Simple GNN Regularisation for 3D Molecular Property Prediction & Beyond

15 June 2021

Jonathan Godwin

Michael Schaarschmidt

Alex Gaunt

Alvaro Sanchez-Gonzalez

Papers citing "Simple GNN Regularisation for 3D Molecular Property Prediction & Beyond"

40 / 40 papers shown

Title
Conformation Generation using Transformer Flows Sohil Shah V. Koltun MedIm AI4CE 21 0 0 16 Nov 2024
Harnessing the Power of Noise: A Survey of Techniques and Applications Reyhaneh Abdolazimi Shengmin Jin Pramod K. Varshney Reza Zafarani 16 0 0 08 Oct 2024
Advancing Molecular Machine (Learned) Representations with Stereoelectronics-Infused Molecular Graphs Daniil A. Boiko Thiago Reschutzegger Benjamín Sánchez-Lengeling Samuel M. Blau Gabe Gomes GNN AI4CE 15 1 0 08 Aug 2024
Evaluating representation learning on the protein structure universe Arian R. Jamasb Alex Morehead Chaitanya K. Joshi Zuobai Zhang Kieran Didi ... Charles Harris Jian Tang Jianlin Cheng Pietro Lio Tom L. Blundell SSL 31 12 0 19 Jun 2024
DualBind: A Dual-Loss Framework for Protein-Ligand Binding Affinity Prediction Meng Liu Saee Paliwal 21 0 0 11 Jun 2024
On the Scalability of GNNs for Molecular Graphs Maciej Sypetkowski Frederik Wenkel Farimah Poursafaei Nia Dickson Karush Suri Philip Fradkin Dominique Beaini GNN AI4CE 34 11 0 17 Apr 2024
Entropy Aware Message Passing in Graph Neural Networks Philipp Nazari Oliver Lemke Davide Guidobene Artiom Gesp 25 0 0 07 Mar 2024
PASCL: Supervised Contrastive Learning with Perturbative Augmentation for Particle Decay Reconstruction Junjian Lu Siwei Liu Dmitrii Kobylianski Etienne Dreyer Eilam Gross Shangsong Liang 19 3 0 18 Feb 2024
Impact of Domain Knowledge and Multi-Modality on Intelligent Molecular Property Prediction: A Systematic Survey Taojie Kuang Pengfei Liu Zhixiang Ren AI4CE 37 1 0 11 Feb 2024
Triplet Interaction Improves Graph Transformers: Accurate Molecular Graph Learning with Triplet Graph Transformers Md Shamim Hussain Mohammed J. Zaki D. Subramanian ViT 19 4 0 07 Feb 2024
Sliced Denoising: A Physics-Informed Molecular Pre-Training Method Yuyan Ni Shikun Feng Wei-Ying Ma Zhiming Ma Yanyan Lan DiffM AI4CE 24 9 0 03 Nov 2023
Investigating the Behavior of Diffusion Models for Accelerating Electronic Structure Calculations D. Rothchild Andrew S. Rosen Eric Taw Connie Robinson Joseph E. Gonzalez Aditi S. Krishnapriyan DiffM 11 2 0 02 Nov 2023
$Generating QM1B with PySCF$_{\text{IPU}}$$ Generating QM1B with PySCF $_{\text{IPU}}$ Alexander Mathiasen Hatem Helal Kerstin Klaser Paul Balanca Josef Dean Carlo Luschi Dominique Beaini Andrew Fitzgibbon Dominic Masters 18 1 0 02 Nov 2023
Improving Compositional Generalization Using Iterated Learning and Simplicial Embeddings Yi Ren Samuel Lavoie Mikhail Galkin Danica J. Sutherland Aaron Courville 25 15 0 28 Oct 2023
May the Force be with You: Unified Force-Centric Pre-Training for 3D Molecular Conformations Rui Feng Qi Zhu Huan Tran Binghong Chen Aubrey Toland R. Ramprasad Chao Zhang AI4CE 14 9 0 24 Aug 2023
Fractional Denoising for 3D Molecular Pre-training Shi Feng Yuyan Ni Yanyan Lan Zhiming Ma Wei-Ying Ma DiffM AI4CE 30 25 0 20 Jul 2023
Accelerating Molecular Graph Neural Networks via Knowledge Distillation Filip Ekstrom Kelvinius D. Georgiev Artur P. Toshev Johannes Gasteiger 19 6 0 26 Jun 2023
Principles for Initialization and Architecture Selection in Graph Neural Networks with ReLU Activations G. Dezoort Boris Hanin AI4CE 17 3 0 20 Jun 2023
Smooth, exact rotational symmetrization for deep learning on point clouds Sergey Pozdnyakov Michele Ceriotti 3DPC 30 25 0 30 May 2023
Evaluation of the MACE Force Field Architecture: from Medicinal Chemistry to Materials Science D. P. Kovács Ilyes Batatia E. Arany Gábor Csányi AI4CE 13 80 0 23 May 2023
FAENet: Frame Averaging Equivariant GNN for Materials Modeling Alexandre Duval Victor Schmidt A. Garcia Santiago Miret Fragkiskos D. Malliaros Yoshua Bengio David Rolnick 20 52 0 28 Apr 2023
Diffusion-based Generative AI for Exploring Transition States from 2D Molecular Graphs Seonghwan Kim Jeheon Woo Woo Youn Kim DiffM 25 25 0 20 Apr 2023
GeoTMI:Predicting quantum chemical property with easy-to-obtain geometry via positional denoising Hyeonsu Kim Jeheon Woo Seonghwan Kim Seokhyun Moon Jun Hyeong Kim Woo Youn Kim AI4CE 19 6 0 28 Mar 2023
Trainable Projected Gradient Method for Robust Fine-tuning Junjiao Tian Xiaoliang Dai Chih-Yao Ma Zecheng He Yen-Cheng Liu Z. Kira 43 30 0 19 Mar 2023
Wigner kernels: body-ordered equivariant machine learning without a basis Filippo Bigi Sergey Pozdnyakov Michele Ceriotti 19 15 0 07 Mar 2023
DR-Label: Improving GNN Models for Catalysis Systems by Label Deconstruction and Reconstruction Bo-Lan Wang Chen Liang Jiaze Wang Furui Liu Shaogang Hao Dong Li Jianye Hao Guangyong Chen Xiaolong Zou Pheng-Ann Heng 34 3 0 06 Mar 2023
SupSiam: Non-contrastive Auxiliary Loss for Learning from Molecular Conformers Michael R. Maser Ji Won Park J. Lin Jae Hyeon Lee Nathan C. Frey Andrew Watkins 8 5 0 15 Feb 2023
GPS++: Reviving the Art of Message Passing for Molecular Property Prediction Dominic Masters Josef Dean Kerstin Klaser Zhiyi Li Sam Maddrell-Mander ... D. Beker Andrew Fitzgibbon Shenyang Huang Ladislav Rampášek Dominique Beaini 28 8 0 06 Feb 2023
Latent Graph Inference using Product Manifolds Haitz Sáez de Ocáriz Borde Anees Kazi Federico Barbero Pietro Lio' BDL 19 18 0 26 Nov 2022
Curvature-informed multi-task learning for graph networks Alexander New M. Pekala Nam Q. Le Janna Domenico C. Piatko Christopher D. Stiles 17 4 0 02 Aug 2022
Spherical Channels for Modeling Atomic Interactions C. L. Zitnick Abhishek Das Adeesh Kolluru Janice Lan Muhammed Shuaibi Anuroop Sriram Zachary W. Ulissi Brandon M. Wood 79 57 0 29 Jun 2022
Equiformer: Equivariant Graph Attention Transformer for 3D Atomistic Graphs Yi-Lun Liao Tess E. Smidt 75 211 0 23 Jun 2022
The Open Catalyst 2022 (OC22) Dataset and Challenges for Oxide Electrocatalysts Richard Tran Janice Lan Muhammed Shuaibi Brandon M. Wood Siddharth Goyal ... Jehad Abed Oleksandr Voznyy Edward H. Sargent Zachary W. Ulissi C. L. Zitnick 13 170 0 17 Jun 2022
The CLRS Algorithmic Reasoning Benchmark Petar Velivcković Adria Puigdomenech Badia David Budden Razvan Pascanu Andrea Banino Mikhail Dashevskiy R. Hadsell Charles Blundell 157 87 0 31 May 2022
Towards Training Billion Parameter Graph Neural Networks for Atomic Simulations Anuroop Sriram Abhishek Das Brandon M. Wood Siddharth Goyal C. L. Zitnick AI4CE 17 27 0 18 Mar 2022
Benchmarking Graphormer on Large-Scale Molecular Modeling Datasets Yu Shi Shuxin Zheng Guolin Ke Yifei Shen Jiacheng You Jiyan He Shengjie Luo Chang-Shu Liu Di He Tie-Yan Liu AI4CE 26 65 0 09 Mar 2022
E(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentials Simon L. Batzner Albert Musaelian Lixin Sun Mario Geiger J. Mailoa M. Kornbluth N. Molinari Tess E. Smidt Boris Kozinsky 188 1,229 0 08 Jan 2021
The Open Catalyst 2020 (OC20) Dataset and Community Challenges L. Chanussot Abhishek Das Siddharth Goyal Thibaut Lavril Muhammed Shuaibi ... Brandon M. Wood Junwoong Yoon Devi Parikh C. L. Zitnick Zachary W. Ulissi 218 498 0 20 Oct 2020
Robust Optimization as Data Augmentation for Large-scale Graphs Kezhi Kong G. Li Mucong Ding Zuxuan Wu Chen Zhu Bernard Ghanem Gavin Taylor Tom Goldstein 87 74 0 19 Oct 2020
Geometric deep learning: going beyond Euclidean data M. Bronstein Joan Bruna Yann LeCun Arthur Szlam P. Vandergheynst GNN 231 3,230 0 24 Nov 2016