Fetching the paper…
Reading the bibliography…
Large molecular representation models pre-trained on massive unlabeled data have shown great success in predicting molecular properties.
Roberta: A robustly optimized BERT pretraining approach
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov · 1907
Earlier work this paper cites.
Huggingface’s transformers: State-of-the-art natural language processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, and Jamie Brew · 1910
Earlier work this paper cites.
Deep ensembles: A loss landscape perspective
Stanislav Fort, Huiyi Hu, and Balaji Lakshminarayanan · 1912
Earlier work this paper cites.
Verification of forecasts expressed in terms of probability
Glemm W. Brier · 1950
Earlier work this paper cites.
The generation of a unique machine description for chemical structures-a technique developed at chemical abstracts service
H. L. Morgan · 1965
Earlier work this paper cites.
A new vector partition of the probability score
Allan H Murphy · 1973
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.
Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods
John Platt et al · 1999
Earlier work this paper cites.
Ensemble methods in machine learning
Thomas G. Dietterich · 2000
Earlier work this paper cites.
A benchmark study on reliable molecular supervised learning via bayesian learning
Doyeong Hwang, Grace Lee, Hanseok Jo, Seyoul Yoon, and Seongok Ryu · 2006
Earlier work this paper cites.
Evaluating predictive uncertainty challenge
Joaquin Quiñonero-Candela, Carl Edward Rasmussen, Fabian Sinz, Olivier Bousquet, and Bernhard Schölkopf · 2006
Earlier work this paper cites.
Strictly proper scoring rules, prediction, and estimation
Tilmann Gneiting and Adrian E Raftery · 2007
Earlier work this paper cites.
The graph neural network model
Franco Scarselli, Marco Gori, Ah Chung Tsoi, Markus Hagenbuchner, and Gabriele Monfardini · 2008
Earlier work this paper cites.
Pubchem: a public information system for analyzing bioactivities of small molecules
Yanli Wang, Jewen Xiao, Tugba O. Suzek, Jian Zhang, Jiyao Wang, and Stephen H. Bryant · 2009
Earlier work this paper cites.
Chemberta: Large-scale self-supervised pretraining for molecular property prediction
Seyone Chithrananda, Gabriel Grand, and Bharath Ramsundar · 2010
Earlier work this paper cites.
Uncertainty in information seeking and retrieval: A study in an academic environment
Sudatta Chowdhury, Forbes Gibb, and Monica Landoni · 2010
Earlier work this paper cites.
Bayesian learning via stochastic gradient langevin dynamics
Max Welling and Yee Whye Teh · 2011
Earlier work this paper cites.
Chembl: a large-scale bioactivity database for drug discovery
Anna Gaulton, Louisa J Bellis, A Patricia Bento, Jon Chambers, Mark Davies, Anne Hersey, Yvonne Light, Shaun McGlinchey, David Michalovich, Bissan Al-Lazikani, et al · 2012
Earlier work this paper cites.
Deep gaussian processes
Andreas C. Damianou and Neil D. Lawrence · 2013
Earlier work this paper cites.
Auto-encoding variational bayes
Diederik P. Kingma and Max Welling · 2014
Earlier work this paper cites.
Dropout: a simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey E. Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
Earlier work this paper cites.
Weight uncertainty in neural networks
Charles Blundell, Julien Cornebise, Koray Kavukcuoglu, and Daan Wierstra · 2015
Earlier work this paper cites.
Variational dropout and the local reparameterization trick
Diederik P. Kingma, Tim Salimans, and Max Welling · 2015
Earlier work this paper cites.
Obtaining well calibrated probabilities using bayesian binning
Mahdi Pakdaman Naeini, Gregory Cooper, and Milos Hauskrecht · 2015
Earlier work this paper cites.
Zinc 15 - ligand discovery for everyone
Teague Sterling and John J. Irwin · 2015
Earlier work this paper cites.
Dropout as a bayesian approximation: Representing model uncertainty in deep learning
Yarin Gal and Zoubin Ghahramani · 2016
Earlier work this paper cites.
Gaussian error linear units (gelus), 2016
Dan Hendrycks and Kevin Gimpel · 2016
Earlier work this paper cites.
Selective classification for deep neural networks
Yonatan Geifman and Ran El-Yaniv · 2017
Earlier work this paper cites.
Neural message passing for quantum chemistry
Justin Gilmer, Samuel S. Schoenholz, Patrick F. Riley, Oriol Vinyals, and George E. Dahl · 2017
Earlier work this paper cites.
On calibration of modern neural networks
Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q. Weinberger · 2017
Earlier work this paper cites.
Semi-supervised classification with graph convolutional networks
Thomas N. Kipf and Max Welling · 2017
Earlier work this paper cites.
Simple and scalable predictive uncertainty estimation using deep ensembles
Balaji Lakshminarayanan, Alexander Pritzel, and Charles Blundell · 2017
Earlier work this paper cites.
Focal loss for dense object detection
Tsung-Yi Lin, Priya Goyal, Ross B. Girshick, Kaiming He, and Piotr Dollár · 2017
Earlier work this paper cites.
Pubchemqc project: A large-scale first-principles electronic structure database for data-driven chemistry
Maho Nakata and Tomomi Shimazaki · 2017
Earlier work this paper cites.
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
Earlier work this paper cites.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin · 2017
Earlier work this paper cites.
Loss surfaces, mode connectivity, and fast ensembling of dnns
Timur Garipov, Pavel Izmailov, Dmitrii Podoprikhin, Dmitry P. Vetrov, and Andrew Gordon Wilson · 2018
Earlier work this paper cites.
Averaging weights leads to wider optima and better generalization
Pavel Izmailov, Dmitrii Podoprikhin, Timur Garipov, Dmitry P. Vetrov, and Andrew Gordon Wilson · 2018
Earlier work this paper cites.
Accurate uncertainties for deep learning using calibrated regression
Volodymyr Kuleshov, Nathan Fenner, and Stefano Ermon · 2018
Earlier work this paper cites.
Improving language understanding with unsupervised learning
Alec Radford, Karthik Narasimhan, Tim Salimans, and Ilya Sutskever · 2018
Earlier work this paper cites.
Evidential deep learning to quantify classification uncertainty
Murat Sensoy, Lance M. Kaplan, and Melih Kandemir · 2018
Earlier work this paper cites.
Tensor field networks: Rotation- and translation-equivariant neural networks for 3d point clouds
Nathaniel Thomas, Tess E. Smidt, Steven Kearnes, Lusann Yang, Li Li, Kai Kohlhoff, and Patrick Riley · 2018
Earlier work this paper cites.
BERT: pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
Earlier work this paper cites.
Fast graph representation learning with PyTorch Geometric
Matthias Fey and Jan E. Lenssen · 2019
Cited alongside, same era.
Bounding box regression with uncertainty for accurate object detection
Yihui He, Chenchen Zhu, Jianren Wang, Marios Savvides, and Xiangyu Zhang · 2019
Cited alongside, same era.
Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2019
Cited alongside, same era.
A simple baseline for bayesian uncertainty in deep learning
Wesley J. Maddox, Pavel Izmailov, Timur Garipov, Dmitry P. Vetrov, and Andrew Gordon Wilson · 2019
Cited alongside, same era.
Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Köpf, Edward Z. Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala · 2019
Cited alongside, same era.
Spherical message passing for 3d graph networks
Yi Liu, Limei Wang, Meng Liu, Xuan Zhang, Bora Oztekin, and Shuiwang Ji · 2021
Later among the works it cites.
Uncertainty baselines: Benchmarks for uncertainty & robustness in deep learning
Zachary Nado, Neil Band, Mark Collier, Josip Djolonga, Michael W. Dusenberry, Sebastian Farquhar, Angelos Filos, Marton Havasi, Rodolphe Jenatton, Ghassen Jerfel, Jeremiah Z. Liu, Zelda Mariet, Jeremy Nixon, Shreyas Padhy, Jie Ren, Tim G. J. Rudner, Yeming Wen, Florian Wenzel, Kevin Murphy, D. Sculley, Balaji Lakshminarayanan, Jasper Snoek, Yarin Gal, and Dustin Tran · 2021
Later among the works it cites.
E(n) equivariant graph neural networks
Victor Garcia Satorras, Emiel Hoogeboom, and Max Welling · 2021
Later among the works it cites.
Evidential deep learning for guided molecular property prediction and discovery
Ava P. Soleimany, Alexander Amini, Samuel Goldman, Daniela Rus, Sangeeta N. Bhatia, and Connor W. Coley · 2021
Later among the works it cites.
Score-based generative modeling through stochastic differential equations
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
A bayesian graph convolutional network for reliable prediction of molecular properties with uncertainty quantification
Seongok Ryu, Yongchan Kwon, and Woo Youn Kim · 2019
Cited alongside, same era.
Distribution calibration for regression
Hao Song, Tom Diethe, Meelis Kull, and Peter A. Flach · 2019
Cited alongside, same era.
SMILES-BERT: large scale unsupervised pre-training for molecular property prediction
Sheng Wang, Yuzhi Guo, Yuhong Wang, Hongmao Sun, and Junzhou Huang · 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.
Correction to analyzing learned molecular representations for property prediction
Kevin Yang, Kyle Swanson, Wengong Jin, Connor Coley, Philipp Eiden, Hua Gao, Angel Guzman-Perez, Timothy Hopper, Brian Kelley, Miriam Mathea, Andrew Palmer, Volker Settels, Tommi Jaakkola, Klavs Jensen, and Regina Barzilay · 2019
Cited alongside, same era.
An overview of overfitting and its solutions
Xue Ying · 2019
Cited alongside, same era.
Bayesian semi-supervised learning for uncertainty-calibrated prediction of molecular properties and active learning
Yao Zhang and Alpha A. Lee · 2019
Cited alongside, same era.
Yang Song, Jascha Sohl-Dickstein, Diederik P. Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole · 2021
Later among the works it cites.
Applications of deep learning in molecule generation and molecular property prediction
W. Patrick Walters and Regina Barzilay · 2021
Later among the works it cites.
Chemberta-2: Towards chemical foundation models
Walid Ahmad, Elana Simon, Seyone Chithrananda, Gabriel Grand, and Bharath Ramsundar · 2022
Later among the works it cites.
E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials
Simon Batzner, Albert Musaelian, Lixin Sun, Mario Geiger, Jonathan P Mailoa, Mordechai Kornbluth, Nicola Molinari, Tess E Smidt, and Boris Kozinsky · 2022
Later among the works it cites.
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
Later among the works it cites.
Geometry-enhanced molecular representation learning for property prediction
Xiaomin Fang, Lihang Liu, Jieqiong Lei, Donglong He, Shanzhuo Zhang, Jingbo Zhou, Fan Wang, Hua Wu, and Haifeng Wang · 2022
Later among the works it cites.
Benchmarking uncertainty quantification for protein engineering
Kevin P. Greenman, Ava Soleimany, and Kevin K Yang · 2022
Later among the works it cites.
Graph self-supervised learning with accurate discrepancy learning
Dongki Kim, Jinheon Baek, and Sung Ju Hwang · 2022
Later among the works it cites.
End-to-end stochastic optimization with energy-based model
Lingkai Kong, Jiaming Cui, Yuchen Zhuang, Rui Feng, B. Aditya Prakash, and Chao Zhang · 2022
Later among the works it cites.
Sparse conditional hidden markov model for weakly supervised named entity recognition
Yinghao Li, Le Song, and Chao Zhang · 2022
Later among the works it cites.
Pre-training molecular graph representation with 3d geometry
Shengchao Liu, Hanchen Wang, Weiyang Liu, Joan Lasenby, Hongyu Guo, and Jian Tang · 2022
Later among the works it cites.
Large-scale chemical language representations capture molecular structure and properties
Jerret Ross, Brian Belgodere, Vijil Chenthamarakshan, Inkit Padhi, Youssef Mroueh, and Payel Das · 2022
Later among the works it cites.
3d infomax improves gnns for molecular property prediction
Hannes Stärk, Dominique Beaini, Gabriele Corso, Prudencio Tossou, Christian Dallago, Stephan Günnemann, and Pietro Lió · 2022
Later among the works it cites.
Galactica: A large language model for science
Ross Taylor, Marcin Kardas, Guillem Cucurull, Thomas Scialom, Anthony Hartshorn, Elvis Saravia, Andrew Poulton, Viktor Kerkez, and Robert Stojnic · 2022
Later among the works it cites.
Equivariant transformers for neural network based molecular potentials
Philipp Thölke and Gianni De Fabritiis · 2022
Later among the works it cites.
Exposing the limitations of molecular machine learning with activity cliffs
Derek van Tilborg, Alisa Alenicheva, and Francesca Grisoni · 2022
Later among the works it cites.
Molecular contrastive learning of representations via graph neural networks
Yuyang Wang, Jianren Wang, Zhonglin Cao, and Amir Barati Farimani · 2022
Later among the works it cites.
Actune: Uncertainty-based active self-training for active fine-tuning of pretrained language models
Yue Yu, Lingkai Kong, Jieyu Zhang, Rongzhi Zhang, and Chao Zhang · 2022
Later among the works it cites.
Gpt-molberta: GPT molecular features language model for molecular property prediction
Suryanarayanan Balaji, Rishikesh Magar, Yayati Jadhav, and Amir Barati Farimani · 2023
Closest in time.
Group selfies: a robust fragment-based molecular string representation
Austin H Cheng, Andy Cai, Santiago Miret, Gustavo Malkomes, Mariano Phielipp, and Alán Aspuru-Guzik · 2023
Closest in time.
A systematic study of key elements underlying molecular property prediction
Jianyuan Deng, Zhibo Yang, Hehe Wang, Iwao Ojima, Dimitris Samaras, and Fusheng Wang · 2023
Closest in time.
Fortuna: A library for uncertainty quantification in deep learning
Gianluca Detommaso, Alberto Gasparin, Michele Donini, Matthias W. Seeger, Andrew Gordon Wilson, and Cédric Archambeau · 2023
Closest in time.
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
Closest in time.
A hitchhiker’s guide to geometric gnns for 3d atomic systems
Alexandre Duval, Simon V. Mathis, Chaitanya K. Joshi, Victor Schmidt, Santiago Miret, Fragkiskos D. Malliaros, Taco Cohen, Pietro Lio, Yoshua Bengio, and Michael M. Bronstein · 2023
Closest in time.
Revisiting deep ensemble for out-of-distribution detection: A loss landscape perspective
Kun Fang, Qinghua Tao, Xiaolin Huang, and Jie Yang · 2023
Closest in time.
Searching for high-value molecules using reinforcement learning and transformers
Raj Ghugare, Santiago Miret, Adriana Hugessen, Mariano Phielipp, and Glen Berseth · 2023
Closest in time.
Clarifying trust of materials property predictions using neural networks with distribution-specific uncertainty quantification
Cameron J Gruich, Varun Madhavan, Yixin Wang, and Bryan R Goldsmith · 2023
Closest in time.
Variational bayesian last layers
James Harrison, John Willes, and Jasper Snoek · 2023
Closest in time.
Towards inference efficient deep ensemble learning
Ziyue Li, Kan Ren, Yifan Yang, Xinyang Jiang, Yuqing Yang, and Dongsheng Li · 2023
Closest in time.
Equiformer: Equivariant graph attention transformer for 3d atomistic graphs
Yi-Lun Liao and Tess E. Smidt · 2023
Closest in time.
Hendrik A. Mehrtens, Alexander Kurz, Tabea-Clara Bucher, and Titus J. Brinker · 2023
Closest in time.
Gotta be SAFE: A new framework for molecular design
Emmanuel Noutahi, Cristian Gabellini, Michael Craig, Jonathan S. C. Lim, and Prudencio Tossou · 2023
Closest in time.
A framework for benchmarking uncertainty in deep regression
Franko Schmähling, Jörg Martin, and Clemens Elster · 2023
Closest in time.
Materials property prediction with uncertainty quantification: A benchmark study
Daniel Varivoda, Rongzhi Dong, Sadman Sadeed Omee, and Jianjun Hu · 2023
Closest in time.
Uncertainty estimation for molecules: Desiderata and methods
Tom Wollschläger, Nicholas Gao, Bertrand Charpentier, Mohamed Amine Ketata, and Stephan Günnemann · 2023
Closest in time.
A systematic survey of chemical pre-trained models
Jun Xia, Yanqiao Zhu, Yuanqi Du, and Stan Z. Li · 2023
Closest in time.
Pre-training via denoising for molecular property prediction
Sheheryar Zaidi, Michael Schaarschmidt, James Martens, Hyunjik Kim, Yee Whye Teh, Alvaro Sanchez-Gonzalez, Peter W. Battaglia, Razvan Pascanu, and Jonathan Godwin · 2023
Closest in time.
Uni-mol: A universal 3d molecular representation learning framework
Gengmo Zhou, Zhifeng Gao, Qiankun Ding, Hang Zheng, Hongteng Xu, Zhewei Wei, Linfeng Zhang, and Guolin Ke · 2023
Closest in time.
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 · 2041
Closest in time.