, and Jonathan S. Weissman · 2022
Later among the works it cites.
Boosting protein–ligand binding pose prediction and virtual screening based on residue–atom distance likelihood potential and graph transformer
Chao Shen, Xujun Zhang, Yafeng Deng, Junbo Gao, Dong Wang, Lei Xu, Peichen Pan, Tingjun Hou, and Yu Kang · 2022
Later among the works it cites.
Embracing assay heterogeneity with neural processes for markedly improved bioactivity predictions
Lucian Chan, Marcel Verdonk, and Carl Poelking · 2023
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Semi-supervised junction tree variational autoencoder for molecular property prediction, 2023
Atia Hamidizadeh, Tony Shen, and Martin Ester · 2023
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Benchmarking generated poses: How rational is structure-based drug design with generative models?, 2023
Charles Harris, Kieran Didi, Arian R. Jamasb, Chaitanya K. Joshi, Simon V. Mathis, Pietro Lio, and Tom Blundell · 2023
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Multi-objective gflownets, 2023
Moksh Jain, Sharath Chandra Raparthy, Alex Hernandez-Garcia, Jarrid Rector-Brooks, Yoshua Bengio, Santiago Miret, and Emmanuel Bengio · 2023
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DGFN: Double generative flow networks
Elaine Lau, Nikhil Murali Vemgal, Doina Precup, and Emmanuel Bengio · 2023
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Exploring chemical space with score-based out-of-distribution generation
Seul Lee, Jaehyeong Jo, and Sung Ju Hwang · 2023
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Trajectory balance: Improved credit assignment in gflownets, 2023
Nikolay Malkin, Moksh Jain, Emmanuel Bengio, Chen Sun, and Yoshua Bengio · 2023
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Geometric deep learning for structure-based ligand design
Alexander S. Powers, Helen H. Yu, Patricia Suriana, Rohan V. Koodli, Tianyu Lu, Joseph M. Paggi, and Ron O. Dror · 2023
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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
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Pharmaconet: Accelerating large-scale virtual screening by deep pharmacophore modeling, 2023
Seonghwan Seo and Woo Youn Kim · 2023
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Molecular generative model via retrosynthetically prepared chemical building block assembly
Seonghwan Seo, Jaechang Lim, and Woo Youn Kim · 2023
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Uni-dock: Gpu-accelerated docking enables ultralarge virtual screening
Yuejiang Yu, Chun Cai, Jiayue Wang, Zonghua Bo, Zhengdan Zhu, and Hang Zheng · 2023
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Learning subpocket prototypes for generalizable structure-based drug design
Zaixi Zhang and Qi Liu · 2023
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EvoSBDD: Latent evolution for accurate and efficient structure-based drug design
Danny Reidenbach · 2024
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Decompopt: Controllable and decomposed diffusion models for structure-based molecular optimization, 2024
Xiangxin Zhou, Xiwei Cheng, Yuwei Yang, Yu Bao, Liang Wang, and Quanquan Gu · 2024
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