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Finding synthesis routes for molecules of interest is an essential step in the discovery of new drugs and materials.
“Contrastive Multiview Coding”, 2020
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“A Model to Search for Synthesizable Molecules”, 2019
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“Retrosynthesis Prediction with Conditional Graph Logic Network”, 2020
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“A Graph to Graphs Framework for Retrosynthesis Prediction”, 2020, pp. 10
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“The Role of the Medicinal Chemist in Drug Discovery — Then and Now”
Joseph. Lombardino and John. Lowe · 2004
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Sai Gottipati et al · 2004
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“Learning Graph Models for Template-Free Retrosynthesis”, 2020
Vignesh Somnath, Charlotte Bunne, Connor. Coley, Andreas Krause and Regina Barzilay · 2006
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“Dimensionality Reduction by Learning an Invariant Mapping”, 2006, pp. 1735–1742
R. Hadsell, S. Chopra and Y. LeCun · 2006
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“RDKit: Open-Source Cheminformatics”, 2006
Greg Landrum · 2006
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Mikołaj Sacha, Mikołaj Błaż, Piotr Byrski, Paweł Włodarczyk-Pruszyński and Stanisław Jastrzębski · 2006
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“Retro*: Learning Retrosynthetic Planning with Neural Guided A* Search”, 2020
Binghong Chen, Chengtao Li, Hanjun Dai and Le Song · 2006
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“Modern Hopfield Networks and Attention for Immune Repertoire Classification”, 2020
Michael Widrich et al · 2007
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“Hopfield Networks Is All You Need”, 2020
Hubert Ramsauer et al · 2008
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“Contrastive Learning of Medical Visual Representations from Paired Images and Text”, 2020
Yuhao Zhang, Hang Jiang, Yasuhide Miura, Christopher. Manning and Curtis. Langlotz · 2010
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“Extended-Connectivity Fingerprints”
David Rogers and Mathew Hahn · 2010
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“RetroXpert: Decompose Retrosynthesis Prediction like a Chemist”, 2020
Chaochao Yan, Qianggang Ding, Peilin Zhao, Shuangjia Zheng, Jinyu Yang, Yang Yu and Junzhou Huang · 2011
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“Extraction of Chemical Structures and Reactions from the Literature”, 2012
Daniel Lowe · 2012
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“Sequence to sequence learning with neural networks”
Ilya Sutskever, Oriol Vinyals and Quoc Le · 2014
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“Challenges and Opportunities in Computer-Aided Molecular Design”
Lik Ng, Fah Chong and Nishanth. Chemmangattuvalappil · 2015
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“Development of a Novel Fingerprint for Chemical Reactions and Its Application to Large-Scale Reaction Classification and Similarity”
Nadine Schneider, Daniel. Lowe, Roger. Sayle and Gregory. Landrum · 2015
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“Computer-Assisted Synthetic Planning: The End of the Beginning”
Sara Szymkuć, Ewa. Gajewska, Tomasz Klucznik, Karol Molga, Piotr Dittwald, Michał Startek, Michał Bajczyk and Bartosz. Grzybowski · 2016
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“Linking the Neural Machine Translation and the Prediction of Organic Chemistry Reactions”, 2016
Juno Nam and Jurae Kim · 2016
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“Neural Networks for the Prediction of Organic Chemistry Reactions”
Jennifer. Wei, David Duvenaud and Alán Aspuru-Guzik · 2016
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Jimmy Ba, Jamie Kiros and Geoffrey. Hinton · 2016
“RDChiral: An RDKit Wrapper for Handling Stereochemistry in Retrosynthetic Template Extraction and Application”
Connor. Coley, William. Green and Klavs. Jensen · 2019
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“A Graph-Convolutional Neural Network Model for the Prediction of Chemical Reactivity”
Connor W., Wengong Jin, Luke Rogers, Timothy F., Tommi S., William H., Regina Barzilay and Klavs F · 2019
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“Interpretable machine-learning strategy for soft-magnetic property and thermal stability in Fe-based metallic glasses”
Zhichao Lu et al · 2020
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“Current and Future Roles of Artificial Intelligence in Medicinal Chemistry Synthesis”
Thomas. Struble et al · 2020
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“State-of-the-Art Augmented NLP Transformer Models for Direct and Single-Step Retrosynthesis”
Igor. Tetko, Pavel Karpov, Ruud Van and Guillaume Godin · 2020
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“What’s What: The (Nearly) Definitive Guide to Reaction Role Assignment”
Nadine Schneider, Nikolaus Stiefl and Gregory. Landrum · 2016
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“Neural-Symbolic Machine Learning for Retrosynthesis and Reaction Prediction”
Marwin.. Segler and Mark. Waller · 2017
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“Computer-Assisted Retrosynthesis Based on Molecular Similarity”
Connor. Coley, Luke Rogers, William. Green and Klavs. Jensen · 2017
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“Retrosynthetic Reaction Prediction Using Neural Sequence-to-Sequence Models”
Bowen Liu et al · 2017
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“Attention Is All You Need”, 2017
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan. Gomez, Lukasz Kaiser and Illia Polosukhin · 2017
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“Computer-Assisted Retrosynthesis Based on Molecular Similarity”
Connor. Coley, Luke Rogers, William. Green and Klavs. Jensen · 2017
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“Neural Message Passing for Quantum Chemistry”
Justin Gilmer, Samuel. Schoenholz, Patrick. Riley, Oriol Vinyals and George. Dahl · 2017
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“Data Augmentation and Pretraining for Template-Based Retrosynthetic Prediction in Computer-Aided Synthesis Planning”
Michael. Fortunato, Connor. Coley, Brian. Barnes and Klavs. Jensen · 2020
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“Artificial Applicability Labels for Improving Policies in Retrosynthesis Prediction”
Esben Bjerrum, Amol Thakkar and Ola Engkvist · 2020
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“A Simple Framework for Contrastive Learning of Visual Representations”
Ting Chen, Simon Kornblith, Mohammad Norouzi and Geoffrey Hinton · 2020
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“Self-Supervised Learning of Pretext-Invariant Representations”, 2020, pp. 6707–6717
Ishan Misra and Laurens van Maaten · 2020
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“Momentum Contrast for Unsupervised Visual Representation Learning”, 2020, pp. 9729–9738
Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie and Ross Girshick · 2020
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“Energy-Based View of Retrosynthesis”, 2020
Ruoxi Sun, Hanjun Dai, Li Li, Steven Kearnes and Bo Dai · 2020
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“Molecular Graph Enhanced Transformer for Retrosynthesis Prediction”
Kelong Mao, Peilin Zhao, Tingyang Xu, Yu Rong, Xi Xiao and Junzhou Huang · 2020
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“Predicting Retrosynthetic Reactions Using Self-Corrected Transformer Neural Networks”
Shuangjia Zheng, Jiahua Rao, Zhongyue Zhang, Jun Xu and Yuedong Yang · 2020
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“A Bayesian Algorithm for Retrosynthesis”
Zhongliang Guo, Stephen Wu, Mitsuru Ohno and Ryo Yoshida · 2020
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“Data Transfer Approaches to Improve Seq-to-Seq Retrosynthesis”
Katsuhiko Ishiguro, Kazuya Ujihara, R. Sawada, Hirotaka Akita and Masaaki Kotera · 2020
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“Molecular Design in Synthetically Accessible Chemical Space via Deep Reinforcement Learning”
Julien Horwood and Emmanuel Noutahi · 2020
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“On Failure Modes in Molecule Generation and Optimization”
Philipp Renz, Dries Van, Jörg Wegner, Sepp Hochreiter and Günter Klambauer · 2020
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“Learning Transferable Visual Models From Natural Language Supervision”, 2021, pp. 47
Alec Radford et al · 2021
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“Hopfield Networks is All You Need”
Hubert Ramsauer et al · 2021
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“RetroPrime: A Diverse, Plausible and Transformer-Based Method for Single-Step Retrosynthesis Predictions”
Xiaorui Wang, Yuquan Li, Jiezhong Qiu, Guangyong Chen, Huanxiang Liu, Benben Liao, Chang-Yu Hsieh and Xiaojun Yao · 2021
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“Retrosynthesis Prediction Using Grammar-Based Neural Machine Translation: An Information-Theoretic Approach”
Vipul Mann and Venkat Venkatasubramanian · 2021
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“RetCL: A Selection-Based Approach for Retrosynthesis via Contrastive Learning”, 2021
Hankook Lee, Sungsoo Ahn, Seung-Woo Seo, You Song, Sung-Ju Hwang, Eunho Yang and Jinwoo Shin · 2021
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“Single-Step Retrosynthesis Prediction Based on the Identification of Potential Disconnection Sites Using Molecular Substructure Fingerprints”
Haris Hasic and Takashi Ishida · 2021
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“Substructure-based neural machine translation for retrosynthetic prediction”
Umit. Ucak, Taek Kang, Junsu Ko and Juyong Lee · 2021
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