State-of-the-art augmented NLP transformer models for direct and single-step retrosynthesis

[1]  Artem Cherkasov,et al.  QSAR without borders. , 2020, Chemical Society reviews.

[2]  Regina Barzilay,et al.  Current and Future Roles of Artificial Intelligence in Medicinal Chemistry Synthesis , 2020, Journal of medicinal chemistry.

[3]  Jian Tang,et al.  A Graph to Graphs Framework for Retrosynthesis Prediction , 2020, ICML.

[4]  Igor V. Tetko,et al.  Transformer-CNN: Swiss knife for QSAR modeling and interpretation , 2020, Journal of Cheminformatics.

[5]  Riccardo Petraglia,et al.  Predicting retrosynthetic pathways using transformer-based models and a hyper-graph exploration strategy† , 2020, Chemical science.

[6]  Jianfeng Pei,et al.  Automatic retrosynthetic route planning using template-free models , 2020, Chemical science.

[7]  Brian C. Barnes,et al.  Data Augmentation and Pretraining for Template-Based Retrosynthetic Prediction in Computer-Aided Synthesis Planning , 2020, J. Chem. Inf. Model..

[8]  Le Song,et al.  Retrosynthesis Prediction with Conditional Graph Logic Network , 2020, NeurIPS.

[9]  Jun Xu,et al.  Predicting Retrosynthetic Reactions Using Self-Corrected Transformer Neural Networks , 2019, J. Chem. Inf. Model..

[10]  Regina Barzilay,et al.  Learning to Make Generalizable and Diverse Predictions for Retrosynthesis , 2019, ArXiv.

[11]  Igor V. Tetko,et al.  Augmentation Is What You Need! , 2019, ICANN.

[12]  Taghi M. Khoshgoftaar,et al.  A survey on Image Data Augmentation for Deep Learning , 2019, Journal of Big Data.

[13]  R. Kojima,et al.  Prediction and Interpretable Visualization of Retrosynthetic Reactions Using Graph Convolutional Networks , 2019, J. Chem. Inf. Model..

[14]  Igor V. Tetko,et al.  A Transformer Model for Retrosynthesis , 2019, ICANN.

[15]  Svetha Venkatesh,et al.  Graph Transformation Policy Network for Chemical Reaction Prediction , 2018, KDD.

[16]  Igor V. Tetko,et al.  Synergy Effect between Convolutional Neural Networks and the Multiplicity of SMILES for Improvement of Molecular Prediction , 2018, ArXiv.

[17]  Christopher A. Hunter,et al.  Molecular Transformer: A Model for Uncertainty-Calibrated Chemical Reaction Prediction , 2018, ACS central science.

[18]  Connor W. Coley,et al.  A graph-convolutional neural network model for the prediction of chemical reactivity , 2018, Chemical science.

[19]  Piotr Dittwald,et al.  Efficient Syntheses of Diverse, Medicinally Relevant Targets Planned by Computer and Executed in the Laboratory , 2018 .

[20]  Richard C. Larock,et al.  Comprehensive organic transformations : a guide to functional group preparations , 2018 .

[21]  Igor I. Baskin,et al.  Artificial intelligence in synthetic chemistry: achievements and prospects , 2017 .

[22]  William H. Green,et al.  Computer-Assisted Retrosynthesis Based on Molecular Similarity , 2017, ACS central science.

[23]  Constantine Bekas,et al.  “Found in Translation”: predicting outcomes of complex organic chemistry reactions using neural sequence-to-sequence models† †Electronic supplementary information (ESI) available: Time-split test set and example predictions, together with attention weights, confidence and token probabilities. See DO , 2017, Chemical science.

[24]  Regina Barzilay,et al.  Predicting Organic Reaction Outcomes with Weisfeiler-Lehman Network , 2017, NIPS.

[25]  Mike Preuss,et al.  Planning chemical syntheses with deep neural networks and symbolic AI , 2017, Nature.

[26]  Lukasz Kaiser,et al.  Attention is All you Need , 2017, NIPS.

[27]  Bowen Liu,et al.  Retrosynthetic Reaction Prediction Using Neural Sequence-to-Sequence Models , 2017, ACS central science.

[28]  Marwin H. S. Segler,et al.  Neural-Symbolic Machine Learning for Retrosynthesis and Reaction Prediction. , 2017, Chemistry.

[29]  Regina Barzilay,et al.  Prediction of Organic Reaction Outcomes Using Machine Learning , 2017, ACS central science.

[30]  Esben Jannik Bjerrum,et al.  SMILES Enumeration as Data Augmentation for Neural Network Modeling of Molecules , 2017, ArXiv.

[31]  Juno Nam,et al.  Linking the Neural Machine Translation and the Prediction of Organic Chemistry Reactions , 2016, ArXiv.

[32]  Piotr Dittwald,et al.  Computer-Assisted Synthetic Planning: The End of the Beginning. , 2016, Angewandte Chemie.

[33]  Quoc V. Le,et al.  Sequence to Sequence Learning with Neural Networks , 2014, NIPS.

[34]  Daniel M. Lowe.  Extraction of chemical structures and reactions from the literature , 2012 .

[35]  Yang Liu,et al.  Route Designer: A Retrosynthetic Analysis Tool Utilizing Automated Retrosynthetic Rule Generation , 2009, J. Chem. Inf. Model..

[36]  Igor V. Tetko,et al.  Neural network studies, 1. Comparison of overfitting and overtraining , 1995, J. Chem. Inf. Comput. Sci..

[37]  Gregg D. Wilensky,et al.  Neural Network Studies , 1993 .

[38]  S. Segawa,et al.  End of the beginning , 1990, Nature.

[39]  David Weininger,et al.  SMILES. 2. Algorithm for generation of unique SMILES notation , 1989, J. Chem. Inf. Comput. Sci..

[40]  E. Corey,et al.  The Logic of Chemical Synthesis , 1989 .

[41]  David Weininger,et al.  SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules , 1988, J. Chem. Inf. Comput. Sci..

[42]  E. Corey,et al.  Computer-assisted analysis in organic synthesis. , 1985, Science.

[43]  Hilde van der Togt,et al.  Publisher's Note , 2003, J. Netw. Comput. Appl..

[44]  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..

[45]  Sartaj Aziz.  Achievements and Prospects , 1978 .