{"list":"other","total":84,"rows":[{"id":"1512.03385","title":"Deep Residual Learning for Image Recognition","created":"2015-12-10","cited_by_count":52096},{"id":"1810.04805","title":"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding","created":"2018-10-11","cited_by_count":40399},{"id":"2006.11239","title":"Denoising Diffusion Probabilistic Models","created":"2020-06-19","cited_by_count":21076},{"id":"1503.03585","title":"Deep Unsupervised Learning using Nonequilibrium Thermodynamics","created":"2015-03-12","cited_by_count":7058},{"id":"2210.02747","title":"Flow Matching for Generative Modeling","created":"2022-10-06","cited_by_count":5702},{"id":"1505.05770","title":"Variational Inference with Normalizing Flows","created":"2015-05-21","cited_by_count":2170},{"id":"1605.08803","title":"Density estimation using Real NVP","created":"2016-05-27","cited_by_count":2042},{"id":"2004.07780","title":"Shortcut Learning in Deep Neural Networks","created":"2020-04-16","cited_by_count":1969},{"id":"1410.8516","title":"NICE: Non-linear Independent Components Estimation","created":"2014-10-30","cited_by_count":1058},{"id":"1611.08097","title":"Geometric deep learning: going beyond Euclidean data","created":"2016-11-24","cited_by_count":1048},{"id":"2209.15571","title":"Building Normalizing Flows with Stochastic Interpolants","created":"2022-09-30","cited_by_count":995},{"id":"2102.09844","title":"E(n) Equivariant Graph Neural Networks","created":"2021-02-19","cited_by_count":965},{"id":"2303.08797","title":"Stochastic Interpolants: A Unifying Framework for Flows and Diffusions","created":"2023-03-15","cited_by_count":939},{"id":"1610.02415","title":"Automatic chemical design using a data-driven continuous representation of molecules","created":"2016-10-07","cited_by_count":765},{"id":"1802.08219","title":"Tensor field networks: Rotation- and translation-equivariant neural networks for 3D point clouds","created":"2018-02-22","cited_by_count":746},{"id":"1706.08566","title":"SchNet: A continuous-filter convolutional neural network for modeling quantum interactions","created":"2017-06-26","cited_by_count":571},{"id":"2003.03123","title":"Directional Message Passing for Molecular Graphs","created":"2020-03-06","cited_by_count":561},{"id":"2106.03893","title":"Rethinking Graph Transformers with Spectral Attention","created":"2021-06-07","cited_by_count":473},{"id":"2302.09419","title":"A Comprehensive Survey on Pretrained Foundation Models: A History from BERT to ChatGPT","created":"2023-02-18","cited_by_count":368},{"id":"2009.01411","title":"Learning from Protein Structure with Geometric Vector Perceptrons","created":"2020-09-03","cited_by_count":313},{"id":"2210.01776","title":"DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking","created":"2022-10-04","cited_by_count":313},{"id":"2104.12229","title":"Vector Neurons: A General Framework for SO(3)-Equivariant Networks","created":"2021-04-25","cited_by_count":305},{"id":"2110.06197","title":"Crystal Diffusion Variational Autoencoder for Periodic Material Generation","created":"2021-10-12","cited_by_count":270},{"id":"2206.11990","title":"Equiformer: Equivariant Graph Attention Transformer for 3D Atomistic Graphs","created":"2022-06-23","cited_by_count":264},{"id":"2011.14115","title":"Fast and Uncertainty-Aware Directional Message Passing for Non-Equilibrium Molecules","created":"2020-11-28","cited_by_count":255},{"id":"2006.05531","title":"GEOM: Energy-annotated molecular conformations for property prediction and molecular generation","created":"2020-06-09","cited_by_count":243},{"id":"2210.13695","title":"Structure-based Drug Design with Equivariant Diffusion Models","created":"2022-10-24","cited_by_count":217},{"id":"2205.07249","title":"Pocket2Mol: Efficient Molecular Sampling Based on 3D Protein Pockets","created":"2022-05-15","cited_by_count":183},{"id":"2303.03543","title":"3D Equivariant Diffusion for Target-Aware Molecule Generation and Affinity Prediction","created":"2023-03-06","cited_by_count":177},{"id":"2203.10446","title":"A 3D Generative Model for Structure-Based Drug Design","created":"2022-03-20","cited_by_count":149},{"id":"2202.05146","title":"EquiBind: Geometric Deep Learning for Drug Binding Structure Prediction","created":"2022-02-07","cited_by_count":144},{"id":"2004.03497","title":"ProGen: Language Modeling for Protein Generation","created":"2020-03-08","cited_by_count":128},{"id":"2210.05274","title":"Equivariant 3D-Conditional Diffusion Models for Molecular Linker Design","created":"2022-10-11","cited_by_count":122},{"id":"2107.12375","title":"Geometric Deep Learning on Molecular Representations","created":"2021-07-26","cited_by_count":113},{"id":"2204.09410","title":"Generating 3D Molecules for Target Protein Binding","created":"2022-04-19","cited_by_count":104},{"id":"2104.04883","title":"Graph Representation Learning in Biomedicine","created":"2021-04-11","cited_by_count":80},{"id":"2301.12040","title":"ProtST: Multi-Modality Learning of Protein Sequences and Biomedical Texts","created":"2023-01-28","cited_by_count":78},{"id":"2210.01765","title":"One Transformer Can Understand Both 2D & 3D Molecular Data","created":"2022-10-04","cited_by_count":77},{"id":"2208.06073","title":"Conditional Antibody Design as 3D Equivariant Graph Translation","created":"2022-08-12","cited_by_count":75},{"id":"2012.04035","title":"ATOM3D: Tasks On Molecules in Three Dimensions","created":"2020-12-07","cited_by_count":74},{"id":"2110.15200","title":"Generating 3D Molecules Conditional on Receptor Binding Sites with Deep Generative Models","created":"2021-10-28","cited_by_count":73},{"id":"2210.03930","title":"Hierarchical Graph Transformer with Adaptive Node Sampling","created":"2022-10-08","cited_by_count":72},{"id":"2206.08515","title":"ComENet: Towards Complete and Efficient Message Passing for 3D Molecular Graphs","created":"2022-06-17","cited_by_count":69},{"id":"2203.06442","title":"Equivariant Graph Mechanics Networks with Constraints","created":"2022-03-12","cited_by_count":68},{"id":"2203.14500","title":"MolGenSurvey: A Systematic Survey in Machine Learning Models for Molecule Design","created":"2022-03-28","cited_by_count":66},{"id":"2107.10670","title":"Structure-aware Interactive Graph Neural Networks for the Prediction of Protein-Ligand Binding Affinity","created":"2021-07-21","cited_by_count":60},{"id":"2211.11214","title":"DiffBP: Generative Diffusion of 3D Molecules for Target Protein Binding","created":"2022-11-21","cited_by_count":58},{"id":"1612.02751","title":"Protein-Ligand Scoring with Convolutional Neural Networks","created":"2016-12-08","cited_by_count":57},{"id":"2202.07230","title":"Geometrically Equivariant Graph Neural Networks: A Survey","created":"2022-02-15","cited_by_count":56},{"id":"2209.03299","title":"Multimodal learning with graphs","created":"2022-09-07","cited_by_count":54},{"id":"2106.03843","title":"Equivariant Graph Neural Networks for 3D Macromolecular Structure","created":"2021-06-07","cited_by_count":53},{"id":"2204.02337","title":"Multi-Scale Representation Learning on Proteins","created":"2022-04-04","cited_by_count":52},{"id":"1712.07042","title":"Development and evaluation of a deep learning model for protein-ligand binding affinity prediction","created":"2017-12-19","cited_by_count":51},{"id":"2102.04252","title":"HINT: Hierarchical Interaction Network for Trial Outcome Prediction Leveraging Web Data","created":"2021-02-08","cited_by_count":49},{"id":"2211.16508","title":"Reinforced Genetic Algorithm for Structure-based Drug Design","created":"2022-11-28","cited_by_count":46},{"id":"2210.11250","title":"Structure-based drug design with geometric deep learning","created":"2022-10-19","cited_by_count":40},{"id":"2104.08474","title":"Learning to design drug-like molecules in three-dimensional space using deep generative models","created":"2021-04-17","cited_by_count":37},{"id":"2005.07704","title":"Improved Protein-ligand Binding Affinity Prediction with Structure-Based Deep Fusion Inference","created":"2020-05-17","cited_by_count":35},{"id":"2209.15171","title":"State-specific protein-ligand complex structure prediction with a multi-scale deep generative model","created":"2022-09-30","cited_by_count":35},{"id":"2210.06069","title":"E3Bind: An End-to-End Equivariant Network for Protein-Ligand Docking","created":"2022-10-12","cited_by_count":35},{"id":"2010.14442","title":"Generating 3D Molecular Structures Conditional on a Receptor Binding Site with Deep Generative Models","created":"2020-10-16","cited_by_count":33},{"id":"2205.07309","title":"3DLinker: An E(3) Equivariant Variational Autoencoder for Molecular Linker Design","created":"2022-05-15","cited_by_count":33},{"id":"2008.12249","title":"PIGNet: A physics-informed deep learning model toward generalized drug-target interaction predictions","created":"2020-08-22","cited_by_count":31},{"id":"2110.04383","title":"Learning 3D Representations of Molecular Chirality with Invariance to Bond Rotations","created":"2021-10-08","cited_by_count":29},{"id":"2305.13997","title":"Learning Subpocket Prototypes for Generalizable Structure-based Drug Design","created":"2023-05-22","cited_by_count":25},{"id":"2209.13865","title":"Zero-Shot 3D Drug Design by Sketching and Generating","created":"2022-09-28","cited_by_count":24},{"id":"2210.04893","title":"Equivariant Shape-Conditioned Generation of 3D Molecules for Ligand-Based Drug Design","created":"2022-10-06","cited_by_count":19},{"id":"2306.01474","title":"Generalist Equivariant Transformer Towards 3D Molecular Interaction Learning","created":"2023-06-02","cited_by_count":19},{"id":"2002.05643","title":"DeepSurf: A surface-based deep learning approach for the prediction of ligand binding sites on proteins","created":"2020-02-13","cited_by_count":17},{"id":"1904.06517","title":"Improving detection of protein-ligand binding sites with 3D segmentation","created":"2019-04-13","cited_by_count":15},{"id":"2302.12177","title":"EquiPocket: an E(3)-Equivariant Geometric Graph Neural Network for Ligand Binding Site Prediction","created":"2023-02-23","cited_by_count":15},{"id":"1912.00318","title":"DeepAtom: A Framework for Protein-Ligand Binding Affinity Prediction","created":"2019-12-01","cited_by_count":14},{"id":"2103.10334","title":"Structure Inducing Pre-Training","created":"2021-03-18","cited_by_count":10},{"id":"2304.12436","title":"An Equivariant Generative Framework for Molecular Graph-Structure Co-Design","created":"2023-04-12","cited_by_count":10},{"id":"2302.07120","title":"PrefixMol: Target- and Chemistry-aware Molecule Design via Prefix Embedding","created":"2023-02-14","cited_by_count":7},{"id":"2306.04886","title":"Multi-task Bioassay Pre-training for Protein-ligand Binding Affinity Prediction","created":"2023-06-08","cited_by_count":7},{"id":"2206.02789","title":"Efficient and Accurate Physics-aware Multiplex Graph Neural Networks for 3D Small Molecules and Macromolecule Complexes","created":"2022-06-06","cited_by_count":6},{"id":"2209.13014","title":"Predicting Protein-Ligand Binding Affinity via Joint Global-Local Interaction Modeling","created":"2022-09-18","cited_by_count":5},{"id":"2303.09916","title":"DSDP: A Blind Docking Strategy Accelerated by GPUs","created":"2023-03-16","cited_by_count":4},{"id":"2304.12825","title":"GraphVF: Controllable Protein-Specific 3D Molecule Generation with Variational Flow","created":"2023-02-23","cited_by_count":4},{"id":"2108.09926","title":"APObind: A Dataset of Ligand Unbound Protein Conformations for Machine Learning Applications in De Novo Drug Design","created":"2021-08-23","cited_by_count":3},{"id":"2212.13295","title":"Structure-based drug discovery with deep learning","created":"2022-12-26","cited_by_count":3},{"id":"2302.05847","title":"3D Molecular Generation via Virtual Dynamics","created":"2023-02-12","cited_by_count":3},{"id":"2301.00984","title":"Protein-Ligand Complex Generator & Drug Screening via Tiered Tensor Transform","created":"2023-01-03","cited_by_count":1}],"next_cursor":null}