{"total":165,"rows":[{"id":"2203.17003","title":"Equivariant Diffusion for Molecule Generation in 3D","created":"2022-03-31","cited_by_count":677,"area":"diffusion"},{"id":"2303.03543","title":"3D Equivariant Diffusion for Target-Aware Molecule Generation and Affinity Prediction","created":"2023-03-06","cited_by_count":177,"area":null},{"id":"2310.02391","title":"SE(3)-Stochastic Flow Matching for Protein Backbone Generation","created":"2023-10-03","cited_by_count":150,"area":"diffusion"},{"id":"2306.15030","title":"Equivariant flow matching","created":"2023-06-26","cited_by_count":135,"area":"diffusion"},{"id":"2210.05274","title":"Equivariant 3D-Conditional Diffusion Models for Molecular Linker Design","created":"2022-10-11","cited_by_count":122,"area":"diffusion"},{"id":"2402.06121","title":"Iterated Denoising Energy Matching for Sampling from Boltzmann Densities","created":"2024-02-09","cited_by_count":116,"area":null},{"id":"2209.00865","title":"Diffusion-based Molecule Generation with Informative Prior Bridges","created":"2022-09-02","cited_by_count":106,"area":null},{"id":"2204.01613","title":"SPECTRE: Spectral Conditioning Helps to Overcome the Expressivity Limits of One-shot Graph Generators","created":"2022-04-04","cited_by_count":99,"area":null},{"id":"2302.09048","title":"MiDi: Mixed Graph and 3D Denoising Diffusion for Molecule Generation","created":"2023-02-17","cited_by_count":98,"area":"diffusion"},{"id":"2109.04824","title":"Inverse design of 3d molecular structures with conditional generative neural networks","created":"2021-09-10","cited_by_count":93,"area":null},{"id":"2209.11807","title":"Periodic Graph Transformers for Crystal Material Property Prediction","created":"2022-09-23","cited_by_count":87,"area":null},{"id":"2209.15408","title":"Equivariant Energy-Guided SDE for Inverse Molecular Design","created":"2022-09-30","cited_by_count":73,"area":null},{"id":"2312.07511","title":"A Hitchhiker's Guide to Geometric GNNs for 3D Atomic Systems","created":"2023-12-12","cited_by_count":68,"area":null},{"id":"2105.13926","title":"Geometric Deep Learning and Equivariant Neural Networks","created":"2021-05-28","cited_by_count":65,"area":null},{"id":"2305.07508","title":"MolDiff: Addressing the Atom-Bond Inconsistency Problem in 3D Molecule Diffusion Generation","created":"2023-05-11","cited_by_count":65,"area":null},{"id":"2305.05708","title":"Language models can generate molecules, materials, and protein binding sites directly in three dimensions as XYZ, CIF, and PDB files","created":"2023-05-09","cited_by_count":59,"area":null},{"id":"2209.05710","title":"MDM: Molecular Diffusion Model for 3D Molecule Generation","created":"2022-09-13","cited_by_count":58,"area":"diffusion"},{"id":"2211.11214","title":"DiffBP: Generative Diffusion of 3D Molecules for Target Protein Binding","created":"2022-11-21","cited_by_count":58,"area":null},{"id":"2202.07230","title":"Geometrically Equivariant Graph Neural Networks: A Survey","created":"2022-02-15","cited_by_count":56,"area":null},{"id":"2302.03596","title":"Graph Generation with Diffusion Mixture","created":"2023-02-07","cited_by_count":55,"area":null},{"id":"2404.19739","title":"Mixed Continuous and Categorical Flow Matching for 3D De Novo Molecule Generation","created":"2024-04-30","cited_by_count":54,"area":"diffusion"},{"id":"2409.17808","title":"Generative Modeling of Molecular Dynamics Trajectories","created":"2024-09-26","cited_by_count":51,"area":null},{"id":"2502.03638","title":"SymmCD: Symmetry-Preserving Crystal Generation with Diffusion Models","created":"2025-02-05","cited_by_count":51,"area":null},{"id":"2309.17296","title":"Navigating the Design Space of Equivariant Diffusion-Based Generative Models for De Novo 3D Molecule Generation","created":"2023-09-29","cited_by_count":50,"area":"diffusion"},{"id":"2406.14426","title":"Transferable Boltzmann Generators","created":"2024-06-20","cited_by_count":46,"area":null},{"id":"2304.14621","title":"MUDiff: Unified Diffusion for Complete Molecule Generation","created":"2023-04-28","cited_by_count":42,"area":null},{"id":"2310.02970","title":"Fast, Expressive SE$(n)$ Equivariant Networks through Weight-Sharing in Position-Orientation Space","created":"2023-10-04","cited_by_count":41,"area":null},{"id":"2406.07266","title":"SemlaFlow -- Efficient 3D Molecular Generation with Latent Attention and Equivariant Flow Matching","created":"2024-06-11","cited_by_count":41,"area":null},{"id":"2306.07473","title":"3D molecule generation by denoising voxel grids","created":"2023-06-13","cited_by_count":40,"area":null},{"id":"2502.18462","title":"Scalable Equilibrium Sampling with Sequential Boltzmann Generators","created":"2025-02-25","cited_by_count":38,"area":null},{"id":"2302.04313","title":"Geometry-Complete Diffusion for 3D Molecule Generation and Optimization","created":"2023-02-08","cited_by_count":37,"area":"diffusion"},{"id":"2308.10364","title":"SE(3) Equivariant Augmented Coupling Flows","created":"2023-08-20","cited_by_count":37,"area":null},{"id":"2310.04925","title":"Crystal-GFN: sampling crystals with desirable properties and constraints","created":"2023-10-07","cited_by_count":31,"area":null},{"id":"2306.13769","title":"Functional-Group-Based Diffusion for Pocket-Specific Molecule Generation and Elaboration","created":"2023-05-30","cited_by_count":30,"area":null},{"id":"2206.05075","title":"Diffeomorphic Counterfactuals with Generative Models","created":"2022-06-10","cited_by_count":29,"area":null},{"id":"2402.08703","title":"A Survey of Generative AI for de novo Drug Design: New Frontiers in Molecule and Protein Generation","created":"2024-02-13","cited_by_count":29,"area":null},{"id":"2403.00485","title":"A Survey of Geometric Graph Neural Networks: Data Structures, Models and Applications","created":"2024-03-01","cited_by_count":28,"area":null},{"id":"2301.11355","title":"Rigid Body Flows for Sampling Molecular Crystal Structures","created":"2023-01-26","cited_by_count":27,"area":null},{"id":"2305.12347","title":"Learning Joint 2D & 3D Diffusion Models for Complete Molecule Generation","created":"2023-05-21","cited_by_count":27,"area":"diffusion"},{"id":"2307.02707","title":"Towards Symmetry-Aware Generation of Periodic Materials","created":"2023-07-06","cited_by_count":26,"area":null},{"id":"2310.16624","title":"Free-form Flows: Make Any Architecture a Normalizing Flow","created":"2023-10-25","cited_by_count":26,"area":null},{"id":"2405.06642","title":"PPFlow: Target-aware Peptide Design with Torsional Flow Matching","created":"2024-03-05","cited_by_count":26,"area":null},{"id":"2308.14177","title":"AI-Generated Content (AIGC) for Various Data Modalities: A Survey","created":"2023-08-27","cited_by_count":25,"area":null},{"id":"2211.15420","title":"Equivariant Networks for Crystal Structures","created":"2022-11-15","cited_by_count":24,"area":null},{"id":"2305.13266","title":"Coarse-to-Fine: a Hierarchical Diffusion Model for Molecule Generation in 3D","created":"2023-05-05","cited_by_count":24,"area":"diffusion"},{"id":"2410.10516","title":"UniGEM: A Unified Approach to Generation and Property Prediction for Molecules","created":"2024-10-14","cited_by_count":21,"area":null},{"id":"2508.12629","title":"FlowMol3: Flow Matching for 3D De Novo Small-Molecule Generation","created":"2025-08-18","cited_by_count":21,"area":"diffusion"},{"id":"2210.04893","title":"Equivariant Shape-Conditioned Generation of 3D Molecules for Ligand-Based Drug Design","created":"2022-10-06","cited_by_count":19,"area":null},{"id":"2306.01474","title":"Generalist Equivariant Transformer Towards 3D Molecular Interaction Learning","created":"2023-06-02","cited_by_count":19,"area":null},{"id":"2406.10840","title":"CBGBench: Fill in the Blank of Protein-Molecule Complex Binding Graph","created":"2024-06-16","cited_by_count":19,"area":null},{"id":"2211.10794","title":"NVDiff: Graph Generation through the Diffusion of Node Vectors","created":"2022-11-19","cited_by_count":18,"area":"diffusion"},{"id":"2202.01129","title":"Structure-preserving GANs","created":"2022-02-02","cited_by_count":17,"area":null},{"id":"2310.00049","title":"EPiC-ly Fast Particle Cloud Generation with Flow-Matching and Diffusion","created":"2023-09-29","cited_by_count":17,"area":"diffusion"},{"id":"2212.14852","title":"An Analysis of Attention via the Lens of Exchangeability and Latent Variable Models","created":"2022-12-30","cited_by_count":16,"area":null},{"id":"2410.13027","title":"Geometric Trajectory Diffusion Models","created":"2024-10-16","cited_by_count":15,"area":"diffusion"},{"id":"2508.17815","title":"Multi-domain Distribution Learning for De Novo Drug Design","created":"2025-08-25","cited_by_count":15,"area":"diffusion"},{"id":"2207.05202","title":"The Cosmic Graph: Optimal Information Extraction from Large-Scale Structure using Catalogues","created":"2022-07-11","cited_by_count":14,"area":null},{"id":"2302.01915","title":"Sample Complexity of Probability Divergences under Group Symmetry","created":"2023-02-03","cited_by_count":14,"area":null},{"id":"2202.09212","title":"Molecule Generation for Drug Design: a Graph Learning Perspective","created":"2022-02-18","cited_by_count":13,"area":null},{"id":"2410.03655","title":"Geometric Representation Condition Improves Equivariant Molecule Generation","created":"2024-10-04","cited_by_count":13,"area":null},{"id":"2506.10532","title":"Equivariant Neural Diffusion for Molecule Generation","created":"2025-06-12","cited_by_count":13,"area":"diffusion"},{"id":"2406.01650","title":"TAGMol: Target-Aware Gradient-guided Molecule Generation","created":"2024-06-03","cited_by_count":12,"area":null},{"id":"2203.06832","title":"Semi-Discrete Normalizing Flows through Differentiable Tessellation","created":"2022-03-14","cited_by_count":11,"area":null},{"id":"2301.08893","title":"Spatial Attention Kinetic Networks with E(n)-Equivariance","created":"2023-01-21","cited_by_count":11,"area":null},{"id":"2309.13167","title":"Flow Factorized Representation Learning","created":"2023-09-22","cited_by_count":11,"area":null},{"id":"2405.15252","title":"Accelerating 3D Molecule Generation via Jointly Geometric Optimal Transport","created":"2024-05-24","cited_by_count":11,"area":"diffusion"},{"id":"2509.25486","title":"Scalable Boltzmann Generators for equilibrium sampling of large-scale materials","created":"2025-09-29","cited_by_count":11,"area":null},{"id":"2304.12436","title":"An Equivariant Generative Framework for Molecular Graph-Structure Co-Design","created":"2023-04-12","cited_by_count":10,"area":null},{"id":"2408.10120","title":"Geometry Informed Tokenization of Molecules for Language Model Generation","created":"2024-08-19","cited_by_count":10,"area":null},{"id":"2502.06027","title":"Generating 3D Binding Molecules Using Shape-Conditioned Diffusion Models with Guidance","created":"2025-02-09","cited_by_count":10,"area":"diffusion"},{"id":"2301.10497","title":"E(n)-equivariant Graph Neural Cellular Automata","created":"2023-01-25","cited_by_count":9,"area":null},{"id":"2404.10935","title":"Molecular relaxation by reverse diffusion with time step prediction","created":"2024-04-16","cited_by_count":9,"area":null},{"id":"2406.12816","title":"Neural Approximate Mirror Maps for Constrained Diffusion Models","created":"2024-06-18","cited_by_count":9,"area":"diffusion"},{"id":"2501.02526","title":"Unified Guidance for Geometry-Conditioned Molecular Generation","created":"2025-01-05","cited_by_count":9,"area":"diffusion"},{"id":"2506.20957","title":"Antibody Design and Optimization with Multi-scale Equivariant Graph Diffusion Models for Accurate Complex Antigen Binding","created":"2025-06-26","cited_by_count":9,"area":"diffusion"},{"id":"2304.07132","title":"Towards Controllable Diffusion Models via Reward-Guided Exploration","created":"2023-04-14","cited_by_count":8,"area":"diffusion"},{"id":"2308.11890","title":"Shape-conditioned 3D Molecule Generation via Equivariant Diffusion Models","created":"2023-08-23","cited_by_count":8,"area":null},{"id":"2406.10513","title":"Lift Your Molecules: Molecular Graph Generation in Latent Euclidean Space","created":"2024-06-15","cited_by_count":8,"area":"diffusion"},{"id":"2410.01452","title":"Ensembles provably learn equivariance through data augmentation","created":"2024-10-02","cited_by_count":8,"area":null},{"id":"2503.02918","title":"Straight-Line Diffusion Model for Efficient 3D Molecular Generation","created":"2025-03-04","cited_by_count":8,"area":"diffusion"},{"id":"2503.16278","title":"Unified Cross-Scale 3D Generation and Understanding via Autoregressive Modeling","created":"2025-03-20","cited_by_count":8,"area":null},{"id":"2505.00169","title":"GEOM-Drugs Revisited: Toward More Chemically Accurate Benchmarks for 3D Molecule Generation","created":"2025-04-30","cited_by_count":8,"area":null},{"id":"2505.13791","title":"Scalable Autoregressive 3D Molecule Generation","created":"2025-05-20","cited_by_count":8,"area":"diffusion"},{"id":"2510.10402","title":"Controllable Graph Generation with Diffusion Models via Inference-Time Tree Search Guidance","created":"2025-10-12","cited_by_count":7,"area":"diffusion"},{"id":"2602.15022","title":"Rethinking Diffusion Models with Symmetries through Canonicalization with Applications to Molecular Graph Generation","created":"2026-02-16","cited_by_count":7,"area":"diffusion"},{"id":"2206.09021","title":"Conditional Permutation Invariant Flows","created":"2022-06-17","cited_by_count":6,"area":null},{"id":"2305.14286","title":"Equivariant Neural Simulators for Stochastic Spatiotemporal Dynamics","created":"2023-05-23","cited_by_count":6,"area":null},{"id":"2410.06502","title":"Chemistry-Inspired Diffusion with Non-Differentiable Guidance","created":"2024-10-09","cited_by_count":6,"area":"diffusion"},{"id":"2505.21005","title":"Efficient and Unbiased Sampling from Boltzmann Distributions via Variance-Tuned Diffusion Models","created":"2025-05-27","cited_by_count":6,"area":"diffusion"},{"id":"2506.15378","title":"Sampling 3D Molecular Conformers with Diffusion Transformers","created":"2025-06-18","cited_by_count":6,"area":"diffusion"},{"id":"2509.01486","title":"Prior-Guided Flow Matching for Target-Aware Molecule Design with Learnable Atom Number","created":"2025-09-01","cited_by_count":6,"area":null},{"id":"2509.24779","title":"MarS-FM: Generative Modeling of Molecular Dynamics via Markov State Models","created":"2025-09-29","cited_by_count":6,"area":null},{"id":"2512.16607","title":"Boltzmann generators for amorphous particle systems","created":"2025-12-18","cited_by_count":6,"area":null},{"id":"2402.01195","title":"Conditional Normalizing Flows for Active Learning of Coarse-Grained Molecular Representations","created":"2024-02-02","cited_by_count":5,"area":null},{"id":"2410.03803","title":"Text-guided Diffusion Model for 3D Molecule Generation","created":"2024-10-04","cited_by_count":5,"area":"diffusion"},{"id":"2507.06853","title":"DiffSpectra: Molecular Structure Elucidation from Spectra using Diffusion Models","created":"2025-07-09","cited_by_count":5,"area":"diffusion"},{"id":"2601.22327","title":"Molecular Representations in Implicit Functional Space via Hyper-Networks","created":"2026-01-29","cited_by_count":5,"area":null},{"id":"2305.13517","title":"Statistical Guarantees of Group-Invariant GANs","created":"2023-05-22","cited_by_count":4,"area":null},{"id":"2409.01931","title":"On the design space between molecular mechanics and machine learning force fields","created":"2024-09-03","cited_by_count":4,"area":null},{"id":"2412.01491","title":"Understanding complex crowd dynamics with generative neural simulators","created":"2024-12-02","cited_by_count":4,"area":null}],"next_cursor":"100"}