 Machine Learning - Main Conferences / Journals
@string{uaii="Uncertainty in Artificial Intelligence (UAI)"}
@string{icml="International Conference on Machine Learning (ICML)"}
@string{nips="Advances in Neural Information Processing Systems (NeurIPS)"}
@string{jmlr="Journal of Machine Learning Research"}
@string{iclr="International Conference on Learning Representations (ICLR)"}
@string{iclrws="International Conference on Learning Representations (ICLR), Workshop Track"}
@string{mlsp="IEEE International Workshop for Machine Learning in Signal Processing"}

 NLP
@string{acl="Proceedings of the Annual Meeting of the Association for Computational Linguistics (ACL)"}
@string{conll="SIGNLL Conference on Computational Natural Language Learning (CoNLL)"}
@string{emnlp="Conference on Empirical Methods in Natural Language Processing (EMNLP)"}
@string{tacl="Transactions of the Association for Computational Linguistics (TACL)"}

 Vision
@string{cvpr="IEEE Conference on Computer Vision and Pattern Recognition (CVPR)"}
@string{ijcv="International Journal of Computer Vision"}
@string{iccv="International Conference on Computer Vision (ICCV)"}
@string{eccv="European Conference on Computer Vision (ECCV)"}
@string{siggraph="Proceedings of SIGGRAPH"}

 Theory
@string{tcs="Theoretical Computer Science"}
@string{soda="SIAM-ACM Symposium on Discrete Algorithms (SODA)"}
@string{focs="IEEE Symposium on Foundations of Computer Science (FOCS)"}
@string{colt="Conference on Learning Theory (COLT)"}
@string{stoc="Symposium on Theory of Computing (STOC)"}
@string{icalp="International Colloquium on Automata, Languages and Programming (ICALP)"}

 Robotics
@string{icra="IEEE International Conference on Robotics and Automation (ICRA)"}

 Statistics
@string{uai="Conference on Uncertainty in Artificial Intelligence (UAI)"}

 Misc AI
@string{jair="Journal or Artificial Intelligence Research"}
@string{aaai="AAAI Conference on Artificial Intelligence"}
@string{aistats="International Conference on Artificial Intelligence and Statistics (AISTATS)"}
@string{kdd="ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)"}
@string{tpami="IEEE Transactions on Pattern Analysis and Machine Intelligence"}
@string{tog="ACM Transactions on Graphics (TOG)"}
@string{mathprog="Mathematical Programming"}
@string{infpl="Information Processing Letters"}
@string{jacm="Journal of the ACM"}
@string{tkdd="ACM Trassactions on Knowledge Discovery from Data (TKDD)"}
@string{icalp="International Colloquium on Automata, Languages, and Programming (ICALP)"}
@string{wads="Workshop on Algorithms and Data Structures (WADS)"}
@string{wadsnew="International Symposium on Algorithms and Data Structures (WADS)"}
@string{vldb="International Conference on Very Large Data Bases (VLDB)"}
@string{wsdm="ACM International Conference on Web Search and Data Mining (WSDM)"}
@string{acc="Annual Allerton Conference on Circuit and System Theory"}
@string{www="Proceedings of the International World Wide Web Conference (WWW)"}
@string{icwsm="International AAAI Conference on Web and Social Media (ICWSM)"}

# More general
@string{arxiv="arXiv Preprint"}
@string{rsc="Royal Society of Chemistry"}
@string{acspub="ACS Publications"}
@string{wol="Wiley Online Library"}
@string{spr="Springer"}
@string{npg="Nature Publishing Group"}
@string{ang="Angew. Chem., Int. Ed."}


@inproceedings{liudeep2022,
  title={{Deep Generalized Schr{\"o}dinger Bridge}},
  author={Liu, Guan-Horng and Chen, Tianrong and So, Oswin and Theodorou, Evangelos},
  booktitle=nips,
  year={2022}
}

@book{rogers2000diffusions,
  title={{Diffusions, Markov Processes and Martingales: Volume 2, It{\^o} Calculus}},
  author={Rogers, L Chris G and Williams, David},
  volume={2},
  year={2000},
  publisher={Cambridge University Press}
}

@article{liu2023learning,
  title={{Learning Diffusion Bridges on Constrained Domains}},
  author={Liu, Xingchao and Wu, Lemeng and Ye, Mao and Liu, qiang},
  journal=iclr,
  year={2023}
}


@article{tong2023conditional,
  title={{Conditional Flow Matching: Simulation-Free Dynamic Optimal Transport}},
  author={Tong, Alexander and Malkin, Nikolay and Huguet, Guillaume and Zhang, Yanlei and Rector-Brooks, Jarrid and Fatras, Kilian and Wolf, Guy and Bengio, Yoshua},
  journal={arXiv preprint arXiv:2302.00482},
  year={2023}
}

@article{fortet1940resolution,
  title={{R{\'e}solution d’un systeme d’{\'e}quations de M. Schr{\"o}dinger}},
  author={Fortet, Robert},
  journal={J. Math. Pure Appl. IX},
  volume={1},
  year={1940}
}

@article{kullback1968probability,
  title={Probability densities with given marginals},
  author={Kullback, Solomon},
  journal={The Annals of Mathematical Statistics},
  volume={39},
  number={4},
  pages={1236--1243},
  year={1968},
  publisher={JSTOR}
}

@article{holdijk2022path,
  title={{Path Integral Stochastic Optimal Control for Sampling Transition Paths}},
  author={Holdijk, Lars and Du, Yuanqi and Hooft, Ferry and Jaini, Priyank and Ensing, Bernd and Welling, Max},
  journal={arXiv preprint arXiv:2207.02149},
  year={2022}
}

@inproceedings{zhang2021path,
  title={{Path Integral Sampler: A Stochastic Control Approach For Sampling}},
  author={Zhang, Qinsheng and Chen, Yongxin},
  booktitle=iclr,
  year={2022}
}

@book{doob1984classical,
  title={{Classical Potential Theory and Its Probabilistic Counterpart}},
  author={Doob, Joseph},
  volume={549},
  year={1984},
  publisher={Springer}
}

@inproceedings{liu2022deep,
  title={{Deep Generalized Schr\"odinger Bridge}},
  author={Liu, Guan-Horng and Chen, Tianrong and So, Oswin and Theodorou, Evangelos A},
  booktitle=nips,
  year={2022}
}

@article{gretton2012kernel,
  title={{A Kernel Two-Sample Test}},
  author={Gretton, Arthur and Borgwardt, Karsten M and Rasch, Malte J and Sch{\"o}lkopf, Bernhard and Smola, Alexander},
  journal=jmlr,
  volume={13},
  year={2012}
}


@article{chen2021optimal,
  title={{Optimal Transport in Systems and Control}},
  author={Chen, Yongxin and Georgiou, Tryphon T and Pavon, Michele},
  journal={Annual Review of Control, Robotics, and Autonomous Systems},
  volume={4},
  year={2021},
  publisher={Annual Reviews}
}

@inproceedings{bunne2022recovering,
  title={{The Schrödinger Bridge between Gaussian Measures has a Closed Form}},
  author={Bunne, Charlotte and Hsieh, Ya-Ping and Cuturi, Marci and Krause, Andreas},
  booktitle=aistats,
  year={2023},
}

@book{nelson1967dynamical,
  title={Dynamical Theories of Brownian Motion},
  author={Nelson, Edward},
  volume={3},
  year={1967},
  publisher={Princeton University Press}
}

@article{santambrogio2017euclidean,
  title={$\{$Euclidean, metric, and {Wasserstein}$\}$ gradient flows: an overview},
  author={Santambrogio, Filippo},
  journal={Bulletin of Mathematical Sciences},
  volume={7},
  number={1},
  year={2017},
  publisher={Springer}
}

@article{santambrogio2015optimal,
  title={{Optimal Transport for Applied Mathematicians}},
  author={Santambrogio, Filippo},
  journal={Birk{\"a}user, NY},
  volume={55},
  number={58-63},
  pages={94},
  year={2015},
  publisher={Springer}
}

@article{yang2020predicting,
  title={Predicting cell lineages using autoencoders and optimal transport},
  author={Yang, Karren Dai and Damodaran, Karthik and Venkatachalapathy, Saradha and Soylemezoglu, Ali C and Shivashankar, GV and Uhler, Caroline},
  journal={PLoS Computational Biology},
  volume={16},
  number={4},
  year={2020},
}

@article{Monge1781,
  author={Monge, Gaspard},
  journal={Histoire de l'Acad{\'e}mie Royale des Sciences},
  pages={666--704},
  title={M{\'e}moire sur la th{\'e}orie des d{\'e}blais et des remblais},
  year={1781}
}

@inproceedings{vaswani2017attention,
  title={{Attention is All you Need}},
  author={Vaswani, Ashish and Shazeer, Noam and Parmar, Niki and Uszkoreit, Jakob and Jones, Llion and Gomez, Aidan N and Kaiser, {\L}ukasz and Polosukhin, Illia},
  booktitle=nips,
  year={2017}
}

@article{yang2018scalable,
  title={{Scalable Unbalanced Optimal Transport using Generative Adversarial Networks}},
  author={Yang, Karren D and Uhler, Caroline},
  journal=iclr,
  year={2019}
}

@article{benamou2016,
  title={Discretization of functionals involving the {Monge--Amp{\'e}re} operator},
  author={Benamou, Jean-David and Carlier, Guillaume and M{\'e}rigot, Quentin and Oudet, Edouard},
  journal={Numerische Mathematik},
  volume={134},
  number={3},
  year={2016},
  publisher={Springer}
}

@inproceedings{krishnan2017structured,
  title={Structured Inference Networks for Nonlinear State Space Models},
  author={Krishnan, Rahul and Shalit, Uri and Sontag, David},
  booktitle={aistats},
  volume={31},
  number={1},
  year={2017}
}

@article{benamou2016augmented,
  title={An augmented Lagrangian approach to Wasserstein gradient flows and applications},
  author={Benamou, Jean-David and Carlier, Guillaume and Laborde, Maxime},
  journal={ESAIM: Proceedings and surveys},
  volume={54},
  pages={1--17},
  year={2016},
  publisher={EDP Sciences}
}

@inproceedings{
ganea2022independent,
title={{Independent {SE}(3)-Equivariant Models for End-to-End Rigid Protein Docking}},
author={Octavian-Eugen Ganea and Xinyuan Huang and Charlotte Bunne and Yatao Bian and Regina Barzilay and Tommi S. Jaakkola and Andreas Krause},
booktitle=iclr,
year={2022},
}


@article{tsaban2022harnessing,
  title={Harnessing protein folding neural networks for peptide-protein docking},
  author={Tsaban, Tomer and Varga, Julia K and Avraham, Orly and Ben-Aharon, Ziv and Khramushin, Alisa and Schueler-Furman, Ora},
  journal={Nature Communications},
  volume={13},
  number={1},
  pages={176},
  year={2022},
}


@inproceedings{hashimoto2016learning,
  title={{Learning Population-Level Diffusions with Generative Recurrent Networks}},
  author={Hashimoto, Tatsunori and Gifford, David and Jaakkola, Tommi},
  booktitle=icml,
  year={2016},
  volume={33}
}

@inproceedings{salimans2018improving,
  title={{Improving GANs Using Optimal Transport}},
  author={Tim Salimans and Han Zhang and Alec Radford and Dimitris Metaxas},
  booktitle=iclr,
  year={2018},
  }

@article{gallouet2017jko,
  title={{A JKO Splitting Scheme for Kantorovich--Fisher--Rao Gradient Flows}},
  author={Gallou{\"e}t, Thomas O and Monsaingeon, Leonard},
  journal={SIAM Journal on Mathematical Analysis},
  volume={49},
  number={2},
  year={2017},
  publisher={SIAM}
}

@article{ramdas2017wasserstein,
  title={{On Wasserstein Two Sample Testing and Related Families of Nonparametric Tests}},
  author={Ramdas, Aaditya and Trillos, Nicol{\'a}s Garc{\'\i}a and Cuturi, Marco},
  journal={Entropy},
  volume={19},
  number={2},
  pages={47},
  year={2017},
  publisher={Multidisciplinary Digital Publishing Institute}
}

@inproceedings{janati2020spatio,
  title={{Spatio-Temporal Alignments: Optimal transport through space and time}},
  author={Janati, Hicham and Cuturi, Marco and Gramfort, Alexandre},
  booktitle=aistats,
  year={2020},
  volume={23}
}

@article{2015-Peyre-siims,
  author={Gabriel Peyr{\'e}},
  title={{Entropic Approximation of Wasserstein Gradient Flows}},
  year={2015},
  journal={SIAM Journal on Imaging Sciences},
  volume={8},
  number={4},
}

@inproceedings{cuturi2013sinkhorn,
  title={{Sinkhorn Distances: Lightspeed Computation of Optimal Transport}},
  author={Cuturi, Marco},
  booktitle=nips,
  year={2013},
  volume={26}
}

@inproceedings{pytorch2019paszke,
  title={{PyTorch: An Imperative Style, High-Performance Deep Learning Library}},
  author={Paszke, Adam and Gross, Sam and Massa, Francisco and Lerer, Adam and Bradbury, James and Chanan, Gregory and Killeen, Trevor and Lin, Zeming and Gimelshein, Natalia and Antiga, Luca and Desmaison, Alban and Kopf, Andreas and Yang, Edward and DeVito, Zachary and Raison, Martin and Tejani, Alykhan and Chilamkurthy, Sasank and Steiner, Benoit and Fang, Lu and Bai, Junjie and Chintala, Soumith},
  booktitle=nips,
  year={2019}
}

@article{liaw2018tune,
    title={{Tune: A Research Platform for Distributed Model Selection and Training}},
    author={Liaw, Richard and Liang, Eric and Nishihara, Robert and Moritz, Philipp and Gonzalez, Joseph E and Stoica, Ion},
    journal={arXiv preprint arXiv:1807.05118},
    year={2018}
}

@article{luo2020multiple,
  title={Multiple object tracking: A literature review},
  author={Luo, Wenhan and Xing, Junliang and Milan, Anton and Zhang, Xiaoqin and Liu, Wei and Kim, Tae-Kyun},
  journal={Artificial Intelligence},
  pages={103448},
  year={2020},
  publisher={Elsevier}
}

@inproceedings{rezende2015variational,
  title={{Variational Inference with Normalizing Flows}},
  author={Rezende, Danilo and Mohamed, Shakir},
  booktitle=icml,
  year={2015}
}

@article{sigrist2015stochastic,
  title={Stochastic partial differential equation based modelling of large space--time data sets},
  author={Sigrist, Fabio and K{\"u}nsch, Hans R and Stahel, Werner A},
  journal={Journal of the Royal Statistical Society: Series B: Statistical Methodology},
  pages={3--33},
  year={2015},
  publisher={JSTOR}
}

@inproceedings{de2019stochastic,
  title={{Stochastic Deep Networks}},
  author={De Bie, Gwendoline and Peyr{\'e}, Gabriel and Cuturi, Marco},
  booktitle=icml,
  year={2019},
  volume={36}
}

@inproceedings{jing2022torsional,
  title={{Torsional Diffusion for Molecular Conformer Generation}},
  author={Jing, Bowen and Corso, Gabriele and Chang, Jeffrey and Barzilay, Regina and Jaakkola, Tommi},
  booktitle=nips,
  year={2022}
}

@inproceedings{corso2022diffdock,
  title={{Diffusion Steps, Twists, and Turns for Molecular Docking}},
  author={Corso, Gabriele and St{\"a}rk, Hannes and Jing, Bowen and Barzilay, Regina and Jaakkola, Tommi},
  booktitle=iclr,
  year={2023}
}

@article{Peyre2019computational,
   title={{Computational Optimal Transport}},
   volume={11},
   number={5-6},
   journal={Foundations and Trends in Machine Learning},
   publisher={Now Publishers},
   author={Peyré, Gabriel and Cuturi, Marco},
   year={2019}
}

@article{stathias2018drug,
  title={Drug and disease signature integration identifies synergistic combinations in glioblastoma},
  author={Stathias, Vasileios and Jermakowicz, Anna M and Maloof, Marie E and Forlin, Michele and Walters, Winston and Suter, Robert K and Durante, Michael A and Williams, Sion L and Harbour, J William and Volmar, Claude-Henry and others},
  journal={Nature Communications},
  volume={9},
  number={1},
  year={2018},
  publisher={Nature Publishing Group}
}

@inproceedings{feydy2018interpolating,
  title={{Interpolating between Optimal Transport and MMD using Sinkhorn Divergences}},
  author={Feydy, Jean and S{\'e}journ{\'e}, Thibault and Vialard, Fran{\c{c}}ois-Xavier and Amari, Shun-Ichi and Trouv{\'e}, Alain and Peyr{\'e}, Gabriel},
  booktitle=aistats,
  year={2019},
  volume={22}
}

@inproceedings{genevay2018,
  title={{Sample Complexity of Sinkhorn Divergences}},
  author={Genevay, Aude and Chizat, L{\'e}naic and Bach, Francis and Cuturi, Marco and Peyr{\'e}, Gabriel},
  booktitle=aistats,
  year={2019},
  volume={22}
}

@misc{jax2018github,
  author={James Bradbury and Roy Frostig and Peter Hawkins and Matthew James Johnson and Chris Leary and Dougal Maclaurin and George Necula and Adam Paszke and Jake Vander{P}las and Skye Wanderman-{M}ilne and Qiao Zhang},
  title={{JAX}: composable transformations of {P}ython+{N}um{P}y programs},
  url={http://github.com/google/jax},
  version={0.2.5},
  year={2018},
}

@article{Brenier1987,
  title={D{\'e}composition polaire et r{\'e}arrangement monotone des champs de vecteurs},
  author={Brenier, Yann},
  journal={CR Acad. Sci. Paris S{\'e}r. I Math.},
  volume={305},
  year={1987}
}

@inproceedings{salim2020,
  title={{The Wasserstein Proximal Gradient Algorithm}},
  author={Salim, Adil and Korba, Anna and Luise, Giulia},
  booktitle=nips,
  volume={33},
  year={2020}
}

@inproceedings{metz2016unrolled,
  title={{Unrolled Generative Adversarial Networks}},
  author={Metz, Luke and Poole, Ben and Pfau, David and Sohl-Dickstein, Jascha},
  booktitle=iclr,
  year={2017}
}

@article{jordan1998variational,
  author={Jordan, Richard and Kinderlehrer, David and Otto, Felix},
  title={{The Variational Formulation of the Fokker--Planck Equation}},
  journal={SIAM Journal on Mathematical Analysis},
  year={1998},
  volume={29},
  number={1},
  publisher={SIAM}
}

@article{carrillo2021primal,
  title={{Primal Dual Methods for Wasserstein Gradient Flows}},
  author={Carrillo, Jose A and Craig, Katy and Wang, Li and Wei, Chaozhen},
  journal={Foundations of Computational Mathematics},
  year={2021},
  publisher={Springer}
}

@book{ambrosio2006gradient,
  title={{Gradient Flows in Metric Spaces and in the Space of Probability Measures}},
  author={Ambrosio, L. and Gigli, N. and Savar{\'e}, G.},
  year={2006},
  publisher={Springer}
}

@article{cucker2007a,
  author={Cucker, Felipe and Smale, Steve},
  journal={IEEE Transactions on Automatic Control}, 
  title={{Emergent Behavior in Flocks}}, 
  year={2007},
  volume={52},
  number={5},
}

@article{cucker2007b,
  title={On the mathematics of emergence},
  author={Cucker, Felipe and Smale, Steve},
  journal={Japanese Journal of Mathematics},
  volume={2},
  number={1},
  year={2007},
  publisher={Springer}
}

@article{takahashi2006,
  title={{Induction of Pluripotent Stem Cells from Mouse Embryonic and Adult Fibroblast Cultures by Defined Factors}},
  author={Takahashi, Kazutoshi and Yamanaka, Shinya},
  journal={Cell},
  volume={126},
  number={4},
  year={2006},
  publisher={Elsevier}
}

@article{takahashi2016,
  title={A decade of transcription factor-mediated reprogramming to pluripotency},
  author={Takahashi, Kazutoshi and Yamanaka, Shinya},
  journal={Nature Reviews Molecular Cell Biology},
  volume={17},
  number={3},
  pages={183},
  year={2016},
  publisher={Nature Publishing Group}
}

@article{schiebinger2019optimal,
  title={{Optimal-Transport Analysis of Single-Cell Gene Expression Identifies Developmental Trajectories in Reprogramming}},
  author={Schiebinger, Geoffrey and Shu, Jian and Tabaka, Marcin and Cleary, Brian and Subramanian, Vidya and Solomon, Aryeh and Gould, Joshua and Liu, Siyan and Lin, Stacie and Berube, Peter and others},
  journal={Cell},
  volume={176},
  number={4},
  year={2019},
  publisher={Elsevier}
}

@inproceedings{tong2020trajectorynet,
  title={{TrajectoryNet: A Dynamic Optimal Transport Network for Modeling Cellular Dynamics}},
  author={Tong, Alexander and Huang, Jessie and Wolf, Guy and Van Dijk, David and Krishnaswamy, Smita},
  booktitle=icml,
  year={2020},
}

@inproceedings{pmlr-v119-makkuva20a,
  title={Optimal transport mapping via input convex neural networks}, 
  author={Makkuva, Ashok and Taghvaei, Amirhossein and Oh, Sewoong and Lee, Jason},
  booktitle=icml, 
  year={2020},
  volume={37},
}

@incollection{combettes2011proximal,
  title={Proximal splitting methods in signal processing},
  author={Combettes, Patrick L and Pesquet, Jean-Christophe},
  booktitle={Fixed-point algorithms for inverse problems in science and engineering},
  pages={185--212},
  year={2011},
  publisher={Springer}
}

@inproceedings{amos2017input,
  title={{Input Convex Neural Networks}},
  author={Amos, Brandon and Xu, Lei and Kolter, J Zico},
  booktitle=icml,
  year={2017},
  volume={34}
}
@inproceedings{scaman2018lipschitz,
  title={Lipschitz regularity of deep neural networks: analysis and efficient estimation},
  author={Scaman, Kevin and Virmaux, Aladin},
  booktitle=nips,
  year={2018}
}

@inproceedings{paty2020regularity,
  title={{Regularity as Regularization: Smooth and Strongly Convex Brenier Potentials in Optimal Transport}},
  author={Paty, Fran{\c{c}}ois-Pierre and d’Aspremont, Alexandre and Cuturi, Marco},
  booktitle=aistats,
  year={2020},
}

@article{figalli2010optimal,
  title={{The Optimal Partial Transport Problem}},
  author={Figalli, Alessio},
  journal={Archive for Rational Mechanics and Analysis},
  volume={195},
  number={2},
  year={2010},
  publisher={Springer}
}

@article{caffarelli2000monotonicity,
  title={{Monotonicity Properties of Optimal Transportation and the FKG and Related Inequalities}},
  author={Caffarelli, Luis A},
  journal={Communications in Mathematical Physics},
  volume={214},
  number={3},
  year={2000},
  publisher={Springer}
}

@inproceedings{mokrov2021large,
  title={{Large-Scale Wasserstein Gradient Flows}},
  author={Mokrov, Petr and Korotin, Alexander and Li, Lingxiao and Genevay, Aude and Solomon, Justin and Burnaev, Evgeny},
  booktitle=nips,
  year={2021}
}

@article{alvarez2021optimizing,
  title={{Optimizing Functionals on the Space of Probabilities with Input Convex Neural Networks}},
  author={Alvarez-Melis, David and Schiff, Yair and Mroueh, Youssef},
  journal=arxiv,
  year={2021}
}

@inproceedings{grathwohl2018ffjord,
  title={{FFJORD: Free-Form Continuous Dynamics for Scalable Reversible Generative Models}},
  author={Grathwohl, Will and Chen, Ricky TQ and Bettencourt, Jesse and Sutskever, Ilya and Duvenaud, David},
  booktitle=iclr,
  year={2019}
}

@inproceedings{huang2021convex,
  title={{Convex Potential Flows: Universal Probability Distributions with Optimal Transport and  Convex Optimization}},
  author={Chin-Wei Huang and Ricky T. Q. Chen and Christos Tsirigotis and Aaron Courville},
  booktitle=iclr,
  year={2021},
}

@inproceedings{chen2018optimal,
  title={{Optimal Control Via Neural Networks: A Convex Approach}},
  author={Chen, Yize and Shi, Yuanyuan and Zhang, Baosen},
  booktitle=iclr,
  year={2019}
}

@inproceedings{lorraine2020,
  title={{Optimizing Millions of Hyperparameters by Implicit Differentiation}},
  author={Lorraine, Jonathan and Vicol, Paul and Duvenaud, David},
  booktitle=aistats,
  year={2020},
  volume={23}
}

@article{fischer2019,
  title={{Inferring population dynamics from single-cell RNA-sequencing time series data}},
  author={Fischer, David S and Fiedler, Anna K and Kernfeld, Eric M and Genga, Ryan MJ and Bastidas-Ponce, Aim{\'e}e and Bakhti, Mostafa and Lickert, Heiko and Hasenauer, Jan and Maehr, Rene and Theis, Fabian J},
  journal={Nature Biotechnology},
  volume={37},
  number={4},
  year={2019},
  publisher={Nature Publishing Group}
}

@article{klein2015,
  title={{Droplet Barcoding for Single-Cell Transcriptomics Applied to Embryonic Stem Cells}},
  author={Klein, Allon M and Mazutis, Linas and Akartuna, Ilke and Tallapragada, Naren and Veres, Adrian and Li, Victor and Peshkin, Leonid and Weitz, David A and Kirschner, Marc W},
  journal={Cell},
  volume={161},
  number={5},
  year={2015},
  publisher={Elsevier}
}

@article{moon2019visualizing,
  title={{Visualizing structure and transitions in high-dimensional biological data}},
  author={Moon, Kevin R and van Dijk, David and Wang, Zheng and Gigante, Scott and Burkhardt, Daniel B and Chen, William S and Yim, Kristina and van den Elzen, Antonia and Hirn, Matthew J and Coifman, Ronald R and others},
  journal={Nature Biotechnology},
  volume={37},
  number={12},
  year={2019},
  publisher={Nature Publishing Group}
}

@incollection{shamblott2009derivation,
  title={{Derivation and Differentiation of Human Embryonic Germ Cells}},
  author={Shamblott, Michael J and Kerr, Candace L and Axelman, Joyce and Littlefield, John W and Clark, Gregory O and Patterson, Ethan S and Addis, Russell C and Kraszewski, Jennifer N and Kent, Kathleen C and Gearhart, John D},
  booktitle={Essentials of Stem Cell Biology},
  year={2009},
  publisher={Elsevier}
}

@article{martin1975,
  title={{Differentiation of Clonal Lines of Teratocarcinoma Cells: Formation of Embryoid Bodies In Vitro}},
  author={Martin, Gail R and Evans, Martin J},
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% PPI
@article {Evans2021.10.04.463034,
	author={Evans, Richard and O{\textquoteright}Neill, Michael and Pritzel, Alexander and Antropova, Natasha and Senior, Andrew W and Green, Timothy and {\v Z}{\'\i}dek, Augustin and Bates, Russell and Blackwell, Sam and Yim, Jason and Ronneberger, Olaf and Bodenstein, Sebastian and Zielinski, Michal and Bridgland, Alex and Potapenko, Anna and Cowie, Andrew and Tunyasuvunakool, Kathryn and Jain, Rishub and Clancy, Ellen and Kohli, Pushmeet and Jumper, John and Hassabis, Demis},
	title={Protein complex prediction with AlphaFold-Multimer},
	elocation-id={2021.10.04.463034},
	year={2021},
	doi={10.1101/2021.10.04.463034},
	publisher={Cold Spring Harbor Laboratory},
	journal={bioRxiv}
}

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@article{kozakov2017cluspro,
  title={{The ClusPro web server for protein--protein docking}},
  author={Kozakov, Dima and Hall, David R and Xia, Bing and Porter, Kathryn A and Padhorny, Dzmitry and Yueh, Christine and Beglov, Dmitri and Vajda, Sandor},
  journal={Nature Protocols},
  volume={12},
  number={2},
  year={2017},
  publisher={npg}
}
@article{vakser2014protein,
  title={Protein-protein docking: From interaction to interactome},
  author={Vakser, Ilya A},
  journal={Biophysical journal},
  volume={107},
  number={8},
  pages={1785--1793},
  year={2014},
  publisher={Elsevier}
}
@article{baldassarre2021graphqa,
  title={GraphQA: protein model quality assessment using graph convolutional networks},
  author={Baldassarre, Federico and Men{\'e}ndez Hurtado, David and Elofsson, Arne and Azizpour, Hossein},
  journal={Bioinformatics},
  volume={37},
  number={3},
  pages={360--366},
  year={2021},
  publisher={Oxford University Press}
}
@article{sanyal2020proteingcn,
  title={ProteinGCN: Protein model quality assessment using graph convolutional networks},
  author={Sanyal, Soumya and Anishchenko, Ivan and Dagar, Anirudh and Baker, David and Talukdar, Partha},
  journal={bioRxiv},
  year={2020},
  publisher={Cold Spring Harbor Laboratory}
}
@inproceedings{liu2020deep,
  title={Deep learning of high-order interactions for protein interface prediction},
  author={Liu, Yi and Yuan, Hao and Cai, Lei and Ji, Shuiwang},
  booktitle={Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery \& Data Mining},
  pages={679--687},
  year={2020}
}
@article{ghani2021improved,
  title={Improved Docking of Protein Models by a Combination of Alphafold2 and ClusPro},
  author={Ghani, Usman and Desta, Israel and Jindal, Akhil and Khan, Omeir and Jones, George and Kotelnikov, Sergey and Padhorny, Dzmitry and Vajda, Sandor and Kozakov, Dima},
  journal={bioRxiv},
  year={2021},
  publisher={Cold Spring Harbor Laboratory}
}
@article{sunny2021fpdock,
  title={FPDock: Protein--protein docking using flower pollination algorithm},
  author={Sunny, Sharon and Jayaraj, PB},
  journal={Computational Biology and Chemistry},
  volume={93},
  pages={107518},
  year={2021},
  publisher={Elsevier}
}
@article{verburgt2021benchmarking,
  title={Benchmarking of structure refinement methods for protein complex models},
  author={Verburgt, Jacob and Kihara, Daisuke},
  journal={Proteins: Structure, Function, and Bioinformatics},
  year={2021},
  publisher={Wiley Online Library}
}
@inproceedings{fout2017protein,
  title={Protein interface prediction using graph convolutional networks},
  author={Fout, Alex and Byrd, Jonathon and Shariat, Basir and Ben-Hur, Asa},
  booktitle={Proceedings of the 31st International Conference on Neural Information Processing Systems},
  pages={6533--6542},
  year={2017}
}
@article{townshend2019end,
  title={End-to-end learning on 3d protein structure for interface prediction},
  author={Townshend, Raphael and Bedi, Rishi and Suriana, Patricia and Dror, Ron},
  journal={Advances in Neural Information Processing Systems},
  volume={32},
  pages={15642--15651},
  year={2019}
}
@article{xie2021deep,
  title={Deep graph learning of inter-protein contacts},
  author={Xie, Ziwei and Xu, Jinbo},
  journal={bioRxiv},
  year={2021},
  publisher={Cold Spring Harbor Laboratory}
}
@article{townshend2018transferrable,
  title={Transferrable End-to-End Learning for Protein Interface Prediction},
  author={Townshend, Raphael JL and Bedi, Rishi and Dror, Ron O},
  year={2018}
}
@article{dai2021protein,
  title={Protein interaction interface region prediction by geometric deep learning},
  author={Dai, Bowen and Bailey-Kellogg, Chris},
  journal={Bioinformatics},
  year={2021}
}
@article{bryant2021improved,
  title={Improved prediction of protein-protein interactions using AlphaFold2 and extended multiple-sequence alignments},
  author={Bryant, Patrick and Pozzati, Gabriele and Elofsson, Arne},
  journal={bioRxiv},
  year={2021},
  publisher={Cold Spring Harbor Laboratory}
}
@article {Humphreys2021,
	author={Humphreys, Ian R and Pei, Jimin and Baek, Minkyung and Krishnakumar, Aditya and Anishchenko, Ivan and Ovchinnikov, Sergey R and Zheng, Jing and Ness, Travis and Banjade, Sudeep and Bagde, Saket R and Stancheva, Viktoriya and Li, Xiaohan and Liu, Kaixian and Zheng, Zhi and Barerro, Daniel and Roy, Upasana and Fernandez, Israel S and Szakal, Barnabas and Branzei, Dana and Greene, Eric C and Biggins, Sue and Keeney, Scott and Miller, Elizabeth A. and Fromme, J Christopher and Hendrickson, Tamara and Cong, Qian and Baker, David},
	title={Structures of core eukaryotic protein complexes},
	elocation-id={2021.09.30.462231},
	year={2021},
	doi={10.1101/2021.09.30.462231},
	publisher={Cold Spring Harbor Laboratory}
}
@article{pei2021human,
  title={Human mitochondrial protein complexes revealed by large-scale coevolution analysis and deep learning-based structure modeling},
  author={Pei, Jimin and Zhang, Jing and Cong, Qian},
  journal={bioRxiv},
  year={2021},
  publisher={Cold Spring Harbor Laboratory}
}
@article{Jovine21,
author={Jovine, Luca},
title={Using machine learning to study protein–protein interactions: From the uromodulin polymer to egg zona pellucida filaments},
journal={Molecular Reproduction and Development},
volume={n/a},
number={n/a},
pages={},
keywords={artificial intelligence, protein polymerization, protein–protein interactions, uromodulin, zona pellucida},
doi={https://doi.org/10.1002/mrd.23538},
url={https://onlinelibrary.wiley.com/doi/abs/10.1002/mrd.23538},
eprint={https://onlinelibrary.wiley.com/doi/pdf/10.1002/mrd.23538}
}


%protein-ligand
@article{li2020monn,
  title={MONN: a multi-objective neural network for predicting compound-protein interactions and affinities},
  author={Li, Shuya and Wan, Fangping and Shu, Hantao and Jiang, Tao and Zhao, Dan and Zeng, Jianyang},
  journal={Cell Systems},
  volume={10},
  number={4},
  pages={308--322},
  year={2020},
  publisher={Elsevier}
}
@article{koes2013lessons,
  title={Lessons learned in empirical scoring with smina from the CSAR 2011 benchmarking exercise},
  author={Koes, David Ryan and Baumgartner, Matthew P and Camacho, Carlos J},
  journal={Journal of chemical information and modeling},
  volume={53},
  number={8},
  pages={1893--1904},
  year={2013},
  publisher={ACS Publications}
}
@article{trott2010autodock,
  title={AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading},
  author={Trott, Oleg and Olson, Arthur J},
  journal={Journal of computational chemistry},
  volume={31},
  number={2},
  pages={455--461},
  year={2010},
  publisher={Wiley Online Library}
}
@article{stepniewska2018development,
  title={Development and evaluation of a deep learning model for protein--ligand binding affinity prediction},
  author={Stepniewska-Dziubinska, Marta M and Zielenkiewicz, Piotr and Siedlecki, Pawel},
  journal={Bioinformatics},
  volume={34},
  number={21},
  pages={3666--3674},
  year={2018},
  publisher={Oxford University Press}
}
@article{wallach2015atomnet,
  title={AtomNet: a deep convolutional neural network for bioactivity prediction in structure-based drug discovery},
  author={Wallach, Izhar and Dzamba, Michael and Heifets, Abraham},
  journal={arXiv preprint arXiv:1510.02855},
  year={2015}
}
@article{mcnutt2021gnina,
  title={GNINA 1.0: molecular docking with deep learning},
  author={McNutt, Andrew T and Francoeur, Paul and Aggarwal, Rishal and Masuda, Tomohide and Meli, Rocco and Ragoza, Matthew and Sunseri, Jocelyn and Koes, David Ryan},
  journal={Journal of cheminformatics},
  volume={13},
  number={1},
  pages={1--20},
  year={2021},
  publisher={BioMed Central}
}
@article{bao2021deepbsp,
  title={DeepBSP—a Machine Learning Method for Accurate Prediction of Protein--Ligand Docking Structures},
  author={Bao, Jingxiao and He, Xiao and Zhang, John ZH},
  journal={Journal of Chemical Information and Modeling},
  year={2021},
  publisher={ACS Publications}
}
@article{tsubaki2019compound,
  title={Compound--protein interaction prediction with end-to-end learning of neural networks for graphs and sequences},
  author={Tsubaki, Masashi and Tomii, Kentaro and Sese, Jun},
  journal={Bioinformatics},
  volume={35},
  number={2},
  pages={309--318},
  year={2019},
  publisher={Oxford University Press}
}
@article{karimi2019deepaffinity,
  title={DeepAffinity: interpretable deep learning of compound--protein affinity through unified recurrent and convolutional neural networks},
  author={Karimi, Mostafa and Wu, Di and Wang, Zhangyang and Shen, Yang},
  journal={Bioinformatics},
  volume={35},
  number={18},
  pages={3329--3338},
  year={2019},
  publisher={Oxford University Press}
}
@article{ozturk2018deepdta,
  title={DeepDTA: deep drug--target binding affinity prediction},
  author={{\"O}zt{\"u}rk, Hakime and {\"O}zg{\"u}r, Arzucan and Ozkirimli, Elif},
  journal={Bioinformatics},
  volume={34},
  number={17},
  pages={i821--i829},
  year={2018},
  publisher={Oxford University Press}
}
@article{torng2019graph,
  title={Graph convolutional neural networks for predicting drug-target interactions},
  author={Torng, Wen and Altman, Russ B},
  journal={Journal of chemical information and modeling},
  volume={59},
  number={10},
  pages={4131--4149},
  year={2019},
  publisher={ACS Publications}
}
@inproceedings{li2021structure,
  title={Structure-aware Interactive Graph Neural Networks for the Prediction of Protein-Ligand Binding Affinity},
  author={Li, Shuangli and Zhou, Jingbo and Xu, Tong and Huang, Liang and Wang, Fan and Xiong, Haoyi and Huang, Weili and Dou, Dejing and Xiong, Hui},
  booktitle={Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery \& Data Mining},
  pages={975--985},
  year={2021}
}

@article{ko2021can,
  title={Can AlphaFold2 predict protein-peptide complex structures accurately?},
  author={Ko, Junsu and Lee, Juyong},
  journal={bioRxiv},
  year={2021},
  publisher={Cold Spring Harbor Laboratory}
}
@article{khramushin2021patchman,
  title={PatchMAN docking: Modeling peptide-protein interactions in the context of the receptor surface},
  author={Khramushin, Alisa and Tsaban, Tomer and Varga, Julia K and Avraham, Orly and Schueler-Furman, Ora},
  journal={bioRxiv},
  year={2021},
  publisher={Cold Spring Harbor Laboratory}
}
@article{adhikari2018confold2,
  author={Adhikari, Badri and Cheng, Jianlin},
  title={CONFOLD2: Improved Contact-driven ab initio Protein Structure Modeling},
  journal={BMC bioinformatics},
  volume={19},
  number={1},
  pages={1-5},
  year={2018},
  publisher={Springer}
}
@article{yang2020improved,
  author={Yang, Jianyi and Anishchenko, Ivan and Park, Hahnbeom and Peng, Zhenling and Ovchinnikov, Sergey and Baker, David},
  title={Improved Protein Structure Prediction using Predicted Interresidue Orientations},
  journal={Proceedings of the National Academy of Sciences},
  volume={117},
  number={3},
  pages={1496-1503},
  year={2020},
}
@article{ju2021copulanet,
  author={Ju, Fusong and Zhu, Jianwei and Shao, Bin and Kong, Lupeng and Liu, Tie-Yan and Zheng, Wei-Mou and Bu, Dongbo},
  title={CopulaNet: Learning Residue Co-evolution Directly from Multiple Sequence Alignment for Protein Structure Prediction},
  journal={Nature Communications},
  year={2021},
  month={May},
  volume={12},
  number={1},
  pages={2535},
}
@inproceedings{cohen2016group,
  title={Group equivariant convolutional networks},
  author={Cohen, Taco and Welling, Max},
  booktitle={International conference on machine learning},
  pages={2990--2999},
  year={2016},
  organization={PMLR}
}
@article{cohen2016steerable,
  title={Steerable cnns},
  author={Cohen, Taco S and Welling, Max},
  journal={arXiv preprint arXiv:1612.08498},
  year={2016}
}
% GNN
@article{weiler2019general,
  title={General $ E (2) $-Equivariant Steerable CNNs},
  author={Weiler, Maurice and Cesa, Gabriele},
  journal={arXiv preprint arXiv:1911.08251},
  year={2019}
}
@article{rezende2019equivariant,
  title={Equivariant hamiltonian flows},
  author={Rezende, Danilo Jimenez and Racani{\`e}re, S{\'e}bastien and Higgins, Irina and Toth, Peter},
  journal={arXiv preprint arXiv:1909.13739},
  year={2019}
}
@article{romero2020group,
  title={Group equivariant stand-alone self-attention for vision},
  author={Romero, David W and Cordonnier, Jean-Baptiste},
  journal={arXiv preprint arXiv:2010.00977},
  year={2020}
}
@article{hutchinson2021lietransformer,
  author={Michael Hutchinson and Charline Le Lan and Sheheryar Zaidi and Emilien Dupont and Yee Whye Teh and Hyunjik Kim},
  title={LieTransformer: Equivariant Self-attention for Lie Groups},
  journal={arXiv Preprint},
  volume={2012.10885},
  year={2020},
}
@article{bruna2013spectral,
  title={Spectral networks and locally connected networks on graphs},
  author={Bruna, Joan and Zaremba, Wojciech and Szlam, Arthur and LeCun, Yann},
  journal={arXiv preprint arXiv:1312.6203},
  year={2013}
}

@article{defferrard2016convolutional,
  title={Convolutional neural networks on graphs with fast localized spectral filtering},
  author={Defferrard, Micha{\"e}l and Bresson, Xavier and Vandergheynst, Pierre},
  journal={arXiv preprint arXiv:1606.09375},
  year={2016}
}

@article{kipf2016semi,
  title={Semi-supervised classification with graph convolutional networks},
  author={Kipf, Thomas N and Welling, Max},
  journal={arXiv preprint arXiv:1609.02907},
  year={2016}
}

@inproceedings{gilmer2017neural,
  title={Neural message passing for quantum chemistry},
  author={Gilmer, Justin and Schoenholz, Samuel S and Riley, Patrick F and Vinyals, Oriol and Dahl, George E},
  booktitle={International Conference on Machine Learning},
  pages={1263--1272},
  year={2017},
  organization={PMLR}
}

@article{li2015gated,
  title={Gated graph sequence neural networks},
  author={Li, Yujia and Tarlow, Daniel and Brockschmidt, Marc and Zemel, Richard},
  journal={arXiv preprint arXiv:1511.05493},
  year={2015}
}

% docking

@article{weng2019hawkdock,
  title={HawkDock: a web server to predict and analyze the protein--protein complex based on computational docking and MM/GBSA},
  author={Weng, Gaoqi and Wang, Ercheng and Wang, Zhe and Liu, Hui and Zhu, Feng and Li, Dan and Hou, Tingjun},
  journal={Nucleic acids research},
  volume={47},
  number={W1},
  pages={W322--W330},
  year={2019},
  publisher={Oxford University Press}
}
@article{christoffer2021lzerd,
  title={LZerD webserver for pairwise and multiple protein--protein docking},
  author={Christoffer, Charles and Chen, Siyang and Bharadwaj, Vijay and Aderinwale, Tunde and Kumar, Vidhur and Hormati, Matin and Kihara, Daisuke},
  journal={Nucleic Acids Research},
  year={2021}
}
@article{chen2003zdock,
  title={ZDOCK: an initial-stage protein-docking algorithm},
  author={Chen, Rong and Li, Li and Weng, Zhiping},
  journal={Proteins: Structure, Function, and Bioinformatics},
  volume={52},
  number={1},
  pages={80--87},
  year={2003},
  publisher={Wiley Online Library}
}

@article{venkatraman2009protein,
  title={Protein-protein docking using region-based 3D Zernike descriptors},
  author={Venkatraman, Vishwesh and Yang, Yifeng D and Sael, Lee and Kihara, Daisuke},
  journal={BMC bioinformatics},
  volume={10},
  number={1},
  pages={1--21},
  year={2009},
  publisher={Springer}
}

@article{de2010haddock,
  title={{The HADDOCK web server for data-driven biomolecular docking}},
  author={De Vries, Sjoerd J and Van Dijk, Marc and Bonvin, Alexandre MJJ},
  journal={Nature Protocols},
  volume={5},
  number={5},
  year={2010},
}

@article{torchala2013swarmdock,
  title={SwarmDock: a server for flexible protein--protein docking},
  author={Torchala, Mieczyslaw and Moal, Iain H and Chaleil, Raphael AG and Fernandez-Recio, Juan and Bates, Paul A},
  journal={Bioinformatics},
  volume={29},
  number={6},
  pages={807--809},
  year={2013},
  publisher={Oxford University Press}
}

@article{attract2017,
  title={{Protein-protein and peptide-protein docking and refinement using ATTRACT in CAPRI}},
  author={Schindler, Christina EM and Chauvot de Beauch{\^e}ne, Isaure and de Vries, Sjoerd J and Zacharias, Martin},
  journal={Proteins: Structure, Function, and Bioinformatics},
  volume={85},
  number={3},
  pages={391--398},
  year={2017},
  publisher={Wiley Online Library}
}

% protein interface
@article{esmaielbeiki2016progress,
  title={Progress and challenges in predicting protein interfaces},
  author={Esmaielbeiki, Reyhaneh and Krawczyk, Konrad and Knapp, Bernhard and Nebel, Jean-Christophe and Deane, Charlotte M},
  journal={Briefings in bioinformatics},
  volume={17},
  number={1},
  pages={117--131},
  year={2016},
  publisher={Oxford University Press}
}
% contact prediction

@article{zeng2018complexcontact,
  title={ComplexContact: a web server for inter-protein contact prediction using deep learning},
  author={Zeng, Hong and Wang, Sheng and Zhou, Tianming and Zhao, Feifeng and Li, Xiufeng and Wu, Qing and Xu, Jinbo},
  journal={Nucleic acids research},
  volume={46},
  number={W1},
  pages={W432--W437},
  year={2018},
  publisher={Oxford University Press}
}

% protein-protein interaction site prediction

@article{zeng2020protein,
  title={Protein--protein interaction site prediction through combining local and global features with deep neural networks},
  author={Zeng, Min and Zhang, Fuhao and Wu, Fang-Xiang and Li, Yaohang and Wang, Jianxin and Li, Min},
  journal={Bioinformatics},
  volume={36},
  number={4},
  pages={1114--1120},
  year={2020},
  publisher={Oxford University Press}
}

% protein interface prediction
@phdthesis{fout2017pip,
  title={Protein interface prediction using graph convolutional networks},
  author={Fout, Alex M},
  year={2017},
  school={Colorado State University}
}

@article{townshend2019_3dconv_pip,
  title={End-to-end learning on 3d protein structure for interface prediction},
  author={Townshend, Raphael and Bedi, Rishi and Suriana, Patricia and Dror, Ron},
  journal={Advances in Neural Information Processing Systems},
  volume={32},
  pages={15642--15651},
  year={2019}
}

@article{liu2019attention,
  title={Attention mechanism enhanced LSTM with residual architecture and its application for protein-protein interaction residue pairs prediction},
  author={Liu, Jiale and Gong, Xinqi},
  journal={BMC bioinformatics},
  volume={20},
  number={1},
  pages={1--11},
  year={2019},
  publisher={Springer}
}

@article{xie2020prediction,
  title={Prediction of protein--protein interaction sites using convolutional neural network and improved data sets},
  author={Xie, Zengyan and Deng, Xiaoya and Shu, Kunxian},
  journal={International journal of molecular sciences},
  volume={21},
  number={2},
  pages={467},
  year={2020},
  publisher={Multidisciplinary Digital Publishing Institute}
}

@article{fisher2009data,
  title={{Data assimilation in weather forecasting: a case study in PDE-constrained optimization}},
  author={Fisher, Mike and Nocedal, Jorge and Tr{\'e}molet, Yannick and Wright, Stephen J},
  journal={Optimization and Engineering},
  volume={10},
  number={3},
  year={2009},
  publisher={Springer}
}

@article{basu2016dockq,
  title={DockQ: a quality measure for protein-protein docking models},
  author={Basu, Sankar and Wallner, Bj{\"o}rn},
  journal={PloS one},
  volume={11},
  number={8},
  pages={e0161879},
  year={2016},
  publisher={Public Library of Science San Francisco, CA USA}
}
@article{cao2020energy,
  title={Energy-based graph convolutional networks for scoring protein docking models},
  author={Cao, Yue and Shen, Yang},
  journal={Proteins: Structure, Function, and Bioinformatics},
  volume={88},
  number={8},
  pages={1091--1099},
  year={2020},
  publisher={Wiley Online Library}
}
@article{biesiada2011survey,
  title={Survey of public domain software for docking simulations and virtual screening},
  author={Biesiada, Jacek and Porollo, Aleksey and Velayutham, Prakash and Kouril, Michal and Meller, Jaroslaw},
  journal={Human genomics},
  volume={5},
  number={5},
  pages={1--9},
  year={2011},
  publisher={BioMed Central}
}
@article{launay2020evaluation,
  title={Evaluation of CONSRANK-Like Scoring Functions for Rescoring Ensembles of Protein--Protein Docking Poses},
  author={Launay, Guillaume and Ohue, Masahito and Prieto Santero, Julia and Matsuzaki, Yuri and Hilpert, C{\'e}cile and Uchikoga, Nobuyuki and Hayashi, Takanori and Martin, Juliette},
  journal={Frontiers in molecular biosciences},
  volume={7},
  pages={308},
  year={2020},
  publisher={Frontiers}
}
@article{moal2013scoring,
  title={The scoring of poses in protein-protein docking: current capabilities and future directions},
  author={Moal, Iain H and Torchala, Mieczyslaw and Bates, Paul A and Fern{\'a}ndez-Recio, Juan},
  journal={BMC bioinformatics},
  volume={14},
  number={1},
  pages={1--15},
  year={2013},
  publisher={BioMed Central}
}
@article{eismann2020hierarchical,
  title={Hierarchical, rotation-equivariant neural networks to select structural models of protein complexes},
  author={Eismann, Stephan and Townshend, Raphael JL and Thomas, Nathaniel and Jagota, Milind and Jing, Bowen and Dror, Ron O},
  journal={Proteins: Structure, Function, and Bioinformatics},
  year={2020},
  publisher={Wiley Online Library}
}

% protein surface

@article{gainza2020deciphering,
  title={Deciphering interaction fingerprints from protein molecular surfaces using geometric deep learning},
  author={Gainza, Pablo and Sverrisson, Freyr and Monti, Frederico and Rodola, Emanuele and Boscaini, D and Bronstein, MM and Correia, BE},
  journal={Nature Methods},
  volume={17},
  number={2},
  pages={184--192},
  year={2020},
  publisher={npg}
}

@inproceedings{sverrisson2021fast,
  title={Fast end-to-end learning on protein surfaces},
  author={Sverrisson, Freyr and Feydy, Jean and Correia, Bruno E and Bronstein, Michael M},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={15272--15281},
  year={2021}
}


% equivariant neural networks
@article{thomas2018tensor,
  title={Tensor field networks: Rotation-and translation-equivariant neural networks for 3d point clouds},
  author={Thomas, Nathaniel and Smidt, Tess and Kearnes, Steven and Yang, Lusann and Li, Li and Kohlhoff, Kai and Riley, Patrick},
  journal={arXiv preprint arXiv:1802.08219},
  year={2018}
}

@article{fuchs2020se,
  title={SE (3)-transformers: 3D roto-translation equivariant attention networks},
  author={Fuchs, Fabian B and Worrall, Daniel E and Fischer, Volker and Welling, Max},
  journal={arXiv preprint arXiv:2006.10503},
  year={2020}
}

@inproceedings{finzi2020generalizing,
  title={Generalizing convolutional neural networks for equivariance to lie groups on arbitrary continuous data},
  author={Finzi, Marc and Stanton, Samuel and Izmailov, Pavel and Wilson, Andrew Gordon},
  booktitle={International Conference on Machine Learning},
  pages={3165--3176},
  year={2020},
  organization={PMLR}
}

@article{satorras2021n,
  title={E(n)-equivariant graph neural networks},
  author={Satorras, Victor Garcia and Hoogeboom, Emiel and Welling, Max},
  journal={arXiv preprint arXiv:2102.09844},
  year={2021}
}

% graph matching networks
@inproceedings{li2019graph,
  title={Graph matching networks for learning the similarity of graph structured objects},
  author={Li, Yujia and Gu, Chenjie and Dullien, Thomas and Vinyals, Oriol and Kohli, Pushmeet},
  booktitle={International Conference on Machine Learning},
  pages={3835--3845},
  year={2019},
  organization={PMLR}
}
% rigid transformation
@misc{rigid_body_transformation,
  title={FINDING OPTIMAL ROTATION AND TRANSLATION BETWEEN CORRESPONDING 3D POINTS},
  howpublished={\url{http://nghiaho.com/?page_id=671}},
  author={Ho, Nghia},
  note={Accessed: 2021-07-05}
}


@inproceedings{xu2018powerful,
  title={How Powerful are Graph Neural Networks?},
  author={Xu, Keyulu and Hu, Weihua and Leskovec, Jure and Jegelka, Stefanie},
  booktitle={International Conference on Learning Representations},
  year={2018}
}

% focal loss
@inproceedings{lin2017focal,
  title={Focal loss for dense object detection},
  author={Lin, Tsung-Yi and Goyal, Priya and Girshick, Ross and He, Kaiming and Doll{\'a}r, Piotr},
  booktitle={Proceedings of the IEEE international conference on computer vision},
  year={2017}
}

% db5
@article{vreven2015updates,
  title={Updates to the integrated protein--protein interaction benchmarks: docking benchmark version 5 and affinity benchmark version 2},
  author={Vreven, Thom and Moal, Iain H and Vangone, Anna and Pierce, Brian G and Kastritis, Panagiotis L and Torchala, Mieczyslaw and Chaleil, Raphael and Jim{\'e}nez-Garc{\'\i}a, Brian and Bates, Paul A and Fernandez-Recio, Juan and others},
  journal={Journal of Molecular Biology},
  volume={427},
  number={19},
  year={2015},
  publisher={Elsevier}
}

@inproceedings{bunne2022supervised,
  title={{Supervised Training of Conditional Monge Maps}},
  author={Bunne, Charlotte and Krause, Andreas and Cuturi, Marco},
  booktitle=nips,
  year={2022}
}

@article{cuturi2022optimal,
  title={{Optimal Transport Tools (OTT): A JAX Toolbox for all things Wasserstein}},
  author={Cuturi, Marco and Meng-Papaxanthos, Laetitia and Tian, Yingtao and Bunne, Charlotte and Davis, Geoff and Teboul, Olivier},
  journal={arXiv Preprint arXiv:2201.12324},
  year={2022}
}

@article{jumper2021highly,
  title={Highly accurate protein structure prediction with AlphaFold},
  author={Jumper, John and Evans, Richard and Pritzel, Alexander and Green, Tim and Figurnov, Michael and Ronneberger, Olaf and Tunyasuvunakool, Kathryn and Bates, Russ and {\v{Z}}{\'\i}dek, Augustin and Potapenko, Anna and others},
  journal={Nature},
  volume={596},
  number={7873},
  pages={583--589},
  year={2021},
  publisher={npg}
}

@article{senior2020improved,
  title={Improved protein structure prediction using potentials from deep learning},
  author={Senior, Andrew W and Evans, Richard and Jumper, John and Kirkpatrick, James and Sifre, Laurent and Green, Tim and Qin, Chongli and {\v{Z}}{\'\i}dek, Augustin and Nelson, Alexander WR and Bridgland, Alex and others},
  journal={Nature},
  volume={577},
  number={7792},
  pages={706--710},
  year={2020},
  publisher={npg}
}

@article{baek2021accurate,
  title={Accurate prediction of protein structures and interactions using a three-track neural network},
  author={Baek, Minkyung and DiMaio, Frank and Anishchenko, Ivan and Dauparas, Justas and Ovchinnikov, Sergey and Lee, Gyu Rie and Wang, Jue and Cong, Qian and Kinch, Lisa N and Schaeffer, R Dustin and others},
  journal={Science},
  volume={373},
  number={6557},
  pages={871--876},
  year={2021},
  publisher={American Association for the Advancement of Science}
}

% data
@article{berman2000protein,
  title={The protein data bank},
  author={Berman, Helen M and Westbrook, John and Feng, Zukang and Gilliland, Gary and Bhat, Talapady N and Weissig, Helge and Shindyalov, Ilya N and Bourne, Philip E},
  journal={Nucleic acids research},
  volume={28},
  number={1},
  pages={235--242},
  year={2000},
  publisher={Oxford University Press}
}

% kabsch
@article{kabsch1976solution,
  title={A solution for the best rotation to relate two sets of vectors},
  author={Kabsch, Wolfgang},
  journal={{Acta Crystallographica Section A: Crystal Physics, Diffraction, Theoretical and General Crystallography}},
  volume={32},
  number={5},
  year={1976},
  publisher={International Union of Crystallography}
}

% hdock
@article{yan2020hdock,
  title={{The HDOCK server for integrated protein--protein docking}},
  author={Yan, Yumeng and Tao, Huanyu and He, Jiahua and Huang, Sheng-You},
  journal={Nature Protocols},
  volume={15},
  number={5},
  year={2020},
}

@article{yan2017hdock,
  title={{HDOCK: a web server for protein--protein and protein--DNA/RNA docking based on a hybrid strategy}},
  author={Yan, Yumeng and Zhang, Di and Zhou, Pei and Li, Botong and Huang, Sheng-You},
  journal={Nucleic Acids Research},
  volume={45},
  number={W1},
  year={2017},
  publisher={Oxford University Press}
}

@article{yan2017addressing,
  title={Addressing recent docking challenges: A hybrid strategy to integrate template-based and free protein-protein docking},
  author={Yan, Yumeng and Wen, Zeyu and Wang, Xinxiang and Huang, Sheng-You},
  journal={Proteins: Structure, Function, and Bioinformatics},
  volume={85},
  number={3},
  year={2017},
  publisher={Wiley Online Library}
}

@article{huang2014knowledge,
  title={A knowledge-based scoring function for protein-RNA interactions derived from a statistical mechanics-based iterative method},
  author={Huang, Sheng-You and Zou, Xiaoqin},
  journal={Nucleic acids research},
  volume={42},
  number={7},
  pages={e55--e55},
  year={2014},
  publisher={Oxford University Press}
}

@article{huang2008iterative,
  title={An iterative knowledge-based scoring function for protein--protein recognition},
  author={Huang, Sheng-You and Zou, Xiaoqin},
  journal={Proteins: Structure, Function, and Bioinformatics},
  volume={72},
  number={2},
  pages={557--579},
  year={2008},
  publisher={Wiley Online Library}
}

% misc

@article{ingraham2019generative,
  title={Generative models for graph-based protein design},
  author={Ingraham, John and Garg, Vikas K and Barzilay, Regina and Jaakkola, Tommi},
  year={2019}
}




@inproceedings{ionescu2015matrix,
  title={Matrix backpropagation for deep networks with structured layers},
  author={Ionescu, Catalin and Vantzos, Orestis and Sminchisescu, Cristian},
  booktitle={Proceedings of the IEEE International Conference on Computer Vision},
  pages={2965--2973},
  year={2015}
}


@inproceedings{papadopoulo2000estimating,
  title={Estimating the jacobian of the singular value decomposition: Theory and applications},
  author={Papadopoulo, Th{\'e}odore and Lourakis, Manolis IA},
  booktitle={European Conference on Computer Vision},
  pages={554--570},
  year={2000},
  organization={Springer}
}

@article{flamary2021pot,
  author ={R{\'e}mi Flamary and Nicolas Courty and Alexandre Gramfort and Mokhtar Z. Alaya and Aur{\'e}lie Boisbunon and Stanislas Chambon and Laetitia Chapel and Adrien Corenflos and Kilian Fatras and Nemo Fournier and L{\'e}o Gautheron and Nathalie T.H. Gayraud and Hicham Janati and Alain Rakotomamonjy and Ievgen Redko and Antoine Rolet and Antony Schutz and Vivien Seguy and Danica J. Sutherland and Romain Tavenard and Alexander Tong and Titouan Vayer},
  title  ={POT: Python Optimal Transport},
  journal={Journal of Machine Learning Research},
  year   ={2021},
  volume ={22},
  number ={78},
  pages  ={1-8},
  url    ={http://jmlr.org/papers/v22/20-451.html}
}




@article{kingma2014adam,
  title={Adam: A method for stochastic optimization},
  author={Kingma, Diederik P and Ba, Jimmy},
  journal={arXiv preprint arXiv:1412.6980},
  year={2014}
}


@article{mashiach2010integrated,
  title={An integrated suite of fast docking algorithms},
  author={Mashiach, Efrat and Schneidman-Duhovny, Dina and Peri, Aviyah and Shavit, Yoli and Nussinov, Ruth and Wolfson, Haim J},
  journal={Proteins: Structure, Function, and Bioinformatics},
  volume={78},
  number={15},
  year={2010},
  publisher={Wiley Online Library}
}

@article{desta2020performance,
  title={{Performance and Its Limits in Rigid Body Protein-Protein Docking}},
  author={Desta, Israel T and Porter, Kathryn A and Xia, Bing and Kozakov, Dima and Vajda, Sandor},
  journal={Structure},
  volume={28},
  number={9},
  year={2020},
  publisher={Elsevier}
}


@article{de2015web,
  title={{A Web Interface for Easy Flexible Protein-Protein Docking with ATTRACT}},
  author={de Vries, Sjoerd J and Schindler, Christina EM and de Beauch{\^e}ne, Isaure Chauvot and Zacharias, Martin},
  journal={Biophysical Journal},
  volume={108},
  number={3},
  year={2015},
  publisher={Elsevier}
}



@article{ganea2021geomol,
  title={GeoMol: Torsional Geometric Generation of Molecular 3D Conformer Ensembles},
  author={Ganea, Octavian-Eugen and Pattanaik, Lagnajit and Coley, Connor W and Barzilay, Regina and Jensen, Klavs F and Green, William H and Jaakkola, Tommi S},
  journal={arXiv preprint arXiv:2106.07802},
  year={2021}
}

@article{sanner1996reduced,
  title={Reduced surface: an efficient way to compute molecular surfaces},
  author={Sanner, Michel F and Olson, Arthur J and Spehner, Jean-Claude},
  journal={Biopolymers},
  volume={38},
  number={3},
  pages={305--320},
  year={1996},
  publisher={Wiley Online Library}
}


@article{jin2021iterative,
  title={Iterative Refinement Graph Neural Network for Antibody Sequence-Structure Co-design},
  author={Jin, Wengong and Wohlwend, Jeremy and Barzilay, Regina and Jaakkola, Tommi},
  journal={arXiv preprint arXiv:2110.04624},
  year={2021}
}


@inproceedings{hoogeboom2022equivariant,
  title={Equivariant diffusion for molecule generation in 3d},
  author={Hoogeboom, Emiel and Satorras, V{\i}ctor Garcia and Vignac, Cl{\'e}ment and Welling, Max},
  booktitle={International Conference on Machine Learning},
  pages={8867--8887},
  year={2022},
  organization={PMLR}
}

@article{peng2022d3pm,
  title={D3PM: a comprehensive database for protein motions ranging from residue to domain},
  author={Peng, Cheng and Zhang, Xinben and Xu, Zhijian and Chen, Zhaoqiang and Yang, Yanqing and Cai, Tingting and Zhu, Weiliang},
  journal={BMC bioinformatics},
  volume={23},
  number={1},
  pages={1--11},
  year={2022},
  publisher={BioMed Central}
}

@inproceedings{
xu2022geodiff,
title={GeoDiff: A Geometric Diffusion Model for Molecular Conformation Generation},
author={Minkai Xu and Lantao Yu and Yang Song and Chence Shi and Stefano Ermon and Jian Tang},
booktitle={International Conference on Learning Representations},
year={2022},
}

@misc{peluchetti2023diffusion,
      title={Diffusion Bridge Mixture Transports, Schr\"odinger Bridge Problems and Generative Modeling}, 
      author={Stefano Peluchetti},
      year={2023},
      journal={arXiv preprint}
}

@article{schauer2017guided,
	title = {Guided proposals for simulating multi-dimensional diffusion bridges},
	author = {Moritz Schauer and Frank van der Meulen and Harry van Zanten},
	year = 2017,
	publisher = {Bernoulli Society for Mathematical Statistics and Probability},
	volume = {23},
	number = {4A},
	journal = {Bernoulli}
}

@article{ketata2023diffdock,
  title={DiffDock-PP: Rigid Protein-Protein Docking with Diffusion Models},
  author={Ketata, Mohamed Amine and Laue, Cedrik and Mammadov, Ruslan and St{\"a}rk, Hannes and Wu, Menghua and Corso, Gabriele and Marquet, C{\'e}line and Barzilay, Regina and Jaakkola, Tommi S},
  journal={arXiv preprint arXiv:2304.03889},
  year={2023}
}

@article{dominguez2003haddock,
  title={HADDOCK: a protein- protein docking approach based on biochemical or biophysical information},
  author={Dominguez, Cyril and Boelens, Rolf and Bonvin, Alexandre MJJ},
  journal={Journal of the American Chemical Society},
  volume={125},
  number={7},
  pages={1731--1737},
  year={2003},
  publisher={ACS Publications}
}

@article{geiger2022e3nn,
  title={e3nn: Euclidean neural networks},
  author={Geiger, Mario and Smidt, Tess},
  journal={arXiv preprint arXiv:2207.09453},
  year={2022}
}