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In the last decade, machine learning and artificial intelligence applications have received a significant boost in performance and attention in both academic research and industry.
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Enhancing molecular shape comparison by weighted Gaussian functions
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SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules
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Johnson MA, Maggiora GM · 1990
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Hochreiter S, Schmidhuber J · 1997
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Gradient-based learning applied to document recognition
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Electrostatic and aromatic microdomains within the binding-site crevice of the D2 receptor: contributions of the second membrane-spanning segment
Javitch JA, Ballesteros JA, Chen J, Chiappa V, Simpson MM · 1999
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The protein data bank
Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, et al · 2000
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Variational approximations between mean field theory and the junction tree algorithm
Wiegerinck W · 2000
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Protein flexibility predictions using graph theory
Jacobs DJ, Rader AJ, Kuhn LA, Thorpe MF · 2001
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Approximation with artificial neural networks
Csáji BC · 2001
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Improved protein–ligand docking using GOLD
Verdonk ML, Cole JC, Hartshorn MJ, Murray CW, Taylor RD · 2003
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ESOL: estimating aqueous solubility directly from molecular structure
Delaney JS · 2004
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Functional assays for screening GPCR targets
Thomsen W, Frazer J, Unett D · 2005
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Using GPUs for machine learning algorithms
Steinkraus D, Buck I, Simard P · 2005
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Toward automatic phenotyping of developing embryos from videos
Ning F, Delhomme D, LeCun Y, Piano F, Bottou L, Barbano PE · 2005
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The PDBbind database: methodologies and updates
Wang R, Fang X, Lu Y, Yang CY, Wang S · 2005
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Comprehensive repertoire and phylogenetic analysis of the G protein-coupled receptors in human and mouse
Bjarnadóttir TK, Gloriam DE, Hellstrand SH, Kristiansson H, Fredriksson R, Schiöth HB · 2006
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High performance convolutional neural networks for document processing
Chellapilla K, Puri S, Simard P · 2006
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Dragon software: An easy approach to molecular descriptor calculations
Mauri A, Consonni V, Pavan M, Todeschini R · 2006
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Similarity-based virtual screening using 2D fingerprints
Willett P · 2006
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On the nature of cavities on protein surfaces: application to the identification of drug-binding sites
Nayal M, Honig B · 2006
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Reducing the dimensionality of data with neural networks
Hinton GE, Salakhutdinov RR · 2006
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Matplotlib: A 2D graphics environment
Hunter JD · 2007
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Comparison of shape-matching and docking as virtual screening tools
Hawkins PC, Skillman AG, Nicholls A · 2007
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Crystal structure of the ligand-bound glucagon-like peptide-1 receptor extracellular domain
Runge S, Thøgersen H, Madsen K, Lau J, Rudolph R · 2008
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Exploring structure–selectivity relationships of biogenic amine GPCR antagonists using similarity searching and dynamic compound mapping
Vogt I, Ahmed HE, Auer J, Bajorath J · 2008
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Structural diversity of G protein-coupled receptors and significance for drug discovery
Lagerström MC, Schiöth HB · 2008
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Medicinal chemistry and the molecular operating environment (MOE): application of QSAR and molecular docking to drug discovery
Vilar S, Cozza G, Moro S · 2008
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ImageNet: A large-scale hierarchical image database
Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L · 2009
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Biopython: freely available Python tools for computational molecular biology and bioinformatics
Cock PJ, Antao T, Chang JT, Chapman BA, Cox CJ, Dalke A, et al · 2009
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Large-scale deep unsupervised learning using graphics processors
Raina R, Madhavan A, Ng AY · 2009
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Molecular descriptors for chemoinformatics: volume I: alphabetical listing/volume II: appendices, references. vol. 41
Todeschini R, Consonni V · 2009
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Olfactory perception: receptors, cells, and circuits
Su CY, Menuz K, Carlson JR · 2009
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A survey on transfer learning
Pan SJ, Yang Q · 2009
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How to improve R&D productivity: the pharmaceutical industry’s grand challenge
Paul SM, Mytelka DS, Dunwiddie CT, Persinger CC, Munos BH, Lindborg SR, et al · 2010
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AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading
Trott O, Olson AJ · 2010
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A machine learning approach to predicting protein–ligand binding affinity with applications to molecular docking
Ballester PJ, Mitchell JB · 2010
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Data structures for statistical computing in python
McKinney W, et al · 2010
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Rectified linear units improve restricted boltzmann machines
Nair V, Hinton GE · 2010
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Extended-connectivity fingerprints
Rogers D, Hahn M · 2010
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DSX: a knowledge-based scoring function for the assessment of protein–ligand complexes
Neudert G, Klebe G · 2011
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The NumPy array: a structure for efficient numerical computation
Van Der Walt S, Colbert SC, Varoquaux G · 2011
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Scikit-learn: Machine learning in Python
Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, et al · 2011
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Structural and dynamic determinants of protein-peptide recognition
Dagliyan O, Proctor EA, D’Auria KM, Ding F, Dokholyan NV · 2011
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How chemoproteomics can enable drug discovery and development
Moellering RE, Cravatt BF · 2012
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Lifting the lid on GPCRs: the role of extracellular loops
Wheatley M, Wootten D, Conner MT, Simms J, Kendrick R, Logan RT, et al · 2012
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Multi-column deep neural networks for image classification
Cireşan D, Meier U, Schmidhuber J · 2012
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ImageNet classification with deep convolutional neural networks
Krizhevsky A, Sutskever I, Hinton GE · 2012
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Exploring activity cliffs in medicinal chemistry: miniperspective
Stumpfe D, Bajorath J · 2012
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Directory of useful decoys, enhanced (DUD-E): better ligands and decoys for better benchmarking
Mysinger MM, Carchia M, Irwin JJ, Shoichet BK · 2012
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Performance evaluation of 2D fingerprint and 3D shape similarity methods in virtual screening
Hu G, Kuang G, Xiao W, Li W, Liu G, Tang Y · 2012
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Drug discovery: Chemical beauty contest
Leeson P · 2012
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Are GPCRs still a source of new targets?
Garland SL · 2013
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International Union of Basic and Clinical Pharmacology. LXXXVIII. G protein-coupled receptor list: recommendations for new pairings with cognate ligands
Davenport AP, Alexander SP, Sharman JL, Pawson AJ, Benson HE, Monaghan AE, et al · 2013
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On the difficulty of training recurrent neural networks
Pascanu R, Mikolov T, Bengio Y · 2013
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Interactions of the α \alpha -subunits of heterotrimeric G-proteins with GPCRs, effectors and RGS proteins: a critical review and analysis of interacting surfaces, conformational shifts, structural diversity and electrostatic potentials
Baltoumas FA, Theodoropoulou MC, Hamodrakas SJ · 2013
Cited alongside, same era.
Chemically advanced template search (CATS) for scaffold-hopping and prospective target prediction for ’orphan’ molecules
Reutlinger M, Koch CP, Reker D, Todoroff N, Schneider P, Rodrigues T, et al · 2013
Cited alongside, same era.
Protein–ligand binding site recognition using complementary binding-specific substructure comparison and sequence profile alignment
Yang J, Roy A, Zhang Y · 2013
Cited alongside, same era.
Auto-encoding variational bayes
Kingma DP, Welling M · 2013
Cited alongside, same era.
Adenosine receptors as drug targets–what are the challenges?
Chen JF, Eltzschig HK, Fredholm BB · 2013
Cited alongside, same era.
Enabling the hypothesis-driven prioritization of ligand candidates in big databases: Screenlamp and its application to GPCR inhibitor discovery for invasive species control
Raschka S, Scott AM, Liu N, Gunturu S, Huertas M, Li W, et al · 2018
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Automated inference of chemical discriminants of biological activity
Raschka S, Scott AM, Huertas M, Li W, Kuhn LA · 2018
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Exploring G protein-coupled receptors (GPCRs) ligand space via cheminformatics approaches: impact on rational drug design
Basith S, Cui M, Macalino SJ, Park J, Clavio NA, Kang S, et al · 2018
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Model evaluation, model selection, and algorithm selection in machine learning
Raschka S · 2018
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Recent applications of deep learning and machine intelligence on in silico drug discovery: methods, tools and databases
Rifaioglu AS, Atas H, Martin MJ, Cetin-Atalay R, Atalay V, Dogan T · 2018
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One size does not fit all: the limits of structure-based models in drug discovery
Ross GA, Morris GM, Biggin PC · 2013
Cited alongside, same era.
The ChEMBL bioactivity database: an update
Bento AP, Gaulton A, Hersey A, Bellis LJ, Chambers J, Davies M, et al · 2014
Cited alongside, same era.
A systematic approach to identify novel cancer drug targets using machine learning, inhibitor design and high-throughput screening
Jeon J, Nim S, Teyra J, Datti A, Wrana JL, Sidhu SS, et al · 2014
Cited alongside, same era.
Using information from historical high-throughput screens to predict active compounds
Riniker S, Wang Y, Jenkins JL, Landrum GA · 2014
Cited alongside, same era.
Striving for simplicity: the all convolutional net
Springenberg J, Dosovitskiy A, Brox T, Riedmiller M · 2014
Cited alongside, same era.
Adam: A method for stochastic optimization
Kingma DP, Ba J · 2014
Cited alongside, same era.
Very deep convolutional networks for large-scale image recognition
Simonyan K, Zisserman A · 2014
Cited alongside, same era.
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COACH-D: improved protein–ligand binding sites prediction with refined ligand-binding poses through molecular docking
Wu Q, Peng Z, Zhang Y, Yang J · 2018
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Binding pathway of opiates to μ \mu -opioid receptors revealed by machine learning
Farimani AB, Feinberg E, Pande V · 2018
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Machine learning on human muscle transcriptomic data for biomarker discovery and tissue-specific drug target identification
Mamoshina P, Volosnikova M, Ozerov IV, Putin E, Skibina E, Cortese F, et al · 2018
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MoleculeNet: a benchmark for molecular machine learning
Wu Z, Ramsundar B, Feinberg EN, Gomes J, Geniesse C, Pappu AS, et al · 2018
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Protein–ligand interfaces are polarized: discovery of a strong trend for intermolecular hydrogen bonds to favor donors on the protein side with implications for predicting and designing ligand complexes
Raschka S, Wolf AJ, Bemister-Buffington J, Kuhn LA · 2018
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De novo design of bioactive small molecules by artificial intelligence
Merk D, Friedrich L, Grisoni F, Schneider G · 2018
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Mordred: a molecular descriptor calculator
Moriwaki H, Tian YS, Kawashita N, Takagi T · 2018
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Convolutional neural network based on SMILES representation of compounds for detecting chemical motif
Hirohara M, Saito Y, Koda Y, Sato K, Sakakibara Y · 2018
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WDL-RF: predicting bioactivities of ligand molecules acting with G protein-coupled receptors by combining weighted deep learning and random forest
Wu J, Zhang Q, Wu W, Pang T, Hu H, Chan WK, et al · 2018
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CheMixNet: Mixed DNN architectures for predicting chemical properties using multiple molecular representations
Paul A, Jha D, Al-Bahrani R, Liao Wk, Choudhary A, Agrawal A · 2018
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Automatic chemical design using a data-driven continuous representation of molecules
Gómez-Bombarelli R, Wei JN, Duvenaud D, Hernández-Lobato JM, Sánchez-Lengeling B, Sheberla D, et al · 2018
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A new approach for drug target and bioactivity prediction: The multifingerprint similarity search algorithm (MuSSeL)
Alberga D, Trisciuzzi D, Montaruli M, Leonetti F, Mangiatordi GF, Nicolotti O · 2018
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Machine intelligence decrypts β \beta -lapachone as an allosteric 5-lipoxygenase inhibitor
Rodrigues T, Werner M, Roth J, da Cruz EH, Marques MC, Akkapeddi P, et al · 2018
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K DEEP: protein–ligand absolute binding affinity prediction via 3D-convolutional neural networks
Jiménez J, Skalic M, Martinez-Rosell G, De Fabritiis G · 2018
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Development and evaluation of a deep learning model for protein–ligand binding affinity prediction
Stepniewska-Dziubinska MM, Zielenkiewicz P, Siedlecki P · 2018
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Shufflenet v2: Practical guidelines for efficient cnn architecture design
Ma N, Zhang X, Zheng HT, Sun J · 2018
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Semi-Adversarial Networks: Convolutional autoencoders for imparting privacy to face images
Mirjalili V, Raschka S, Namboodiri A, Ross A · 2018
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Gender privacy: An ensemble of semi adversarial networks for confounding arbitrary gender classifiers
Mirjalili V, Raschka S, Ross A · 2018
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Junction tree variational autoencoder for molecular graph generation
Jin W, Barzilay R, Jaakkola T · 2018
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Graphvae: Towards generation of small graphs using variational autoencoders
Simonovsky M, Komodakis N · 2018
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Syntax-directed variational autoencoder for structured data
Dai H, Tian Y, Dai B, Skiena S, Song L · 2018
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Reinforcement learning: an introduction
Sutton RS, Barto AG · 2018
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Graph convolutional policy network for goal-directed molecular graph generation
You J, Liu B, Ying Z, Pande V, Leskovec J · 2018
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Protein family-specific models using deep neural networks and transfer learning improve virtual screening and highlight the need for more data
Imrie F, Bradley AR, van der Schaar M, Deane CM · 2018
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Using rule-based labels for weak supervised learning: a ChemNet for transferable chemical property prediction
Goh GB, Siegel C, Vishnu A, Hodas N · 2018
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Universal language model fine-tuning for text classification
Howard J, Ruder S · 2018
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Automated discovery of GPCR bioactive ligands
Raschka S · 2019
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Estimation of clinical trial success rates and related parameters
Wong CH, Siah KW, Lo AW · 2019
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Applications of machine learning in drug discovery and development
Vamathevan J, Clark D, Czodrowski P, Dunham I, Ferran E, Lee G, et al · 2019
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Mullard A. 2018 FDA drug approvals · 2019
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Deep learning enables rapid identification of potent DDR1 kinase inhibitors
Zhavoronkov A, Ivanenkov YA, Aliper A, Veselov MS, Aladinskiy VA, Aladinskaya AV, et al · 2019
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Next-Generation Experimentation with Self-Driving Laboratories
Häse F, Roch LM, Aspuru-Guzik A · 2019
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Predicting kinase inhibitors using bioactivity matrix derived informer sets
Zhang H, Ericksen SS, Lee Cp, Ananiev GE, Wlodarchak N, Yu P, et al · 2019
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New binding sites, new opportunities for GPCR drug discovery
Chan HS, Li Y, Dahoun T, Vogel H, Yuan S · 2019
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Rank-consistent ordinal regression for neural networks
Cao W, Mirjalili V, Raschka S · 2019
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Python machine learning: Machine learning and deep learning with Python, scikit-learn, and TensorFlow 2
Raschka S, Mirjalili V · 2019
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PyTorch: An imperative style, high-performance deep learning library
Paszke A, Gross S, Massa F, Lerer A, Bradbury J, Chanan G, et al · 2019
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Interpretable Prediction of Protein-Ligand Interaction by Convolutional Neural Network
Hu F, Jiang J, Yin P · 2019
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committee N. Reproducibility Checklist; · 2019
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The hundred-page machine learning book
Burkov A · 2019
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EfficientNet: rethinking model scaling for convolutional neural networks
Tan M, Le QV · 2019
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Learning drug function from chemical structure with convolutional neural networks and random forests
Meyer JG, Liu S, Miller IJ, Coon JJ, Gitter A · 2019
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Prediction of orthosteric and allosteric regulations on cannabinoid receptors using supervised machine learning classifiers
Bian Y, Jing Y, Wang L, Ma S, Jun JJ, Xie XQ · 2019
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UniProt: a worldwide hub of protein knowledge
Consortium U · 2019
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Machine learning for scent: learning generalizable perceptual representations of small molecules
Sanchez-Lengeling B, Wei JN, Lee BK, Gerkin RC, Aspuru-Guzik A, Wiltschko AB · 2019
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Electrostatic-field and surface-shape similarity for virtual screening and pose prediction
Cleves AE, Johnson SR, Jain AN · 2019
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Dissecting celastrol with machine learning to unveil dark pharmacology
Rodrigues T, de Almeida BP, Barbosa-Morais NL, Bernardes GJ · 2019
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DeepAtom: A Framework for Protein-Ligand Binding Affinity Prediction
Li Y, Rezaei MA, Li C, Li X, Wu D · 2019
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An overview of scoring functions used for protein–ligand interactions in molecular docking
Li J, Fu A, Zhang L · 2019
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Classical scoring functions for docking are unable to exploit large volumes of structural and interaction data
Li H, Peng J, Sidorov P, Leung Y, Leung KS, Wong MH, et al · 2019
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OnionNet: a multiple-layer inter-molecular contact based convolutional neural network for protein-ligand binding affinity prediction
Zheng L, Fan J, Mu Y · 2019
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FlowSAN: privacy-enhancing semi-adversarial networks to confound arbitrary face-based gender classifiers
Mirjalili V, Raschka S, Ross A · 2019
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An exploration strategy improves the diversity of de novo ligands using deep reinforcement learning: a case for the adenosine A 2A receptor
Liu X, Ye K, van Vlijmen HW, IJzerman AP, van Westen GJ · 2019
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Optimization of molecules via deep reinforcement learning
Zhou Z, Kearnes S, Li L, Zare RN, Riley P · 2019
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Approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learning
Smith JS, Nebgen BT, Zubatyuk R, Lubbers N, Devereux C, Barros K, et al · 2019
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Repertoires of G protein-coupled receptors for Ciona-specific neuropeptides
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Food U, Administration D. FDA, editor. What Are Biologics; 2020 · 2020
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Machine learning for target discovery in drug development
Rodrigues T, Bernardes GJ · 2020
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Machine Learning to Identify Flexibility Signatures of Class A GPCR Inhibition
Bemister-Buffington J, Wolf AJ, Raschka S, Kuhn LA · 2020
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Spatiotemporal identification of druggable binding sites using deep learning
Kozlovskii I, Popov P · 2020
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PrivacyNet: semi-adversarial networks for multi-attribute face privacy
Mirjalili V, Raschka S, Ross A · 2020
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Human-in-the-loop machine learning
Munro R · 2020
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Improved protein structure prediction using potentials from deep learning
Senior AW, Evans R, Jumper J, Kirkpatrick J, Sifre L, Green T, et al · 2020
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