Fetching the paper…
Reading the bibliography…
Computational methods that operate on three-dimensional molecular structure have the potential to solve important questions in biology and chemistry.
Cormorant: Covariant Molecular Neural Networks
Brandon Anderson, Truong-Son Hy, and Risi Kondor · 1906
Earlier work this paper cites.
Dictionary of protein secondary structure: Pattern recognition of hydrogen-bonded and geometrical features
Wolfgang Kabsch and Christian Sander · 1983
Earlier work this paper cites.
SMILES, a Chemical Language and Information System: 1: Introduction to Methodology and Encoding Rules
David Weininger · 1988
Earlier work this paper cites.
Basic local alignment search tool
S F Altschul, W Gish, W Miller, E W Myers, and D J Lipman · 1990
Earlier work this paper cites.
Alanine-stretch scanning mutagenesis: a simple and efficient method to probe protein structure and function
Fabrice Lefèvre, Marie-Hélène Rémy, and Jean-Michel Masson · 1997
Earlier work this paper cites.
The protein data bank
Helen M Berman, John Westbrook, Zukang Feng, Gary Gilliland, Talapady N Bhat, Helge Weissig, Ilya N Shindyalov, and Philip E Bourne · 2000
Earlier work this paper cites.
Glide: a new approach for rapid, accurate docking and scoring. 1. method and assessment of docking accuracy
Richard A Friesner, Jay L Banks, Robert B Murphy, Thomas A Halgren, Jasna J Klicic, Daniel T Mainz, Matthew P Repasky, Eric H Knoll, Mee Shelley, Jason K Perry, et al · 2004
Earlier work this paper cites.
The pdbbind database: Collection of binding affinities for protein-ligand complexes with known three-dimensional structures
Renxiao Wang, Xueliang Fang, Yipin Lu, and Shaomeng Wang · 2004
Earlier work this paper cites.
Altering protein specificity: techniques and applications
Nina M Antikainen and Stephen F Martin · 2005
Earlier work this paper cites.
Scratch: a protein structure and structural feature prediction server
Jianlin Cheng, Arlo Randall, Michael Sweredoski, and Pierre Baldi · 2005
Earlier work this paper cites.
Kernels for small molecules and the prediction of mutagenicity, toxicity and anti-cancer activity
S Joshua Swamidass, Jonathan Chen, Jocelyne Bruand, Peter Phung, Liva Ralaivola, and Pierre Baldi · 2005
Earlier work this paper cites.
One-to four-dimensional kernels for virtual screening and the prediction of physical, chemical, and biological properties
Chloé-Agathe Azencott, Alexandre Ksikes, S Joshua Swamidass, Jonathan H Chen, Liva Ralaivola, and Pierre Baldi · 2007
Earlier work this paper cites.
Scoring function for automated assessment of protein structure template quality
Yang Zhang and Jeffrey Skolnick · 2007
Earlier work this paper cites.
ImageNet: A large-scale hierarchical image database
Jia Deng, Wei Dong, R. Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
Earlier work this paper cites.
Improved prediction of protein side-chain conformations with scwrl4
G. G. Krivov, M. V. Shapovalov, and R. L. Dunbrack · 2009
Earlier work this paper cites.
A machine learning approach to predicting protein–ligand binding affinity with applications to molecular docking
Pedro J Ballester and John B O Mitchell · 2010
Earlier work this paper cites.
Pyrosetta: a script-based interface for implementing molecular modeling algorithms using rosetta
Sidhartha Chaudhury, Sergey Lyskov, and Jeffrey J Gray · 2010
Earlier work this paper cites.
A series of pdb related databases for everyday needs
Robbie Joosten, Tim Beek, Elmar Krieger, Maarten Hekkelman, Rob Hooft, Reinhard Schneider, Chris Sander, and Gert Vriend · 2010
Earlier work this paper cites.
Leave-cluster-out cross-validation is appropriate for scoring functions derived from diverse protein data sets
Christian Kramer and Peter Gedeck · 2010
Earlier work this paper cites.
Rosetta3: an object-oriented software suite for the simulation and design of macromolecules
Andrew Leaver-Fay, Michael Tyka, Steven Lewis, Oliver Lange, James Thompson, Ron Jacak, Kristian Kaufman, P. Renfrew, Colin Smith, Will Sheffler, Ian Davis, Seth Cooper, Adrien Treuille, Daniel Mandell, Florian Richter, Yih-En Ban, Sarel Fleishman, Jacob Corn, David Kim, and Philip Bradley · 2011
Earlier work this paper cites.
Rna-puzzles: a casp-like evaluation of rna three-dimensional structure prediction
José Almeida Cruz, Marc-Frédérick Blanchet, Michal Boniecki, Janusz M Bujnicki, Shi-Jie Chen, Song Cao, Rhiju Das, Feng Ding, Nikolay V Dokholyan, Samuel Coulbourn Flores, et al · 2012
Earlier work this paper cites.
Enumeration of 166 Billion Organic Small Molecules in the Chemical Universe Database GDB-17
Lars Ruddigkeit, Ruud van Deursen, Lorenz C Blum, and Jean-Louis Reymond · 2012
Earlier work this paper cites.
SFCscore(RF): a random forest-based scoring function for improved affinity prediction of protein-ligand complexes
David Zilian and Christoph A Sotriffer · 2013
Earlier work this paper cites.
SCOP2 prototype: A new approach to protein structure mining
Antonina Andreeva, Dave Howorth, Cyrus Chothia, Eugene Kulesha, and Alexey G. Murzin · 2014
Earlier work this paper cites.
Beware of machine learning-based scoring functions-on the danger of developing black boxes
Joffrey Gabel, Jérémy Desaphy, and Didier Rognan · 2014
Earlier work this paper cites.
Comparative assessment of scoring functions on an updated benchmark: 1. compilation of the test set
Yan Li, Zhihai Liu, Jie Li, Li Han, Jie Liu, Zhixiong Zhao, and Renxiao Wang · 2014
Earlier work this paper cites.
Sspro/accpro 5: Almost perfect prediction of protein secondary structure and relative solvent accessibility using profiles, machine learning, and structural similarity
Christophe Magnan and Pierre Baldi · 2014
Cited alongside, same era.
Quantum chemistry structures and properties of 134 kilo molecules
Raghunathan Ramakrishnan, Pavlo O. Dral, Matthias Rupp, and O. Anatole Von Lilienfeld · 2014
Cited alongside, same era.
Convolutional networks on graphs for learning molecular fingerprints
David K Duvenaud, Dougal Maclaurin, Jorge Iparraguirre, Rafael Bombarell, Timothy Hirzel, Alán Aspuru-Guzik, and Ryan P Adams · 2015
Cited alongside, same era.
PDB-wide collection of binding data: current status of the PDBbind database
Zhihai Liu, Yan Li, Li Han, Jie Li, Jie Liu, Zhixiong Zhao, Wei Nie, Yuchen Liu, and Renxiao Wang · 2015
Cited alongside, same era.
The PyMOL molecular graphics system, version 1.8
Schrödinger, LLC · 2015
Cited alongside, same era.
Tensor field networks: Rotation- and translation-equivariant neural networks for 3d point clouds
Nathaniel Thomas, Tess Smidt, Steven M. Kearnes, Lusann Yang, Li Li, Kai Kohlhoff, and Patrick Riley · 2018
Later among the works it cites.
3D steerable CNNs: Learning rotationally equivariant features in volumetric data
Maurice Weiler, Mario Geiger, Max Welling, Wouter Boomsma, and Taco Cohen · 2018
Later among the works it cites.
Moleculenet: a benchmark for molecular machine learning
Zhenqin Wu, Bharath Ramsundar, Evan N. Feinberg, Joseph Gomes, Caleb Geniesse, Aneesh S. Pappu, Karl Leswing, and Vijay Pande · 2018
Later among the works it cites.
Fast graph representation learning with PyTorch Geometric
Matthias Fey and Jan E. Lenssen · 2019
Later among the works it cites.
Deep convolutional neural networks for predicting the quality of single protein structural models
Jie Hou, Renzhi Cao, and Jianlin Cheng · 2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Updates to the integrated protein–protein interaction benchmarks: Docking benchmark version 5 and affinity benchmark version 2
Thom Vreven, Iain H. Moal, Anna Vangone, Brian G. Pierce, Panagiotis L. Kastritis, Mieczyslaw Torchala, Raphael Chaleil, Brian Jiménez-García, Paul A. Bates, Juan Fernandez-Recio, Alexandre M.J.J. Bonvin, and Zhiping Weng · 2015
Cited alongside, same era.
Atomnet: A deep convolutional neural network for bioactivity prediction in structure-based drug discovery, 2015
Izhar Wallach, Michael Dzamba, and Abraham Heifets · 2015
Cited alongside, same era.
Xgboost: A scalable tree boosting system
Tianqi Chen and Carlos Guestrin · 2016
Cited alongside, same era.
Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling · 2016
Cited alongside, same era.
FreeSASA: An open source C library for solvent accessible surface area calculations
Simon Mitternacht · 2016
Cited alongside, same era.
SQuad: 100,000+ questions for machine comprehension of text
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang · 2016
Cited alongside, same era.
The Rosetta All-Atom Energy Function for Macromolecular Modeling and Design
Rebecca F. Alford, Andrew Leaver-Fay, Jeliazko R. Jeliazkov, Matthew J. O’Meara, Frank P. DiMaio, Hahnbeom Park, Maxim V. Shapovalov, P. Douglas Renfrew, Vikram K. Mulligan, Kalli Kappel, Jason W. Labonte, Michael S. Pacella, Richard Bonneau, Philip Bradley, Roland L. Dunbrack, Rhiju Das, David Baker, Brian Kuhlman, Tanja Kortemme, and Jeffrey J. Gray · 2017
Cited alongside, same era.
Generative models for Graph-Based protein design
John Ingraham, Vikas K Garg, Regina Barzilay, and Tommi Jaakkola · 2019
Later among the works it cites.
Skempi 2.0: an updated benchmark of changes in protein–protein binding energy, kinetics and thermodynamics upon mutation
Justina Jankauskaitė, Brian Jiménez-García, Justas Dapkūnas, Juan Fernández-Recio, and Iain H Moal · 2019
Later among the works it cites.
Deepaffinity: interpretable deep learning of compound–protein affinity through unified recurrent and convolutional neural networks
Mostafa Karimi, Di Wu, Zhangyang Wang, and Yang Shen · 2019
Later among the works it cites.
Critical assessment of methods of protein structure prediction (casp)—round xiii
Andriy Kryshtafovych, Torsten Schwede, Maya Topf, Krzysztof Fidelis, and John Moult · 2019
Later among the works it cites.
N-gram graph: Simple unsupervised representation for graphs, with applications to molecules
Shengchao Liu, Mehmet F Demirel, and Yingyu Liang · 2019
Later among the works it cites.
Boltzmann generators: Sampling equilibrium states of many-body systems with deep learning
Frank Noé, Simon Olsson, Jonas Köhler, and Hao Wu · 2019
Later among the works it cites.
Protein model quality assessment using 3d oriented convolutional neural networks
Guillaume Pagès, Benoit Charmettant, and Sergei Grudinin · 2019
Later among the works it cites.
Evaluating protein transfer learning with tape
Roshan Rao, Nicholas Bhattacharya, Neil Thomas, Yan Duan, Peter Chen, John Canny, Pieter Abbeel, and Yun Song · 2019
Later among the works it cites.
Comparative assessment of scoring functions: The CASF-2016 update
Minyi Su, Qifan Yang, Yu Du, Guoqin Feng, Zhihai Liu, Yan Li, and Renxiao Wang · 2019
Later among the works it cites.
End-to-end learning on 3d protein structure for interface prediction
Raphael Townshend, Rishi Bedi, Patricia Suriana, and Ron Dror · 2019
Later among the works it cites.
Compound-protein interaction prediction with end-to-end learning of neural networks for graphs and sequences
Masashi Tsubaki, Kentaro Tomii, and Jun Sese · 2019
Later among the works it cites.
Protein sequence design with a learned potential
Namrata Anand, Raphael R Eguchi, Alexander Derry, Russ B Altman, and Po-Ssu Huang · 2020
Closest in time.
Hierarchical, rotation-equivariant neural networks to predict the structure of protein complexes
Stephan Eismann, Raphael J L Townshend, Nathaniel Thomas, Milind Jagota, Bowen Jing, and Ron Dror · 2020
Closest in time.
Euclidean neural networks: e3nn, 2020
Mario Geiger, Tess Smidt, Alby M., Benjamin Kurt Miller, Wouter Boomsma, Bradley Dice, Kostiantyn Lapchevskyi, Maurice Weiler, Michał Tyszkiewicz, Simon Batzner, Martin Uhrin, Jes Frellsen, Nuri Jung, Sophia Sanborn, Josh Rackers, and Michael Bailey · 2020
Closest in time.
Proteingcn: Protein model quality assessment using graph convolutional networks
Soumya Sanyal, Ivan Anishchenko, Anirudh Dagar, David Baker, and Partha Talukdar · 2020
Closest in time.
Improved protein structure prediction using potentials from deep learning
Andrew W Senior, Richard Evans, John Jumper, James Kirkpatrick, Laurent Sifre, Tim Green, Chongli Qin, Augustin Žídek, Alexander WR Nelson, Alex Bridgland, et al · 2020
Closest in time.
FARFAR2: Improved De Novo Rosetta Prediction of Complex Global RNA Folds
Andrew Martin Watkins, Ramya Rangan, and Rhiju Das · 2020
Closest in time.
Accurate prediction of protein structures and interactions using a three-track neural network
Minkyung Baek, Frank DiMaio, Ivan Anishchenko, Justas Dauparas, Sergey Ovchinnikov, Gyu Rie Lee, Jue Wang, Qian Cong, Lisa N. Kinch, R. Dustin Schaeffer, Claudia Millán, Hahnbeom Park, Carson Adams, Caleb R. Glassman, Andy DeGiovanni, Jose H. Pereira, Andria V. Rodrigues, Alberdina A. Van Dijk, Ana C. Ebrecht, Diederik J. Opperman, Theo Sagmeister, Christoph Buhlheller, Tea Pavkov-Keller, Manoj K. Rathinaswamy, Udit Dalwadi, Calvin K. Yip, John E. Burke, K. Christopher Garcia, Nick V. Grishin, Paul D. Adams, Randy J. Read, and David Baker · 2021
Closest in time.
Highly accurate protein structure prediction with AlphaFold
John Jumper, Richard Evans, Alexander Pritzel, Tim Green, Michael Figurnov, Olaf Ronneberger, Kathryn Tunyasuvunakool, Russ Bates, Augustin Žídek, Anna Potapenko, Alex Bridgland, Clemens Meyer, Simon A.A. Kohl, Andrew J. Ballard, Andrew Cowie, Bernardino Romera-Paredes, Stanislav Nikolov, Rishub Jain, Jonas Adler, Trevor Back, Stig Petersen, David Reiman, Ellen Clancy, Michal Zielinski, Martin Steinegger, Michalina Pacholska, Tamas Berghammer, Sebastian Bodenstein, David Silver, Oriol Vinyals, Andrew W. Senior, Koray Kavukcuoglu, Pushmeet Kohli, and Demis Hassabis · 2021
Closest in time.
GElib — C++/CUDA library for rotation group operations, 2021
Risi Kondor and Erik Henning Thiede · 2021
Closest in time.
Geometric Deep Learning of RNA Structure
Raphael J L Townshend, Stephan Eismann, Andrew M Watkins, Ramya Rangan, Maria Karelina, Rhiju Das, and Ron O Dror · 2021
Closest in time.