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Geometric graphs are a special kind of graph with geometric features, which are vital to model many scientific problems.
Language models are few-shot learners
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New cubic perovskites for one-and two-photon water splitting using the computational materials repository
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Computational screening of perovskite metal oxides for optimal solar light capture
Castelli I E, Olsen T, Datta S, Landis D D, Dahl S, Thygesen K S, Jacobsen K W · 2012
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Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings
Lipinski C A, Lombardo F, Dominy B W, Feeney P J · 2012
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SKEMPI: a structural kinetic and energetic database of mutant protein interactions and its use in empirical models
Moal I H, Fernández-Recio J · 2012
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Commentary: The materials project: A materials genome approach to accelerating materials innovation
Jain A, Ong S P, Hautier G, others · 2013
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Quantum chemistry structures and properties of 134 kilo molecules
Ramakrishnan R, Dral P O, Rupp M, Lilienfeld v O A · 2014
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SAbDab: the structural antibody database
Dunbar J, Krawczyk K, Leem J, Baker T, Fuchs A, Georges G, Shi J, Deane C M · 2014
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Euclidean distance geometry and applications
Liberti L, Lavor C, Maculan N, Mucherino A · 2014
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Auto-encoding variational bayes
Kingma D P, Welling M · 2014
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Zinc 15 – ligand discovery for everyone
Sterling T, Irwin J J · 2015
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Updates to the integrated protein–protein interaction benchmarks: docking benchmark version 5 and affinity benchmark version 2
Vreven T, Moal I H, Vangone A, Pierce B G, others · 2015
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U-net: Convolutional networks for biomedical image segmentation
Ronneberger O, Fischer P, Brox T · 2015
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Improving protein-ligand binding site prediction accuracy by classification of inner pocket points using local features
Krivák R, Hoksza D · 2015
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UniRef clusters: a comprehensive and scalable alternative for improving sequence similarity searches
Suzek B E, Wang Y, Huang H, McGarvey P B, Wu C H, Consortium U · 2015
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Peptide therapeutics: current status and future directions
Fosgerau K, Hoffmann T · 2015
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Interaction networks for learning about objects, relations and physics
Battaglia P, Pascanu R, Lai M, Jimenez Rezende D, others · 2016
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More than a million ways to be pushed. a high-fidelity experimental dataset of planar pushing
Yu K T, Bauza M, Fazeli N, Rodriguez A · 2016
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MDAnalysis: a python package for the rapid analysis of molecular dynamics simulations
Gowers R J, Linke M, Barnoud J, Reddy T J, Melo M N, Seyler S L, Domanski J, Dotson D L, Buchoux S, Kenney I M, others · 2016
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Accurate de novo design of hyperstable constrained peptides
Bhardwaj G, Mulligan V K, Bahl C D, Gilmore J M, Harvey P J, Cheneval O, Buchko G W, Pulavarti S V, Kaas Q, Eletsky A, others · 2016
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Quantum-chemical insights from deep tensor neural networks
Schütt K T, Arbabzadah F, Chmiela S, Müller K R, Tkatchenko A · 2017
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Neural message passing for quantum chemistry
Gilmer J, Schoenholz S S, Riley P F, Vinyals O, Dahl G E · 2017
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Forging the basis for developing protein–ligand interaction scoring functions
Liu Z, Su M, Han L, Liu J, Yang Q, Li Y, Wang R · 2017
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Searching for activation functions, 2017
Ramachandran P, Zoph B, Le Q V · 2017
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Attention is all you need
Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez A N, Kaiser Ł, Polosukhin I · 2017
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3d deep convolutional neural networks for amino acid environment similarity analysis
Torng W, Altman R B · 2017
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Machine learning of accurate energy-conserving molecular force fields
Chmiela S, Tkatchenko A, Sauceda H E, Poltavsky I, Schütt K T, Müller K R · 2017
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Molecular dynamics trajectory for benchmarking mdanalysis, 6 2017, 2017
Seyler S, Beckstein O · 2017
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Deeploc: prediction of protein subcellular localization using deep learning
Almagro Armenteros J J, Sønderby C K, Sønderby S K, Nielsen H, Winther O · 2017
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PubChemQC Project: A large-scale first-principles electronic structure database for data-driven chemistry
Nakata M, Shimazaki T · 2017
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DeepSite: protein-binding site predictor using 3d-convolutional neural networks
Jiménez J, Doerr S, Martínez-Rosell G, Rose A S, De Fabritiis G · 2017
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Tensor field networks: Rotation-and translation-equivariant neural networks for 3d point clouds
Thomas N, Smidt T, Kearnes S, Yang L, Li L, Kohlhoff K, Riley P · 2018
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RosettaAntibodyDesign (RAbD): A general framework for computational antibody design
Adolf-Bryfogle J, Kalyuzhniy O, Kubitz M, Weitzner B D, Hu X, Adachi Y, Schief W R, Dunbrack Jr R L · 2018
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Neural relational inference for interacting systems
Kipf T, Fetaya E, Wang K C, Welling M, Zemel R · 2018
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MoleculeNet: a benchmark for molecular machine learning
Wu Z, Ramsundar B, Feinberg E, Gomes J, Geniesse C, Pappu A S, Leswing K, Pande V · 2018
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Schnet–a deep learning architecture for molecules and materials
Schütt K T, Sauceda H E, Kindermans P J, Tkatchenko A, Müller K R · 2018
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Quaternion convolutional neural networks
Zhu X, Xu Y, Xu H, Chen C · 2018
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Introduction to quantum mechanics
Griffiths D J, Schroeter D F · 2018
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3D Steerable CNNs: Learning rotationally equivariant features in volumetric data
Weiler M, Geiger M, Welling M, Boomsma W, Cohen T S · 2018
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Learning particle dynamics for manipulating rigid bodies, deformable objects, and fluids
Li Y, Wu J, Tedrake R, Tenenbaum J B, Torralba A · 2018
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Flexible neural representation for physics prediction
Mrowca D, Zhuang C, Wang E, Haber N, Fei-Fei L, Tenenbaum J B, Yamins D L K · 2018
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SphereNet: Learning spherical representations for detection and classification in omnidirectional images
Coors B, Condurache A P, Geiger A · 2018
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Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties
Xie T, Grossman J C · 2018
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Clustering huge protein sequence sets in linear time
Steinegger M, Söding J · 2018
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Machine learning with force-field-inspired descriptors for materials: Fast screening and mapping energy landscape
Choudhary K, DeCost B, Tavazza F · 2018
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Improving language understanding by generative pre-training, 2018
Radford A, Narasimhan K, Salimans T, Sutskever I, others · 2018
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Equivariant flows: sampling configurations for multi-body systems with symmetric energies
Köhler J, Klein L, Noé F · 2019
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Cormorant: Covariant molecular neural networks
Anderson B, Hy T S, Kondor R · 2019
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Atomic cluster expansion for accurate and transferable interatomic potentials
Drautz R · 2019
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Hamiltonian graph networks with ode integrators
Sanchez-Gonzalez A, Bapst V, Cranmer K, Battaglia P · 2019
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Symmetry-adapted generation of 3d point sets for the targeted discovery of molecules
Gebauer N, Gastegger M, Schütt K · 2019
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Generative models for graph-based protein design
Ingraham J, Garg V, Barzilay R, Jaakkola T · 2019
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Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences
Rives A, Meier J, Sercu T, Goyal S, Lin Z, Liu J, Guo D, Ott M, Zitnick C L, Ma J, Fergus R · 2019
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End-to-end learning on 3d protein structure for interface prediction
Townshend R, Bedi R, Suriana P, Dror R · 2019
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Graph networks as a universal machine learning framework for molecules and crystals
Chen C, Ye W, Zuo Y, Zheng C, Ong S P · 2019
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SCOPe: classification of large macromolecular structures in the structural classification of proteins—extended database
Chandonia J M, Fox N K, Brenner S E · 2019
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Evaluating protein transfer learning with tape
Rao R, Bhattacharya N, Thomas N, Duan Y, Chen P, Canny J, Abbeel P, Song Y · 2019
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NetSurfP-2.0: Improved prediction of protein structural features by integrated deep learning
Klausen M S, Jespersen M C, Nielsen H, others · 2019
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Critical assessment of methods of protein structure prediction (casp)—round xiii
Kryshtafovych A, Schwede T, Topf M, Fidelis K, Moult J · 2019
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Comparative assessment of scoring functions: The casf-2016 update
Su M, Yang Q, Du Y, Feng G, Liu Z, Li Y, Wang R · 2019
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SKEMPI 2.0: an updated benchmark of changes in protein–protein binding energy, kinetics and thermodynamics upon mutation
Jankauskaitė J, Jiménez-García B, Dapkūnas J, Fernández-Recio J, Moal I H · 2019
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PepBDB: a comprehensive structural database of biological peptide–protein interactions
Wen Z, He J, Tao H, Huang S Y · 2019
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Smiles-bert: large scale unsupervised pre-training for molecular property prediction
Wang S, Guo Y, Wang Y, Sun H, Huang J · 2019
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A comprehensive review on current advances in peptide drug development and design
Lee A C L, Harris J L, Khanna K K, Hong J H · 2019
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Language models are unsupervised multitask learners
Radford A, Wu J, Child R, Luan D, Amodei D, Sutskever I, others · 2019
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Directional message passing for molecular graphs
Klicpera J, Groß J, Günnemann S · 2020
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SE(3)-Transformers: 3d roto-translation equivariant attention networks
Fuchs F, Worrall D, Fischer V, Welling M · 2020
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Learning to simulate complex physics with graph networks
Sanchez-Gonzalez A, Godwin J, Pfaff T, Ying R, Leskovec J, Battaglia P · 2020
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Improved protein structure prediction using potentials from deep learning
Senior A W, Evans R, Jumper J, Kirkpatrick J, Sifre L, Green T, Qin C, Žídek A, Nelson A W, Bridgland A, others · 2020
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Theoretical aspects of group equivariant neural networks, 2020
Esteves C · 2020
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A comprehensive survey on graph neural networks
Wu Z, Pan S, Chen F, Long G, Zhang C, Philip S Y · 2020
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Fast and uncertainty-aware directional message passing for non-equilibrium molecules
Gasteiger J, Giri S, Margraf J T, Günnemann S · 2020
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Generalizing convolutional neural networks for equivariance to lie groups on arbitrary continuous data
Finzi M, Stanton S, Izmailov P, Wilson A G · 2020
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Quaternion product units for deep learning on 3d rotation groups
Zhang X, Qin S, Xu Y, Xu H · 2020
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On the universality of rotation equivariant point cloud networks
Dym N, Maron H · 2020
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Equivariant Flows: Exact likelihood generative learning for symmetric densities
Köhler J, Klein L, Noe F · 2020
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Hierarchical, rotation‐equivariant neural networks to select structural models of protein complexes
Eismann S, Townshend R J, Thomas N, Jagota M, Jing B, Dror R O · 2020
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Deciphering interaction fingerprints from protein molecular surfaces using geometric deep learning
Gainza P, Sverrisson F, Monti F, Rodola E, Boscaini D, Bronstein M, Correia B · 2020
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Learning neural generative dynamics for molecular conformation generation
Xu M, Luo S, Bengio Y, Peng J, Tang J · 2020
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Three-dimensional convolutional neural networks and a cross-docked data set for structure-based drug design
Francoeur P G, Masuda T, Sunseri J, Jia A, Iovanisci R B, Snyder I, Koes D R · 2020
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Airss data for carbon at 10gpa and the c+n+h+o system at 1gpa, 2020
Pickard C J · 2020
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The joint automated repository for various integrated simulations (jarvis) for data-driven materials design
Choudhary K, Garrity K F, Reid A C, DeCost B, Biacchi A J, Hight Walker A R, Trautt Z, Hattrick-Simpers J, Kusne A G, Centrone A, others · 2020
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FARFAR2: improved de novo rosetta prediction of complex global rna folds
Watkins A M, Rangan R, Das R · 2020
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Augmented normalizing flows: Bridging the gap between generative flows and latent variable models
Huang C W, Dinh L, Courville A · 2020
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Strategies for pre-training graph neural networks
Hu W, Liu B, Gomes J, Zitnik M, Liang P, Pande V, Leskovec J · 2020
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Self-supervised graph transformer on large-scale molecular data
Rong Y, Bian Y, Xu T, Xie W, WEI Y, Huang W, Huang J · 2020
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Open graph benchmark: Datasets for machine learning on graphs
Hu W, Fey M, Zitnik M, Dong Y, Ren H, Liu B, Catasta M, Leskovec J · 2020
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Automated exploration of the low-energy chemical space with fast quantum chemical methods
Pracht P, Bohle F, Grimme S · 2020
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Transformer protein language models are unsupervised structure learners
Rao R, Meier J, Sercu T, Ovchinnikov S, Rives A · 2020
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Geometric Deep Learning: Grids, groups, graphs, geodesics, and gauges, 2021
Bronstein M M, Bruna J, Cohen T, Veličković P · 2021
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GemNet: Universal directional graph neural networks for molecules
Klicpera J, Becker F, Günnemann S · 2021
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E (n) equivariant graph neural networks
Satorras V G, Hoogeboom E, Welling M · 2021
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Equivariant message passing for the prediction of tensorial properties and molecular spectra
Schütt K, Unke O, Gastegger M · 2021
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Accurate prediction of protein structures and interactions using a three-track neural network
Baek M, DiMaio F, Anishchenko I, Dauparas J, others · 2021
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Geometric deep learning of rna structure
Townshend R J L, Eismann S, Watkins A M, Rangan R, Karelina M, Das R, Dror R O · 2021
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GNINA 1.0: molecular docking with deep learning
McNutt A T, Francoeur P, Aggarwal R, Masuda T, Meli R, Ragoza M, Sunseri J, Koes D R · 2021
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Open catalyst 2020 (oc20) dataset and community challenges
Chanussot* L, Das* A, Goyal* S, others · 2021
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Geometric deep learning on molecular representations
Atz K, Grisoni F, Schneider G · 2021
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Scalars are universal: Equivariant machine learning, structured like classical physics
Villar S, Hogg D W, Storey-Fisher K, Yao W, Blum-Smith B · 2021
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Roto-translated local coordinate frames for interacting dynamical systems
Kofinas M, Nagaraja N S, Gavves E · 2021
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Learning from protein structure with geometric vector perceptrons
Jing B, Eismann S, Suriana P, Townshend R J L, Dror R · 2021
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Frame averaging for invariant and equivariant network design
Puny O, Atzmon M, Smith E J, Misra I, Grover A, Ben-Hamu H, Lipman Y · 2021
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Do transformers really perform badly for graph representation?
Ying C, Cai T, Luo S, Zheng S, Ke G, He D, Shen Y, Liu T Y · 2021
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Lietransformer: equivariant self-attention for lie groups
Hutchinson M J, Le Lan C, Zaidi S, Dupont E, Teh Y W, Kim H · 2021
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Nested graph neural networks
Zhang M, Li P · 2021
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OGB-LSC: A large-scale challenge for machine learning on graphs
Hu W, Fey M, Ren H, Nakata M, Dong Y, Leskovec J · 2021
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Rotation invariant graph neural networks using spin convolutions
Shuaibi M, Kolluru A, Das A, Grover A, Sriram A, Ulissi Z, Zitnick C L · 2021
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An end-to-end framework for molecular conformation generation via bilevel programming
On the expressive power of geometric graph neural networks
Joshi C K, Bodnar C, Mathis S V, Cohen T, Liò P · 2023
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Graph network simulators can learn discontinuous, rigid contact dynamics
Allen K R, Guevara T L, Rubanova Y, Stachenfeld K, Sanchez-Gonzalez A, Battaglia P, Pfaff T · 2023
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Learning rigid dynamics with face interaction graph networks
Allen K R, Rubanova Y, Lopez-Guevara T, Whitney W F, Sanchez-Gonzalez A, Battaglia P, Pfaff T · 2023
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EqMotion: Equivariant multi-agent motion prediction with invariant interaction reasoning
Xu C, Tan R T, Tan Y, Chen S, Wang Y G, Wang X, Wang Y · 2023
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Geometric latent diffusion models for 3d molecule generation
Xu M, Powers A S, Dror R O, Ermon S, Leskovec J · 2023
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Mdm: Molecular diffusion model for 3d molecule generation
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Xu M, Wang W, Luo S, Shi C, Bengio Y, Gomez-Bombarelli R, Tang J · 2021
Cited alongside, same era.
Learning gradient fields for molecular conformation generation
Shi C, Luo S, Xu M, Tang J · 2021
Cited alongside, same era.
Predicting molecular conformation via dynamic graph score matching
Luo S, Shi C, Xu M, Tang J · 2021
Cited alongside, same era.
E(n) equivariant normalizing flows
Satorras V G, Hoogeboom E, Fuchs F B, Posner I, Welling M · 2021
Cited alongside, same era.
GeoMol: Torsional geometric generation of molecular 3d conformer ensembles
Ganea O E, Pattanaik L, Coley C W, Barzilay R, Jensen K, Green W, Jaakkola T S · 2021
Cited alongside, same era.
Structure-based protein function prediction using graph convolutional networks
Gligorijević V, Renfrew P D, Kosciolek T, Leman J K, Berenberg D, Vatanen T, Chandler C, Taylor B C, Fisk I M, Vlamakis H, others · 2021
Cited alongside, same era.
Highly accurate protein structure prediction with AlphaFold
Jumper J, Evans R, Pritzel A, Green T, others · 2021
Cited alongside, same era.
Huang L, Zhang H, Xu T, Wong K C · 2023
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MolDiff: Addressing the atom-bond inconsistency problem in 3D molecule diffusion generation
Peng X, Guan J, Liu Q, Ma J · 2023
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MPerformer: An se (3) transformer-based molecular perceptron
Wang F, Xu H, Chen X, Lu S, Deng Y, Huang W · 2023
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Equivariant energy-guided SDE for inverse molecular design
Bao F, Zhao M, Hao Z, Li P, Li C, Zhu J · 2023
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Coarse-to-fine: a hierarchical diffusion model for molecule generation in 3d
Qiang B, Song Y, Xu M, Gong J, Gao B, Zhou H, Ma W Y, Lan Y · 2023
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Energy-motivated equivariant pretraining for 3d molecular graphs
Jiao R, Han J, Huang W, Rong Y, Liu Y · 2023
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Molecular geometry pretraining with SE(3)-invariant denoising distance matching
Liu S, Guo H, Tang J · 2023
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Pre-training via denoising for molecular property prediction
Zaidi S, Schaarschmidt M, Martens J, Kim H, Teh Y W, Sanchez-Gonzalez A, Battaglia P, Pascanu R, Godwin J · 2023
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Uni-Mol: A universal 3d molecular representation learning framework
Zhou G, Gao Z, Ding Q, Zheng H, Xu H, Wei Z, Zhang L, Ke G · 2023
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One transformer can understand both 2d & 3d molecular data
Luo S, Chen T, Xu Y, Zheng S, Liu T Y, Wang L, He D · 2023
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A group symmetric stochastic differential equation model for molecule multi-modal pretraining
Liu S, Du W, Ma Z M, Guo H, Tang J · 2023
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Sliced Denoising: A physics-informed molecular pre-training method
Ni Y, Feng S, Ma W Y, Ma Z M, Lan Y · 2023
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Fractional denoising for 3d molecular pre-training
Feng S, Ni Y, Lan Y, Ma Z M, Ma W Y · 2023
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Protein model quality assessment using rotation-equivariant transformations on point clouds
Eismann S, Suriana P, Jing B, Townshend R J, Dror R O · 2023
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3d-equivariant graph neural networks for protein model quality assessment
Chen C, Chen X, Morehead A, Wu T, Cheng J · 2023
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EquiPocket: an e (3)-equivariant geometric graph neural network for ligand binding site prediction
Zhang Y, Huang W, Wei Z, Yuan Y, Ding Z · 2023
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Predicting the locations of cryptic pockets from single protein structures using the pocketminer graph neural network
Meller A, Ward M D, Borowsky J H, Lotthammer J M, others · 2023
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Structure-informed language models are protein designers
Zheng Z, Deng Y, Xue D, Zhou Y, Ye F, Gu Q · 2023
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KW-Design: Pushing the limit of protein deign via knowledge refinement
Gao Z, Tan C, Chen X, Zhang Y, Xia J, Li S, Li S Z · 2023
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Generalized biomolecular modeling and design with rosettafold all-atom
Krishna R, Wang J, Ahern W, Sturmfels P, Venkatesh P, Kalvet I, Lee G R, Morey-Burrows F S, Anishchenko I, Humphreys I R, others · 2023
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EigenFold: Generative protein structure prediction with diffusion models
Jing B, Erives E, Pao-Huang P, Corso G, Berger B, Jaakkola T S · 2023
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Evolutionary-scale prediction of atomic-level protein structure with a language model
Lin Z, Akin H, Rao R, Hie B, Zhu Z, Lu W, Smetanin N, Verkuil R, Kabeli O, Shmueli Y, others · 2023
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A method for multiple-sequence-alignment-free protein structure prediction using a protein language model
Fang X, Wang F, Liu L, He J, Lin D, Xiang Y, Zhu K, Zhang X, Wu H, Li H, others · 2023
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Protein sequence and structure co-design with equivariant translation
Shi C, Wang C, Lu J, Zhong B, Tang J · 2023
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Self-supervised pocket pretraining via protein fragment-surroundings alignment
Gao B, Jia Y, Mo Y, Ni Y, Ma W Y, Ma Z M, Lan Y · 2023
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Multi-level protein structure pre-training via prompt learning
Wang Z, Zhang Q, Shuang-Wei H, Yu H, Jin X, Gong Z, Chen H · 2023
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Duan C, Du Y, Jia H, Kulik H J · 2023
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Generalist equivariant transformer towards 3d molecular interaction learning
Kong X, Huang W, Liu Y · 2023
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Geometric graph learning for protein mutation effect prediction
Zhao K, Rong Y, Jiang B, Tang J, Zhang H, Yu J X, Zhao P · 2023
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