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Molecular Representation Learning (MRL) has proven impactful in numerous biochemical applications such as drug discovery and enzyme design.
The Generation of a Unique Machine Description for Chemical Structures — A Technique Developed at Chemical Abstracts Service
H. L. Morgan · 1965
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Development and Application of New Steric Substituent Parameters in Drug Design
A. Verloop, W. Hoogenstraaten, and J. Tipker · 1976
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A New Algorithm for Data Compression
Philip Gage · 1994
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Basic Terminology of Stereochemistry (IUPAC Recommendations 1996)
G. P. Moss · 1996
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Long Short-Term Memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
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Solving the Multiple Instance Problem with Axis-Parallel Rectangles
Thomas G. Dietterich, Richard H. Lathrop, and Tomás Lozano-Pérez · 1997
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A Framework for Multiple-Instance Learning
Oded Maron and Tomás Lozano-Pérez · 1997
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Unsupervised Data Base Clustering Based on Daylight’s Fingerprint and Tanimoto Similarity: A Fast and Automated Way To Cluster Small and Large Data Sets
Darko Butina · 1999
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Toward Reliable Density Functional Methods Without Adjustable Parameters: The PBE0 Model
Carlo Adamo and Vincenzo Barone · 1999
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Random Forests
Leo Breiman · 2001
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Reoptimization of MDL Keys for Use in Drug Discovery
Joseph L. Durant, Burton A. Leland, Douglas R. Henry, and James G. Nourse · 2002
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Conformational Analysis of Drug-Like Molecules Bound to Proteins: An Extensive Study of Ligand Reorganization upon Binding
Emanuele Perola and Paul S. Charifson · 2004
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Circular Fingerprints: Flexible Molecular Descriptors with Applications from Physical Chemistry to ADME
Robert C. Glem, Andreas Bender, Catrin H. Arnby, Lars Carlsson, Scott Boyer, and James Smith · 2006
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Extended-Connectivity Fingerprints
David Rogers and Mathew Hahn · 2010
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Open Babel: An Open Chemical Toolbox
Noel M. O’Boyle, Michael Banck, Craig A. James, Chris Morley, Tim Vandermeersch, and Geoffrey R. Hutchison · 2011
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Scikit-learn: Machine Learning in Python
Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, Jake VanderPlas, Alexandre Passos, David Cournapeau, Matthieu Brucher, Matthieu Perrot, and Edouard Duchesnay · 2011
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Neural Machine Translation by Jointly Learning to Align and Translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio · 2015
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Adam: A Method for Stochastic Optimization
Diederik P. Kingma and Jimmy Ba · 2015
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Predicting the Outcomes of Organic Reactions via Machine Learning: Are Current Descriptors Sufficient?
G. Skoraczyński, P. Dittwald, B. Miasojedow, S. Szymkuć, E. P. Gajewska, B. A. Grzybowski, and A. Gambin · 2017
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Semi-Supervised Classification with Graph Convolutional Networks
Thomas N. Kipf and Max Welling · 2017
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Neural Message Passing for Quantum Chemistry
Justin Gilmer, Samuel S. Schoenholz, Patrick F. Riley, Oriol Vinyals, and George E. Dahl · 2017
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SchNet: A Continuous-Filter Convolutional Neural Network for Modeling Quantum Interactions
Kristof Schütt, Pieter-Jan Kindermans, Huziel Enoc Sauceda Felix, Stefan Chmiela, Alexandre Tkatchenko, and Klaus-Robert Müller · 2017
Cited alongside, same era.
Attention is All You Need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Uszkoreit Kaiser, and Illia Polosukhin · 2017
Cited alongside, same era.
Deep Sets
Manzil Zaheer, Satwik Kottur, Siamak Ravanbakhsh, Barnabás Póczos, Ruslan R. Salakhutdinov, and Alexander J. Smola · 2017
Cited alongside, same era.
Representation Learning on Graphs with Jumping Knowledge Networks
Keyulu Xu, Chengtao Li, Yonglong Tian, Tomohiro Sonobe, Ken-ichi Kawarabayashi, and Stefanie Jegelka · 2018
Cited alongside, same era.
Graph Attention Networks
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio · 2018
Cited alongside, same era.
Machine Learning Meets Volcano Plots: Computational Discovery of Cross-Coupling Catalysts
Applications of Deep Learning in Molecule Generation and Molecular Property Prediction
W. Patrick Walters and Regina Barzilay · 2021
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GemNet: Universal Directional Graph Neural Networks for Molecules
Johannes Gasteiger, Florian Becker, and Stephan Günnemann · 2021
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Equivariant Message Passing for the Prediction of Tensorial Properties and Molecular Spectra
Kristof Schütt, Oliver T. Unke, and Michael Gastegger · 2021
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Machine Learning of Free Energies in Chemical Compound Space Using Ensemble Representations: Reaching Experimental Uncertainty for Solvation
Jan Weinreich, Nicholas J. Browning, and O. Anatole von Lilienfeld · 2021
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Teaching a Neural Network to Attach and Detach Electrons From Molecules
Roman Zubatyuk, Justin S. Smith, Benjamin T. Nebgen, Sergei Tretiak, and Olexandr Isayev · 2021
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Benjamin Meyer, Boodsarin Sawatlon, Stefan Heinen, O. Anatole von Lilienfeld, and Clémence Corminboeuf · 2018
Cited alongside, same era.
Attention-based Deep Multiple Instance Learning
Maximilian Ilse, Jakub M. Tomczak, and Max Welling · 2018
Cited alongside, same era.
Weisfeiler and Leman Go Neural: Higher-Order Graph Neural Networks
Christopher Morris, Martin Ritzert, Matthias Fey, William L. Hamilton, Jan Eric Lenssen, Gaurav Rattan, and Martin Grohe · 2019
Cited alongside, same era.
Deep Learning for the Life Sciences: Applying Deep Learning to Genomics, Microscopy, Drug Discovery, and More
Bharath Ramsundar, Peter Eastman, Patrick Walters, and Vijay Pande · 2019
Cited alongside, same era.
Prediction of Higher-Selectivity Catalysts by Computer-Driven Workflow and Machine Learning
Andrew F. Zahrt, Jeremy J. Henle, Brennan T. Rose, Yang Wang, William T. Darrow, and Scott E. Denmark · 2019
Cited alongside, same era.
Rapid Virtual Screening of Enantioselective Catalysts Using CatVS
Anthony R. Rosales, Jessica Wahlers, Elaine Limé, Rebecca E. Meadows, Kevin W. Leslie, Rhona Savin, Fiona Bell, Eric Hansen, Paul Helquist, Rachel H. Munday, Olaf Wiest, and Per-Ola Norrby · 2019
Cited alongside, same era.
How Powerful Are Graph Neural Networks?
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka · 2019
Cited alongside, same era.
Artificial Intelligence-Enhanced Quantum Chemical Method with Broad Applicability
Peikun Zheng, Roman Zubatyuk, Wei Wu, Olexandr Isayev, and Pavlo O. Dral · 2021
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E(n) Equivariant Graph Neural Networks
Victor Garcia Satorras, Emiel Hoogeboom, and Max Welling · 2021
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SE(3) Equivariant Graph Neural Networks with Complete Local Frames
Weitao Du, He Zhang, Yuanqi Du, Qi Meng, Wei Chen, Nanning Zheng, Bin Shao, and Tie-Yan Liu · 2022
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Learning 3D Representations of Molecular Chirality with Invariance to Bond Rotations
Keir Adams, Lagnajit Pattanaik, and Connor W. Coley · 2022
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GEOM, Energy-Annotated Molecular Conformations for Property Prediction and Molecular Generation
Simon Axelrod and Rafael Gómez-Bombarelli · 2022
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Auto3D: Automatic Generation of the Low-Energy 3D Structures with ANI Neural Network Potentials
Zhen Liu, Tetiana Zubatiuk, Adrian Roitberg, and Olexandr Isayev · 2022
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Auto-QChem: An Automated Workflow for the Generation and Storage of DFT Calculations for Organic Molecules
Andrzej M. Żurański, Jason Y. Wang, Benjamin J. Shields, and Abigail G. Doyle · 2022
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A Comprehensive Discovery Platform for Organophosphorus Ligands for Catalysis
Tobias Gensch, Gabriel dos Passos Gomes, Pascal Friederich, Ellyn Peters, Théophile Gaudin, Robert Pollice, Kjell Jorner, AkshatKumar Nigam, Michael Lindner-D’Addario, Matthew S. Sigman, and Alán Aspuru-Guzik · 2022
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rdkit/rdkit: 2022_03_2 (q1 2022) release, 2022
Greg Landrum, Paolo Tosco, Brian Kelley, Ric, sriniker, gedeck, Riccardo Vianello, NadineSchneider, Eisuke Kawashima, Andrew Dalke, Dan N, David Cosgrove, Brian Cole, Matt Swain, Samo Turk, AlexanderSavelyev, Gareth Jones, Alain Vaucher, Maciej Wójcikowski, Ichiru Take, Daniel Probst, Kazuya Ujihara, Vincent F. Scalfani, guillaume godin, Axel Pahl, Francois Berenger, JLVarjo, strets123, JP, and DoliathGavid · 2022
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Recipe for a General, Powerful, Scalable Graph Transformer
Ladislav Rampášek, Mikhail Galkin, Vijay Prakash Dwivedi, Anh Tuan Luu, Guy Wolf, and Dominique Beaini · 2022
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A Systematic Survey of Chemical Pre-trained Models
Jun Xia, Yanqiao Zhu, Yuanqi Du, and Stan Z. Li · 2023
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The Challenge of Balancing Model Sensitivity and Robustness in Predicting Yields: A Benchmarking Study of Amide Coupling Reactions
Zhen Liu, Yurii S. Moroz, and Olexandr Isayev · 2023
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A New Perspective on Building Efficient and Expressive 3D Equivariant Graph Neural Networks
Weitao Du, Yuanqi Du, Limei Wang, Dieqiao Feng, Guifeng Wang, Shuiwang Ji, Carla Gomes, and Zhi-Ming Ma · 2023
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Multi-State RNA Design with Geometric Multi-Graph Neural Networks
Chaitanya K. Joshi, Arian R. Jamasb, Ramon Viñas, Charles Harris, Simon Mathis, and Pietro Liò · 2023
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Comprehensive Exploration of Graphically Defined Reaction Spaces
Qiyuan Zhao, Sai Mahit Vaddadi, Michael Woulfe, Lawal A. Ogunfowora, Sanjay S. Garimella, Olexandr Isayev, and Brett M. Savoie · 2023
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