Planning chemical syntheses with deep neural networks and symbolic AI
暂无分享,去创建一个
[1] Frank Neese,et al. The ORCA program system , 2012 .
[2] David Rogers,et al. Extended-Connectivity Fingerprints , 2010, J. Chem. Inf. Model..
[3] Miguel A. Sierra,et al. Dead Ends and Detours En Route to Total Syntheses of the 1990s , 2000 .
[4] G. Schneider,et al. Enabling future drug discovery by de novo design , 2011 .
[5] Marwin H. S. Segler,et al. Neural-Symbolic Machine Learning for Retrosynthesis and Reaction Prediction. , 2017, Chemistry.
[6] Kevin P. Murphy,et al. Machine learning - a probabilistic perspective , 2012, Adaptive computation and machine learning series.
[7] Kimito Funatsu,et al. SOPHIA, a Knowledge Base-Guided Reaction Prediction System - Utilization of a Knowledge Base Derived from a Reaction Database , 1995, J. Chem. Inf. Comput. Sci..
[8] Robert P. Sheridan,et al. Time-Split Cross-Validation as a Method for Estimating the Goodness of Prospective Prediction , 2013, J. Chem. Inf. Model..
[9] Joshua B. Tenenbaum,et al. Building machines that learn and think like people , 2016, Behavioral and Brain Sciences.
[10] Alexandre Varnek,et al. Structure–reactivity modeling using mixture-based representation of chemical reactions , 2017, Journal of Computer-Aided Molecular Design.
[11] Amos J. Storkey,et al. Training Deep Convolutional Neural Networks to Play Go , 2015, ICML.
[12] Robert Robinson,et al. LXIII.—A synthesis of tropinone , 1917 .
[13] Thierry Kogej,et al. Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks , 2017, ACS central science.
[14] Alán Aspuru-Guzik,et al. Neural Networks for the Prediction of Organic Chemistry Reactions , 2016, ACS central science.
[15] Daniel M. Lowe,et al. Development of a Novel Fingerprint for Chemical Reactions and Its Application to Large-Scale Reaction Classification and Similarity , 2015, J. Chem. Inf. Model..
[16] Daniel M. Lowe,et al. Corrections to "Development of a Novel Fingerprint for Chemical Reactions and Its Application to Large-Scale Reaction Classification and Similarity" , 2015, J. Chem. Inf. Model..
[17] Darko Butina,et al. Unsupervised Data Base Clustering Based on Daylight's Fingerprint and Tanimoto Similarity: A Fast and Automated Way To Cluster Small and Large Data Sets , 1999, J. Chem. Inf. Comput. Sci..
[18] Alexandre Varnek,et al. Automatized Assessment of Protective Group Reactivity: A Step Toward Big Reaction Data Analysis , 2016, J. Chem. Inf. Model..
[19] Qian Peng,et al. Computing organic stereoselectivity - from concepts to quantitative calculations and predictions. , 2016, Chemical Society reviews.
[20] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[21] João Aires-de-Sousa,et al. Machine learning of chemical reactivity from databases of organic reactions , 2009, J. Comput. Aided Mol. Des..
[22] Johann Gasteiger,et al. Structure and reaction based evaluation of synthetic accessibility , 2007, J. Comput. Aided Mol. Des..
[23] Marvin Minsky,et al. A framework for representing knowledge , 1974 .
[24] Christopher D. Rosin,et al. Multi-armed bandits with episode context , 2011, Annals of Mathematics and Artificial Intelligence.
[25] Marwin H. S. Segler,et al. Modelling Chemical Reasoning to Predict Reactions , 2016, Chemistry.
[26] Tim Rocktäschel,et al. End-to-end Differentiable Proving , 2017, NIPS.
[27] G. É. Vléduts,et al. Concerning one system of classification and codification of organic reactions , 1963, Inf. Storage Retr..
[28] Petra Schneider,et al. De Novo Design at the Edge of Chaos. , 2016, Journal of medicinal chemistry.
[29] Cezary Kaliszyk,et al. Monte Carlo Connection Prover , 2016, ArXiv.
[30] Chyouhwa Chen,et al. Building and refining a knowledge base for synthetic organic chemistry via the methodology of inductive and deductive machine learning , 1990, J. Chem. Inf. Comput. Sci..
[31] John Salvatier,et al. Theano: A Python framework for fast computation of mathematical expressions , 2016, ArXiv.
[32] Marwin H. S. Segler,et al. Dehydrogenative TEMPO-Mediated Formation of Unstable Nitrones: Easy Access to N-Carbamoyl Isoxazolines. , 2015, Chemistry.
[33] Ramakrishna Nirogi,et al. Design, Synthesis and Biological Evaluation of Novel Benzopyran Sulfonamide Derivatives as 5-HT6 Receptor Ligands , 2015 .
[34] Frank Glorius,et al. A robustness screen for the rapid assessment of chemical reactions , 2013, Nature Chemistry.
[35] Yang Liu,et al. Route Designer: A Retrosynthetic Analysis Tool Utilizing Automated Retrosynthetic Rule Generation , 2009, J. Chem. Inf. Model..
[36] Mark H. M. Winands,et al. Monte-Carlo Tree Search Solver , 2008, Computers and Games.
[37] Daniel Merkle,et al. Generic Strategies for Chemical Space Exploration , 2013, Int. J. Comput. Biol. Drug Des..
[38] Jürgen Schmidhuber,et al. Training Very Deep Networks , 2015, NIPS.
[39] Cezary Kaliszyk,et al. Monte Carlo Tableau Proof Search , 2017, CADE.
[40] Qing-You Zhang,et al. Structure-Based Classification of Chemical Reactions without Assignment of Reaction Centers , 2005, J. Chem. Inf. Model..
[41] Peter Ertl,et al. Estimation of synthetic accessibility score of drug-like molecules based on molecular complexity and fragment contributions , 2009, J. Cheminformatics.
[42] Piotr Dittwald,et al. Computer-Assisted Synthetic Planning: The End of the Beginning. , 2016, Angewandte Chemie.
[43] Michael G. Hutchings,et al. Route Design in the 21st Century: The ICSYNTH Software Tool as an Idea Generator for Synthesis Prediction , 2015 .
[44] Lars Ruddigkeit,et al. The enumeration of chemical space , 2012 .
[45] Sepp Hochreiter,et al. Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs) , 2015, ICLR.
[46] K. C. Nicolaou,et al. Strategic applications of named reactions in organic synthesis: background and detailed mechanisms , 2005 .
[47] Simon M. Lucas,et al. A Survey of Monte Carlo Tree Search Methods , 2012, IEEE Transactions on Computational Intelligence and AI in Games.
[48] Regina Barzilay,et al. Prediction of Organic Reaction Outcomes Using Machine Learning , 2017, ACS central science.
[49] T. Huynh-Dinh,et al. The logic of chemical synthesis , 1996 .
[50] Csaba Szepesvári,et al. Bandit Based Monte-Carlo Planning , 2006, ECML.
[51] Kevin Warwick,et al. March of the Machines , 1997 .
[52] K. Holyoak,et al. The Oxford handbook of thinking and reasoning , 2012 .
[53] Demis Hassabis,et al. Mastering the game of Go with deep neural networks and tree search , 2016, Nature.
[54] Catherine Caillet,et al. Discovery and structural diversity of the hepatitis C virus NS3/4A serine protease inhibitor series leading to clinical candidate IDX320. , 2015, Bioorganic & medicinal chemistry letters.
[55] Rémi Coulom,et al. Efficient Selectivity and Backup Operators in Monte-Carlo Tree Search , 2006, Computers and Games.
[56] Rémi Coulom,et al. Computing "Elo Ratings" of Move Patterns in the Game of Go , 2007, J. Int. Comput. Games Assoc..
[57] Andreas Dietz,et al. Models, concepts, theories, and formal languages in chemistry and their use as a basis for computer assistance in chemistry , 1994, Journal of chemical information and computer sciences.
[58] Anthony P. F. Cook,et al. Computer‐aided synthesis design: 40 years on , 2012 .
[59] C. Steinbeck,et al. Recent developments of the chemistry development kit (CDK) - an open-source java library for chemo- and bioinformatics. , 2006, Current pharmaceutical design.
[60] Pierre Baldi,et al. Learning to Predict Chemical Reactions , 2011, J. Chem. Inf. Model..
[61] William H. Green,et al. Computer-Assisted Retrosynthesis Based on Molecular Similarity , 2017, ACS central science.
[62] Valerie J. Gillet,et al. Knowledge-Based Approach to de Novo Design Using Reaction Vectors , 2009, J. Chem. Inf. Model..
[63] Richard J Ingham,et al. Organic synthesis: march of the machines. , 2015, Angewandte Chemie.
[64] Mark H. M. Winands,et al. Neural Networks for Video Game AI , 2015 .
[65] Christian Templin,et al. 40 Years on , 2017, European heart journal.
[66] Johann Gasteiger,et al. Computer‐Assisted Planning of Organic Syntheses: The Second Generation of Programs , 1996 .
[67] David Silver,et al. Move Evaluation in Go Using Deep Convolutional Neural Networks , 2014, ICLR.
[68] Thore Graepel,et al. Bayesian pattern ranking for move prediction in the game of Go , 2006, ICML.
[69] Jonathan M. Goodman,et al. The ROBIA Program for Predicting Organic Reactivity , 2006, Journal of Chemical Information and Modeling.
[70] Matthew H Todd,et al. Computer-aided organic synthesis. , 2005, Chemical Society reviews.
[71] Johann Gasteiger,et al. HORACE: An automatic system for the hierarchical classification of chemical reactions , 1994, Journal of chemical information and computer sciences.
[72] Bowen Liu,et al. Retrosynthetic Reaction Prediction Using Neural Sequence-to-Sequence Models , 2017, ACS central science.
[73] Dragos Horvath,et al. Expert System for Predicting Reaction Conditions: The Michael Reaction Case , 2015, J. Chem. Inf. Model..
[74] Thierry Kogej,et al. Generating Focussed Molecule Libraries for Drug Discovery with Recurrent Neural Networks , 2017, ArXiv.
[75] Robert Neilson Boyd,et al. Organic Chemistry 2nd Ed. , 1956 .
[76] Clara D. Christ,et al. Mining Electronic Laboratory Notebooks: Analysis, Retrosynthesis, and Reaction Based Enumeration , 2012, J. Chem. Inf. Model..
[77] S. Segawa,et al. End of the beginning , 1990, Nature.