Deep reinforcement learning for de novo drug design
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[1] Richard E. Turner,et al. Sequence Tutor: Conservative Fine-Tuning of Sequence Generation Models with KL-control , 2016, ICML.
[2] Matthew E Welsch,et al. Privileged scaffolds for library design and drug discovery. , 2010, Current opinion in chemical biology.
[3] S. Pudenz,et al. The Use of Hasse Diagrams as a Potential Approach for Inverse QSAR , 2001, SAR and QSAR in environmental research.
[4] Juan Carlos Fernández,et al. Multiobjective evolutionary algorithms to identify highly autocorrelated areas: the case of spatial distribution in financially compromised farms , 2014, Ann. Oper. Res..
[5] Tristan Deleu,et al. Learning Operations on a Stack with Neural Turing Machines , 2016, ArXiv.
[6] Richard S. Sutton,et al. Multi-step Reinforcement Learning: A Unifying Algorithm , 2017, AAAI.
[7] J. Hendler,et al. Science Magazine , 2009 .
[8] James B. Morris. Formal Languages and their Relation to Automata , 1970 .
[9] Alán Aspuru-Guzik,et al. Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules , 2016, ACS central science.
[10] Alexandre Varnek,et al. Estimation of the size of drug-like chemical space based on GDB-17 data , 2013, Journal of Computer-Aided Molecular Design.
[11] Sergey Plis,et al. Deep Learning Applications for Predicting Pharmacological Properties of Drugs and Drug Repurposing Using Transcriptomic Data. , 2016, Molecular pharmaceutics.
[12] Mario Cazzola,et al. A gain-of-function mutation of JAK2 in myeloproliferative disorders. , 2005, The New England journal of medicine.
[13] Phil Blunsom,et al. Learning to Transduce with Unbounded Memory , 2015, NIPS.
[14] Ricardo Macarron,et al. Critical review of the role of HTS in drug discovery. , 2006, Drug discovery today.
[15] Petra Schneider,et al. De Novo Design at the Edge of Chaos. , 2016, Journal of medicinal chemistry.
[16] Ryan G. Coleman,et al. ZINC: A Free Tool to Discover Chemistry for Biology , 2012, J. Chem. Inf. Model..
[17] Thomas Blaschke,et al. Molecular de-novo design through deep reinforcement learning , 2017, Journal of Cheminformatics.
[18] Gisbert Schneider,et al. Deep Learning in Drug Discovery , 2016, Molecular informatics.
[19] David Ryan Koes,et al. Protein-Ligand Scoring with Convolutional Neural Networks , 2016, Journal of chemical information and modeling.
[20] Emilio Benfenati,et al. SMILES in QSPR/QSAR Modeling: results and perspectives. , 2007, Current drug discovery technologies.
[21] Maria F. Sassano,et al. Automated design of ligands to polypharmacological profiles , 2012, Nature.
[22] Emilio Benfenati,et al. SMILES as an alternative to the graph in QSAR modelling of bee toxicity , 2007, Comput. Biol. Chem..
[23] Alán Aspuru-Guzik,et al. Optimizing distributions over molecular space. An Objective-Reinforced Generative Adversarial Network for Inverse-design Chemistry (ORGANIC) , 2017 .
[24] Gisbert Schneider,et al. Active-learning strategies in computer-assisted drug discovery. , 2015, Drug discovery today.
[25] J. Reymond. The chemical space project. , 2015, Accounts of chemical research.
[26] C. Lipinski. Lead- and drug-like compounds: the rule-of-five revolution. , 2004, Drug discovery today. Technologies.
[27] Tony Y Zhang,et al. Process chemistry: The science, business, logic, and logistics. , 2006, Chemical reviews.
[28] Heinz-Ulrich Schmitt,et al. Results and perspectives , 2005, Belligerent Reprisals.
[29] Marwin H. S. Segler,et al. Modelling Chemical Reasoning to Predict Reactions , 2016, Chemistry.
[30] Geoffrey E. Hinton,et al. Visualizing Data using t-SNE , 2008 .
[31] J S Smith,et al. ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost , 2016, Chemical science.
[32] Johann Gasteiger,et al. A Graph-Based Genetic Algorithm and Its Application to the Multiobjective Evolution of Median Molecules , 2004, J. Chem. Inf. Model..
[33] Allen D. Roses,et al. Pharmacogenetics in drug discovery and development: a translational perspective , 2008, Nature Reviews Drug Discovery.
[34] Thierry Kogej,et al. Generating Focussed Molecule Libraries for Drug Discovery with Recurrent Neural Networks , 2017, ArXiv.
[35] G. Bemis,et al. The properties of known drugs. 1. Molecular frameworks. , 1996, Journal of medicinal chemistry.
[36] Wolfgang Guba,et al. Recent developments in de novo design and scaffold hopping. , 2008, Current opinion in drug discovery & development.
[37] Kenta Hongo,et al. Bayesian molecular design with a chemical language model , 2017, Journal of Computer-Aided Molecular Design.
[38] Jason Yosinski,et al. Deep neural networks are easily fooled: High confidence predictions for unrecognizable images , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[39] Peter Ertl,et al. Estimation of synthetic accessibility score of drug-like molecules based on molecular complexity and fragment contributions , 2009, J. Cheminformatics.
[40] George Papadatos,et al. The ChEMBL bioactivity database: an update , 2013, Nucleic Acids Res..
[41] R. M. Owen,et al. An analysis of the attrition of drug candidates from four major pharmaceutical companies , 2015, Nature Reviews Drug Discovery.
[42] Yanli Wang,et al. PubChem BioAssay: 2017 update , 2016, Nucleic Acids Res..
[43] Marina Krakovsky. Reinforcement renaissance , 2016, Commun. ACM.
[44] Vijay S. Pande,et al. SIML: A Fast SIMD Algorithm for Calculating LINGO Chemical Similarities on GPUs and CPUs , 2010, J. Chem. Inf. Model..
[45] Joan Bruna,et al. Intriguing properties of neural networks , 2013, ICLR.
[46] Michael S Lajiness,et al. Assessment of the consistency of medicinal chemists in reviewing sets of compounds. , 2004, Journal of medicinal chemistry.
[47] C. Krittanawong,et al. Artificial Intelligence in Precision Cardiovascular Medicine. , 2017, Journal of the American College of Cardiology.
[48] Igor V. Tetko,et al. How Accurately Can We Predict the Melting Points of Drug-like Compounds? , 2014, J. Chem. Inf. Model..
[49] Minoru Sakatsume,et al. The Jak Kinases Differentially Associate with the and (Accessory Factor) Chains of the Interferon Receptor to Form a Functional Receptor Unit Capable of Activating STAT Transcription Factors (*) , 1995, The Journal of Biological Chemistry.
[50] K. Johnson. An Update. , 1984, Journal of food protection.
[51] P Schneider,et al. Multi-objective active machine learning rapidly improves structure–activity models and reveals new protein–protein interaction inhibitors† †Electronic supplementary information (ESI) available: Details about computational comparisons and all screening results. See DOI: 10.1039/c5sc04272k , 2016, Chemical science.
[52] Noel M. O'Boyle. Towards a Universal SMILES representation - A standard method to generate canonical SMILES based on the InChI , 2012, Journal of Cheminformatics.
[53] Andrey Kazennov,et al. The cornucopia of meaningful leads: Applying deep adversarial autoencoders for new molecule development in oncology , 2016, Oncotarget.
[54] Geoffrey E. Hinton,et al. Generating Text with Recurrent Neural Networks , 2011, ICML.
[55] H. Jaap van den Herik,et al. Games solved: Now and in the future , 2002, Artif. Intell..
[56] Fei-Fei Li,et al. Visualizing and Understanding Recurrent Networks , 2015, ArXiv.
[57] Gisbert Schneider,et al. Computer-based de novo design of drug-like molecules , 2005, Nature Reviews Drug Discovery.
[58] Jeffrey Dean,et al. Distributed Representations of Words and Phrases and their Compositionality , 2013, NIPS.
[59] P. Dineen,et al. Now and in the future. , 1970, AORN journal.
[60] Sung Jin Cho,et al. Rational Combinatorial Library Design. 2. Rational Design of Targeted Combinatorial Peptide Libraries Using Chemical Similarity Probe and the Inverse QSAR Approaches , 1998, J. Chem. Inf. Comput. Sci..
[61] Jürgen Schmidhuber,et al. Long Short-Term Memory , 1997, Neural Computation.
[62] F. Lombardo,et al. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. , 2001, Advanced drug delivery reviews.
[63] Pierre Baldi,et al. Deep Architectures and Deep Learning in Chemoinformatics: The Prediction of Aqueous Solubility for Drug-Like Molecules , 2013, J. Chem. Inf. Model..
[64] Tomas Mikolov,et al. Inferring Algorithmic Patterns with Stack-Augmented Recurrent Nets , 2015, NIPS.
[65] Ronald J. Williams,et al. Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning , 2004, Machine Learning.
[66] Yoshua Bengio,et al. Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling , 2014, ArXiv.
[67] Demis Hassabis,et al. Mastering the game of Go with deep neural networks and tree search , 2016, Nature.
[68] E. Emanuel,et al. The End of Radiology? Three Threats to the Future Practice of Radiology. , 2016, Journal of the American College of Radiology : JACR.
[69] Vijay S. Pande,et al. Low Data Drug Discovery with One-Shot Learning , 2016, ACS central science.
[70] Alexandre Tkatchenko,et al. Quantum-chemical insights from deep tensor neural networks , 2016, Nature Communications.
[71] Thierry Kogej,et al. Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks , 2017, ACS central science.
[72] Alexander Tropsha,et al. Best Practices for QSAR Model Development, Validation, and Exploitation , 2010, Molecular informatics.
[73] J. Dearden,et al. QSAR modeling: where have you been? Where are you going to? , 2014, Journal of medicinal chemistry.
[74] Alexander Tropsha,et al. Trust, But Verify: On the Importance of Chemical Structure Curation in Cheminformatics and QSAR Modeling Research , 2010, J. Chem. Inf. Model..
[75] Jonas Boström,et al. Computational chemistry-driven decision making in lead generation. , 2006, Drug discovery today.
[76] Hiromasa Kaneko,et al. Inverse QSPR/QSAR Analysis for Chemical Structure Generation (from y to x) , 2016, J. Chem. Inf. Model..
[77] A. Hopkins,et al. Navigating chemical space for biology and medicine , 2004, Nature.