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Virtual screening can accelerate drug discovery by identifying promising candidates for experimental evaluation.
A qsar investigation of dihydrofolate reductase inhibition by baker triazines based upon molecular shape analysis
Anton J Hopfinger · 1980
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A computational procedure for determining energetically favorable binding sites on biologically important macromolecules
Peter J Goodford · 1985
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Atom pairs as molecular features in structure-activity studies: definition and applications
Raymond E Carhart, Dennis H Smith, and R Venkataraghavan · 1985
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Comparative molecular field analysis (comfa). 1. effect of shape on binding of steroids to carrier proteins
Richard D Cramer, David E Patterson, and Jeffrey D Bunce · 1988
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Qsar analyses of the substituted indanone and benzylpiperidine rings of a series of indanone-benzylpiperidine inhibitors of acetylcholinesterase
MG Cardozo, Y Iimura, H Sugimoto, Y Yamanishi, and AJ Hopfinger · 1992
Earlier work this paper cites.
Three-dimensional molecular shape analysis-quantitative structure-activity relationship of a series of cholecystokinin-a receptor antagonists
John S Tokarski and Anton J Hopfinger · 1994
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Compass: predicting biological activities from molecular surface properties. performance comparisons on a steroid benchmark
Ajay N Jain, Kimberle Koile, and David Chapman · 1994
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A 3d-qsar study of anticoccidial triazines using molecular shape analysis
K-B Rhyu, HC Patel, and Anton J. Hopfinger · 1995
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Comparative molecular field analysis (comfa)
KH Kim · 1995
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Conformational analysis, molecular shape comparison, and pharmacophore identification of different allosteric modulators of muscarinic receptors
Ulrike Holzgrabe and Anton J Hopfinger · 1996
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Construction of 3d-qsar models using the 4d-qsar analysis formalism
AJ Hopfinger, Shen Wang, John S Tokarski, Baiqiang Jin, Magaly Albuquerque, Prakash J Madhav, and Chaya Duraiswami · 1997
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List of comfa references, 1998
KH Kim · 1998
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A critical review of recent comfa applications
Ki Hwan Kim, Giovanni Greco, and Ettore Novellino · 1998
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Four-dimensional quantitative structure- activity relationship analysis of a series of interphenylene 7-oxabicycloheptane oxazole thromboxane a2 receptor antagonists
Magaly G Albuquerque, Anton J Hopfinger, EJ Barreiro, and Ricardo B de Alencastro · 1998
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Qsar and comfa: a perspective on the practical application to drug discovery
Brent L Podlogar and David M Ferguson · 2000
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Drug design by machine learning: support vector machines for pharmaceutical data analysis
Robert Burbidge, Matthew Trotter, B Buxton, and Sl Holden · 2001
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4d-qsar analysis of a set of ecdysteroids and a comparison to comfa modeling
Malini Ravi, Anton J Hopfinger, Robert E Hormann, and Laurence Dinan · 2001
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Thermodynamic aspects of hydrophobicity and biological qsar
Ki H Kim · 2001
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4d-qsar analysis of a set of propofol analogues: mapping binding sites for an anesthetic phenol on the gabaa receptor
Matthew D Krasowski, Xuan Hong, AJ Hopfinger, and Neil L Harrison · 2002
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Current state and perspectives of 3d-qsar
Miki Akamatsu · 2002
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4d-qsar analysis of a series of antifungal p450 inhibitors and 3d-pharmacophore comparisons as a function of alignment
Jianzhong Liu, Dahua Pan, Yufeng Tseng, and Anton J Hopfinger · 2003
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3d-pharmacophores of flavonoid binding at the benzodiazepine gabaa receptor site using 4d-qsar analysis
Xuan Hong and Anton J Hopfinger · 2003
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3d qsar modeling in drug design
Tudor I Oprea · 2003
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Descriptors from molecular geometry
Roberto Todeschini and Viviana Consonni · 2003
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4d-fingerprints, universal qsar and qspr descriptors
Craig L Senese, J Duca, Dahua Pan, Anton J Hopfinger, and Yufeng J Tseng · 2004
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Rational design of new antituberculosis agents: receptor-independent four-dimensional quantitative structure- activity relationship analysis of a set of isoniazid derivatives
Kerly FM Pasqualoto, Elizabeth I Ferreira, Osvaldo A Santos-Filho, and Anton J Hopfinger · 2004
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The 3d structure of the binding pocket of the human oxytocin receptor for benzoxazine antagonists, determined by molecular docking, scoring functions and 3d-qsar methods
Balázs Jójárt, Tamás A Martinek, and Árpád Márki · 2005
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A shape-based 3-d scaffold hopping method and its application to a bacterial protein- protein interaction
Thomas S Rush, J Andrew Grant, Lidia Mosyak, and Anthony Nicholls · 2005
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Construction of 4d-qsar models for use in the design of novel p38-mapk inhibitors
Nelilma Correia Romeiro, Magaly Girão Albuquerque, Ricardo Bicca de Alencastro, Malini Ravi, and Anton J Hopfinger · 2005
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TarFisDock: a web server for identifying drug targets with docking approach
Honglin Li, Zhenting Gao, Ling Kang, Hailei Zhang, Kun Yang, Kunqian Yu, Xiaomin Luo, Weiliang Zhu, Kaixian Chen, Jianhua Shen, et al · 2006
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Combining docking and molecular dynamic simulations in drug design
Hernan Alonso, Andrey A Bliznyuk, and Jill E Gready · 2006
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Three-dimensional qsar using the k-nearest neighbor method and its interpretation
Subhash Ajmani, Kamalakar Jadhav, and Sudhir A Kulkarni · 2006
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Gbpm: Grid-based pharmacophore model: concept and application studies to protein–protein recognition
Francesco Ortuso, Thierry Langer, and Stefano Alcaro · 2006
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3d qsar study of hypolipidemic asarones by comparative molecular surface analysis
Tomasz Magdziarz, Bozena Łozowicka, Rafał Gieleciak, Andrzej Bąk, Jarosław Polański, and Zdzisław Chilmonczyk · 2006
Earlier work this paper cites.
Comparative molecular surface analysis (comsa) for virtual combinatorial library screening of styrylquinoline hiv-1 blocking agents
Halina Niedbala, Jaroslaw Polanski, Rafal Gieleciak, Robert Musiol, Dominik Tabak, Barbara Podeszwa, Andrzej Bak, Anna Palka, Jean-Francois Mouscadet, Johann Gasteiger, et al · 2006
Earlier work this paper cites.
Modeling robust qsar. 2. iterative variable elimination schemes for comsa: Application for modeling benzoic acid p k a values
Rafal Gieleciak and Jaroslaw Polanski · 2007
Cited alongside, same era.
Treating chemical diversity in qsar analysis: modeling diverse hiv-1 integrase inhibitors using 4d fingerprints
Manisha Iyer and Anton J Hopfinger · 2007
Cited alongside, same era.
Comparison of shape-matching and docking as virtual screening tools
Paul CD Hawkins, A Geoffrey Skillman, and Anthony Nicholls · 2007
Cited alongside, same era.
Multidimensional-qsar: Beyond the third-dimension in drug design
M Albuquerque, M Brito, E Cunha, R Alencastro, O Antunes, H Castro, and C Rodrigues · 2007
Cited alongside, same era.
Generating conformer ensembles using a multiobjective genetic algorithm
Mikko J Vainio and Mark S Johnson · 2007
Cited alongside, same era.
ANI-1, A data set of 20 million calculated off-equilibrium conformations for organic molecules
Justin S. Smith, Olexandr Isayev, and Adrian E. Roitberg · 2017
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Nontargeted metabolomics reveals the multilevel response to antibiotic perturbations
Mattia Zampieri, Michael Zimmermann, Manfred Claassen, and Uwe Sauer · 2017
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A simple representation of three-dimensional molecular structure
Seth D Axen, Xi-Ping Huang, Elena L Cáceres, Leo Gendelev, Bryan L Roth, and Michael J Keiser · 2017
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The atomic simulation environment—a Python library for working with atoms
Ask Hjorth Larsen, Jens Jørgen Mortensen, Jakob Blomqvist, Ivano E Castelli, Rune Christensen, Marcin Dułak, Jesper Friis, Michael N Groves, Bjørk Hammer, Cory Hargus, et al · 2017
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SchNet–A deep learning architecture for molecules and materials
Kristof T Schütt, Huziel E Sauceda, P-J Kindermans, Alexandre Tkatchenko, and K-R Müller · 2018
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alphaXiv searches the wider corpus for related work and actual follow-ups.
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Ajay N Jain and Anthony Nicholls · 2008
Cited alongside, same era.
What do we know and when do we know it?
Anthony Nicholls · 2008
Cited alongside, same era.
Rational design and 3d-pharmacophore mapping of 5’-thiourea-substituted α \alpha -thymidine analogues as mycobacterial tmpk inhibitors
Carolina H Andrade, Kerly FM Pasqualoto, Elizabeth I Ferreira, and Anton J Hopfinger · 2009
Cited alongside, same era.
Receptor independent and receptor dependent comsa modeling with ive-pls: application to cbg benchmark steroids and reductase activators
Tomasz Magdziarz, Pawel Mazur, and Jaroslaw Polanski · 2009
Cited alongside, same era.
3d pharmacophore mapping using 4d qsar analysis for the cytotoxicity of lamellarins against human hormone-dependent t47d breast cancer cells
Poonsiri Thipnate, Jianzhong Liu, Supa Hannongbua, and Anton J Hopfinger · 2009
Cited alongside, same era.
Deconstructing the drug development process: the new face of innovation
Kenneth I Kaitin · 2010
Cited alongside, same era.
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
Cited alongside, same era.
Nathaniel Thomas, Tess Smidt, Steven Kearnes, Lusann Yang, Li Li, Kai Kohlhoff, and Patrick Riley · 2018
Later among the works it cites.
Potentialnet for molecular property prediction
Evan N Feinberg, Debnil Sur, Zhenqin Wu, Brooke E Husic, Huanghao Mai, Yang Li, Saisai Sun, Jianyi Yang, Bharath Ramsundar, and Vijay S Pande · 2018
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A sobering assessment of small-molecule force field methods for low energy conformer predictions
Ilana Y. Kanal, John A. Keith, and Geoffrey R. Hutchison · 2018
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https://github.com/atomistic-machine-learning/schnetpack
SchNetPack - Deep Neural Networks for Atomistic Systems · 2018
Later among the works it cites.
CCG: Molecular Operating Environment (MOE)
Chemical Computing Group ULC · 2018
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MoleculeNet: a benchmark for molecular machine learning
Zhenqin Wu, Bharath Ramsundar, Evan˜N. Feinberg, Joseph Gomes, Caleb Geniesse, Aneesh S Pappu, Karl Leswing, and Vijay Pande · 2018
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Large-scale comparison of machine learning methods for drug target prediction on chembl
Andreas Mayr, Günter Klambauer, Thomas Unterthiner, Marvin Steijaert, Jörg K Wegner, Hugo Ceulemans, Djork-Arné Clevert, and Sepp Hochreiter · 2018
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Applications of machine learning in drug discovery and development
Jessica Vamathevan, Dominic Clark, Paul Czodrowski, Ian Dunham, Edgardo Ferran, George Lee, Bin Li, Anant Madabhushi, Parantu Shah, Michaela Spitzer, et al · 2019
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Directional message passing for molecular graphs
Johannes Klicpera, Janek Groß, and Stephan Günnemann · 2019
Later among the works it cites.
Physnet: A neural network for predicting energies, forces, dipole moments, and partial charges
Oliver T Unke and Markus Meuwly · 2019
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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
Later among the works it cites.
Exploration of chemical compound, conformer, and reaction space with meta-dynamics simulations based on tight-binding quantum chemical calculations
Stefan Grimme · 2019
Later among the works it cites.
Bayesian optimization for conformer generation
Lucian Chan, Geoffrey R Hutchison, and Garrett M Morris · 2019
Later among the works it cites.
Coarse-graining auto-encoders for molecular dynamics
Wujie Wang and Rafael Gómez-Bombarelli · 2019
Later among the works it cites.
A deep learning approach to antibiotic discovery
Jonathan M Stokes, Kevin Yang, Kyle Swanson, Wengong Jin, Andres Cubillos-Ruiz, Nina M Donghia, Craig R MacNair, Shawn French, Lindsey A Carfrae, Zohar Bloom-Ackerman, et al · 2020
Closest in time.
From machine learning to deep learning: Advances in scoring functions for protein–ligand docking
Chao Shen, Junjie Ding, Zhe Wang, Dongsheng Cao, Xiaoqin Ding, and Tingjun Hou · 2020
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Transferable multi-level attention neural network for accurate prediction of quantum chemistry properties via multi-task learning
Ziteng Liu, Liqiang Lin, Qingqing Jia, Zheng Cheng, Yanyan Jiang, Yanwen Guo, and Jing Ma · 2020
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Clinical course and outcomes of critically ill patients with SARS-CoV-2 pneumonia in Wuhan, China: a single-centered, retrospective, observational study
Xiaobo Yang, Yuan Yu, Jiqian Xu, Huaqing Shu, Hong Liu, Yongran Wu, Lu Zhang, Zhui Yu, Minghao Fang, Ting Yu, et al · 2020
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GEOM: Energy-annotated molecular conformations for property prediction and molecular generation
Simon Axelrod and Rafael Gomez-Bombarelli · 2020
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https://github.com/chemprop/chemprop
Chemprop Machine Learning for Molecular Property Prediction · 2020
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Benchmarking 2d/3d/md-qsar models for imatinib derivatives: How far can we predict?
Phyo Phyo Kyaw Zin, Alexandre Borrel, and Denis Fourches · 2020
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Identification of inhibitors of SARS-CoV-2 in-vitro cellular toxicity in human (Caco-2) cells using a large scale drug repurposing collection
Bernhard Ellinger, Denisa Bojkova, Andrea Zaliani, Jindrich Cinatl, Carsten Claussen, Sandra Westhaus, Jeanette Reinshagen, Maria Kuzikov, Markus Wolf, Gerd Geisslinger, et al · 2020
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In vitro screening of a FDA approved chemical library reveals potential inhibitors of SARS-CoV-2 replication
Franck Touret, Magali Gilles, Karine Barral, Antoine Nougairède, Etienne Decroly, Xavier de Lamballerie, and Bruno Coutard · 2020
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Accessed: 2020-05-22
https://www.aicures.mit.edu/data , 2020 · 2020
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Nelfinavir was predicted to be a potential inhibitor of 2019-nCov main protease by an integrative approach combining homology modelling, molecular docking and binding free energy calculation
Zhijian Xu, Cheng Peng, Yulong Shi, Zhengdan Zhu, Kaijie Mu, Xiaoyu Wang, and Weiliang Zhu · 2020
Closest in time.
Making Graph Neural Networks Worth It for Low-Data Molecular Machine Learning
Aneesh Pappu and Brooks Paige · 2020
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Temperature-transferable coarse-graining of ionic liquids with dual graph convolutional neural networks
Jurgis Ruza, Wujie Wang, Daniel Schwalbe-Koda, Simon Axelrod, William H Harris, and Rafael Gómez-Bombarelli · 2020
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https://github.com/hyperopt/hyperopt
Distributed Asynchronous Hyperparameter Optimization in Python · 2020
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ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost
J. S. Smith, O. Isayev, and A. E. Roitberg · 2041
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