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Artificial intelligence for scientific discovery has recently generated significant interest within the machine learning and scientific communities, particularly in the domains of chemistry, biology, and material discovery.
Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules
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Enzyme nomenclature: prepared by edwin c. webb, academic press, 1992. £34.00 (xiii + 862 pages) isbn 0 12 227165 3
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Merck molecular force field. i. basis, form, scope, parameterization, and performance of mmff94
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Stochastic analysis on manifolds
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Glide: a new approach for rapid, accurate docking and scoring. 1. method and assessment of docking accuracy
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The pdbbind database: Collection of binding affinities for protein- ligand complexes with known three-dimensional structures
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Invariant variation problems
Emmy Noether and M. A. Tavel · 2005
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The pdbbind database: methodologies and updates
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Extended-connectivity fingerprints
David Rogers and Mathew Hahn · 2010
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Assignment of ec numbers to enzymatic reactions with reaction difference fingerprints
Qian-Nan Hu, Hui Zhu, Xiaobing Li, Manman Zhang, Zhe Deng, Xiaoyan Yang, and Zixin Deng · 2012
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Commentary: The materials project: A materials genome approach to accelerating materials innovation
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RDKit: A software suite for cheminformatics, computational chemistry, and predictive modeling, 2013
Greg Landrum et al · 2013
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Hierarchical classification of protein folds using a novel ensemble classifier
Chen Lin, Ying Zou, Ji Qin, Xiangrong Liu, Yi Jiang, Caihuan Ke, and Quan Zou · 2013
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Hierarchical classification of protein folds using a novel ensemble classifier
Chen Lin, Ying Zou, Ji Qin, Xiangrong Liu, Yi Jiang, Caihuan Ke, and Quan Zou · 2013
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Python materials genomics (pymatgen): A robust, open-source python library for materials analysis
Shyue Ping Ong, William Davidson Richards, Anubhav Jain, Geoffroy Hautier, Michael Kocher, Shreyas Cholia, Dan Gunter, Vincent L Chevrier, Kristin A Persson, and Gerbrand Ceder · 2013
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Quantum chemistry structures and properties of 134 kilo molecules
Raghunathan Ramakrishnan, Pavlo O Dral, Matthias Rupp, and O Anatole Von Lilienfeld · 2014
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Convolutional networks on graphs for learning molecular fingerprints
David Duvenaud, Dougal Maclaurin, Jorge Aguilera-Iparraguirre, Rafael Gómez-Bombarelli, Timothy Hirzel, Alán Aspuru-Guzik, and Ryan P Adams · 2015
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Symmetry
Hermann Weyl · 2015
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Taco S Cohen and Max Welling · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling · 2016
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Sgdr: Stochastic gradient descent with warm restarts
Ilya Loshchilov and Frank Hutter · 2016
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Protein binding pocket dynamics
Antonia Stank, Daria B Kokh, Jonathan C Fuller, and Rebecca C Wade · 2016
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Group theory in a nutshell for physicists
Anthony Zee · 2016
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Machine learning of accurate energy-conserving molecular force fields
Stefan Chmiela, Alexandre Tkatchenko, Huziel E Sauceda, Igor Poltavsky, Kristof T Schütt, and Klaus-Robert Müller · 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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Inductive representation learning on large graphs
Will Hamilton, Zhitao Ying, and Jure Leskovec · 2017
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Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio · 2017
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
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Applied chemoinformatics: achievements and future opportunities
Thomas Engel and Johann Gasteiger · 2018
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Deepsf: deep convolutional neural network for mapping protein sequences to folds
Jie Hou, Badri Adhikari, and Jianlin Cheng · 2018
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Schnet–a deep learning architecture for molecules and materials
Kristof T Schütt, Huziel E Sauceda, P-J Kindermans, Alexandre Tkatchenko, and K-R Müller · 2018
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Tensor field networks: Rotation-and translation-equivariant neural networks for 3d point clouds
Tess Smidt, Nathaniel Thomas, Steven Kearnes, Lusann Yang, Li Li, Kai Kohlhoff, and Patrick Riley · 2018
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Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties
Tian Xie and Jeffrey C Grossman · 2018
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How powerful are graph neural networks?
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka · 2018
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Guacamol: benchmarking models for de novo molecular design
Nathan Brown, Marco Fiscato, Marwin HS Segler, and Alain C Vaucher · 2019
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Sifts: updated structure integration with function, taxonomy and sequences resource allows 40-fold increase in coverage of structure-based annotations for proteins
Jose M Dana, Aleksandras Gutmanas, Nidhi Tyagi, Guoying Qi, Claire O’Donovan, Maria Martin, and Sameer Velankar · 2019
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Fast graph representation learning with PyTorch Geometric
Matthias Fey and Jan E. Lenssen · 2019
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Generative models for graph-based protein design
John Ingraham, Vikas Garg, Regina Barzilay, and Tommi Jaakkola · 2019
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A graph-based genetic algorithm and generative model/monte carlo tree search for the exploration of chemical space
Jan H Jensen · 2019
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Universal invariant and equivariant graph neural networks, 2019
Nicolas Keriven and Gabriel Peyré · 2019
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N-gram graph: Simple unsupervised representation for graphs, with applications to molecules
Shengchao Liu, Mehmet F Demirel, and Yingyu Liang · 2019
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Loss-balanced task weighting to reduce negative transfer in multi-task learning
Shengchao Liu, Yingyu Liang, and Anthony Gitter · 2019
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Quantum versus classical angular momentum
Jan Mostowski and Joanna Pietraszewicz · 2019
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Deep Learning for the Life Sciences
Bharath Ramsundar, Peter Eastman, Patrick Walters, Vijay Pande, Karl Leswing, and Zhenqin Wu · 2019
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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, et al · 2019
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Revised md17 dataset
Anders Christensen and O. Anatole von Lilienfeld · 2020
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On the role of gradients for machine learning of molecular energies and forces
Anders S Christensen and O Anatole von Lilienfeld · 2020
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Principal neighbourhood aggregation for graph nets
Gabriele Corso, Luca Cavalleri, Dominique Beaini, Pietro Liò, and Petar Veličković · 2020
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Benchmarking materials property prediction methods: the matbench test set and automatminer reference algorithm
Alexander Dunn, Qi Wang, Alex Ganose, Daniel Dopp, and Anubhav Jain · 2020
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Crystal diffusion variational autoencoder for periodic material generation
Tian Xie, Xiang Fu, Octavian-Eugen Ganea, Regina Barzilay, and Tommi Jaakkola · 2021
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Molecule3d: A benchmark for predicting 3d geometries from molecular graphs
Zhao Xu, Youzhi Luo, Xuan Zhang, Xinyi Xu, Yaochen Xie, Meng Liu, Kaleb Dickerson, Cheng Deng, Maho Nakata, and Shuiwang Ji · 2021
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Functionally regionalized knowledge transfer for low-resource drug discovery
Huaxiu Yao, Ying Wei, Long-Kai Huang, Ding Xue, Junzhou Huang, and Zhenhui Jessie Li · 2021
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Do transformers really perform badly for graph representation?
Chengxuan Ying, Tianle Cai, Shengjie Luo, Shuxin Zheng, Guolin Ke, Di He, Yanming Shen, and Tie-Yan Liu · 2021
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The design space of e(3)-equivariant atom-centered interatomic potentials, 2022
Ilyes Batatia, Simon Batzner, Dávid Péter Kovács, Albert Musaelian, Gregor N. C. Simm, Ralf Drautz, Christoph Ortner, Boris Kozinsky, and Gábor Csányi · 2022
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Alexander Dunn, Qi Wang, Alex Ganose, Daniel Dopp, and Anubhav Jain · 2020
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On the universality of rotation equivariant point cloud networks
Nadav Dym and Haggai Maron · 2020
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Generalizing convolutional neural networks for equivariance to lie groups on arbitrary continuous data
Marc Finzi, Samuel Stanton, Pavel Izmailov, and Andrew Gordon Wilson · 2020
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Se(3)-transformers: 3d roto-translation equivariant attention networks
Fabian B Fuchs, Daniel E Worrall, Volker Fischer, and Max Welling · 2020
Cited alongside, same era.
Fast and uncertainty-aware directional message passing for non-equilibrium molecules
Johannes Gasteiger, Shankari Giri, Johannes T Margraf, and Stephan Günnemann · 2020
Cited alongside, same era.
Learning to navigate the synthetically accessible chemical space using reinforcement learning
Sai Krishna Gottipati, Boris Sattarov, Sufeng Niu, Yashaswi Pathak, Haoran Wei, Shengchao Liu, Simon Blackburn, Karam Thomas, Connor Coley, Jian Tang, et al · 2020
Cited alongside, same era.
Intrinsic-extrinsic convolution and pooling for learning on 3d protein structures
Pedro Hermosilla, Marco Schäfer, Matěj Lang, Gloria Fackelmann, Pere Pau Vázquez, Barbora Kozlíková, Michael Krone, Tobias Ritschel, and Timo Ropinski · 2020
Cited alongside, same era.
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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
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Robust deep learning–based protein sequence design using proteinmpnn
Justas Dauparas, Ivan Anishchenko, Nathaniel Bennett, Hua Bai, Robert J Ragotte, Lukas F Milles, Basile IM Wicky, Alexis Courbet, Rob J de Haas, Neville Bethel, et al · 2022
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Attentive walk-aggregating graph neural networks
Mehmet F Demirel, Shengchao Liu, Siddhant Garg, Zhenmei Shi, and Yingyu Liang · 2022
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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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Translation between molecules and natural language
Carl Edwards, Tuan Lai, Kevin Ros, Garrett Honke, and Heng Ji · 2022
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e3nn: Euclidean neural networks
Mario Geiger and Tess Smidt · 2022
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Learning inverse folding from millions of predicted structures
Chloe Hsu, Robert Verkuil, Jason Liu, Zeming Lin, Brian Hie, Tom Sercu, Adam Lerer, and Alexander Rives · 2022
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Yuanfeng Ji, Lu Zhang, Jiaxiang Wu, Bingzhe Wu, Long-Kai Huang, Tingyang Xu, Yu Rong, Lanqing Li, Jie Ren, Ding Xue, et al · 2022
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3d equivariant molecular graph pretraining
Rui Jiao, Jiaqi Han, Wenbing Huang, Yu Rong, and Yang Liu · 2022
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Energy-motivated equivariant pretraining for 3d molecular graphs
Rui Jiao, Jiaqi Han, Wenbing Huang, Yu Rong, and Yang Liu · 2022
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Torsional diffusion for molecular conformer generation
Bowen Jing, Gabriele Corso, Jeffrey Chang, Regina Barzilay, and Tommi Jaakkola · 2022
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Equiformer: Equivariant graph attention transformer for 3d atomistic graphs
Yi-Lun Liao and Tess Smidt · 2022
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Molecular geometry pretraining with se (3)-invariant denoising distance matching
Shengchao Liu, Hongyu Guo, and Jian Tang · 2022
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Multi-modal molecule structure-text model for text-based retrieval and editing
Shengchao Liu, Weili Nie, Chengpeng Wang, Jiarui Lu, Zhuoran Qiao, Ling Liu, Jian Tang, Chaowei Xiao, and Anima Anandkumar · 2022
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Structured multi-task learning for molecular property prediction
Shengchao Liu, Meng Qu, Zuobai Zhang, Huiyu Cai, and Jian Tang · 2022
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Learning local equivariant representations for large-scale atomistic dynamics
Albert Musaelian, Simon Batzner, Anders Johansson, Lixin Sun, Cameron J Owen, Mordechai Kornbluth, and Boris Kozinsky · 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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3d infomax improves gnns for molecular property prediction
Hannes Stärk, Dominique Beaini, Gabriele Corso, Prudencio Tossou, Christian Dallago, Stephan Günnemann, and Pietro Liò · 2022
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A molecular multimodal foundation model associating molecule graphs with natural language
Bing Su, Dazhao Du, Zhao Yang, Yujie Zhou, Jiangmeng Li, Anyi Rao, Hao Sun, Zhiwu Lu, and Ji-Rong Wen · 2022
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Rethinking of graph pretraining on molecular representation
Ruoxi Sun, Hanjun Dai, and Adams Wei Yu · 2022
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Evaluating self-supervised learning for molecular graph embeddings
Hanchen Wang, Jean Kaddour, Shengchao Liu, Jian Tang, Matt Kusner, Joan Lasenby, and Qi Liu · 2022
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Learning hierarchical protein representations via complete 3d graph networks
Limei Wang, Haoran Liu, Yi Liu, Jerry Kurtin, and Shuiwang Ji · 2022
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Deep generative model for periodic graphs
Shiyu Wang, Xiaojie Guo, and Liang Zhao · 2022
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Pre-training via denoising for molecular property prediction
Sheheryar Zaidi, Michael Schaarschmidt, James Martens, Hyunjik Kim, Yee Whye Teh, Alvaro Sanchez-Gonzalez, Peter Battaglia, Razvan Pascanu, and Jonathan Godwin · 2022
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A deep-learning system bridging molecule structure and biomedical text with comprehension comparable to human professionals
Zheni Zeng, Yuan Yao, Zhiyuan Liu, and Maosong Sun · 2022
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Ontoprotein: Protein pretraining with gene ontology embedding
Ningyu Zhang, Zhen Bi, Xiaozhuan Liang, Siyuan Cheng, Haosen Hong, Shumin Deng, Jiazhang Lian, Qiang Zhang, and Huajun Chen · 2022
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Protein representation learning by geometric structure pretraining
Zuobai Zhang, Minghao Xu, Arian Jamasb, Vijil Chenthamarakshan, Aurelie Lozano, Payel Das, and Jian Tang · 2022
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Torchdrug: A powerful and flexible machine learning platform for drug discovery
Zhaocheng Zhu, Chence Shi, Zuobai Zhang, Shengchao Liu, Minghao Xu, Xinyu Yuan, Yangtian Zhang, Junkun Chen, Huiyu Cai, Jiarui Lu, et al · 2022
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A new perspective on building efficient and expressive 3d equivariant graph neural networks, 2023
Weitao Du, Yuanqi Du, Limei Wang, Dieqiao Feng, Guifeng Wang, Shuiwang Ji, Carla Gomes, and Zhi-Ming Ma · 2023
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Continuous-discrete convolution for geometry-sequence modeling in proteins
Hehe Fan, Zhangyang Wang, Yi Yang, and Mohan Kankanhalli · 2023
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Daniel Flam-Shepherd and Alán Aspuru-Guzik · 2023
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Protein design with guided discrete diffusion
Nate Gruver, Samuel Stanton, Nathan C Frey, Tim GJ Rudner, Isidro Hotzel, Julien Lafrance-Vanasse, Arvind Rajpal, Kyunghyun Cho, and Andrew Gordon Wilson · 2023
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Efficient evolution of human antibodies from general protein language models
Brian L Hie, Varun R Shanker, Duo Xu, Theodora UJ Bruun, Payton A Weidenbacher, Shaogeng Tang, Wesley Wu, John E Pak, and Peter S Kim · 2023
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Structure-based drug design with geometric deep learning
Clemens Isert, Kenneth Atz, and Gisbert Schneider · 2023
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A group symmetric stochastic differential equation model for molecule multi-modal pretraining
Shengchao Liu, Weitao Du, Zhiming Ma, Hongyu Guo, and Jian Tang · 2023
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Molecular geometry pretraining with SE(3)-invariant denoising distance matching
Shengchao Liu, Hongyu Guo, and Jian Tang · 2023
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Graphcg: Unsupervised discovery of steerable factors in graphs, 2023
Shengchao Liu, Chengpeng Wang, Weili Nie, Hanchen Wang, Jiarui Lu, Bolei Zhou, and Jian Tang · 2023
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Chatgpt-powered conversational drug editing using retrieval and domain feedback
Shengchao Liu, Jiongxiao Wang, Yijin Yang, Chengpeng Wang, Ling Liu, Hongyu Guo, and Chaowei Xiao · 2023
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A text-guided protein design framework
Shengchao Liu, Yutao Zhu, Jiarui Lu, Zhao Xu, Weili Nie, Anthony Gitter, Chaowei Xiao, Jian Tang, Hongyu Guo, and Anima Anandkumar · 2023
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Learning hierarchical protein representations via complete 3d graph networks
Limei Wang, Haoran Liu, Yi Liu, Jerry Kurtin, and Shuiwang Ji · 2023
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Leveraging domain relations for domain generalization
Huaxiu Yao, Xinyu Yang, Xinyi Pan, Shengchao Liu, Pang Wei Koh, and Chelsea Finn · 2023
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Gimlet: A unified graph-text model for instruction-based molecule zero-shot learning
Haiteng Zhao, Shengchao Liu, Chang Ma, Hannan Xu, Jie Fu, Zhi-Hong Deng, Lingpeng Kong, and Qi Liu · 2023
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