{"total":172,"rows":[{"id":"2203.17003","title":"Equivariant Diffusion for Molecule Generation in 3D","created":"2022-03-31","cited_by_count":677,"area":"diffusion"},{"id":"2102.03150","title":"Equivariant message passing for the prediction of tensorial properties and molecular spectra","created":"2021-02-05","cited_by_count":441,"area":null},{"id":"2110.06197","title":"Crystal Diffusion Variational Autoencoder for Periodic Material Generation","created":"2021-10-12","cited_by_count":270,"area":null},{"id":"2305.01140","title":"Geometric Latent Diffusion Models for 3D Molecule Generation","created":"2023-05-02","cited_by_count":186,"area":"diffusion"},{"id":"2205.07249","title":"Pocket2Mol: Efficient Molecular Sampling Based on 3D Protein Pockets","created":"2022-05-15","cited_by_count":183,"area":null},{"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":"2105.09016","title":"E(n) Equivariant Normalizing Flows","created":"2021-05-19","cited_by_count":165,"area":null},{"id":"2203.10446","title":"A 3D Generative Model for Structure-Based Drug Design","created":"2022-03-20","cited_by_count":149,"area":null},{"id":"2309.04475","title":"Crystal Structure Prediction by Joint Equivariant Diffusion","created":"2023-07-30","cited_by_count":148,"area":"diffusion"},{"id":"2209.00865","title":"Diffusion-based Molecule Generation with Informative Prior Bridges","created":"2022-09-02","cited_by_count":106,"area":null},{"id":"2204.09410","title":"Generating 3D Molecules for Target Protein Binding","created":"2022-04-19","cited_by_count":104,"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":"2312.03687","title":"MatterGen: a generative model for inorganic materials design","created":"2023-12-06","cited_by_count":98,"area":null},{"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":"2211.08064","title":"Physics-Informed Machine Learning: A Survey on Problems, Methods and Applications","created":"2022-11-15","cited_by_count":92,"area":null},{"id":"2102.10240","title":"Learning Neural Generative Dynamics for Molecular Conformation Generation","created":"2021-02-20","cited_by_count":88,"area":null},{"id":"2110.04624","title":"Iterative Refinement Graph Neural Network for Antibody Sequence-Structure Co-design","created":"2021-10-09","cited_by_count":86,"area":null},{"id":"2208.06073","title":"Conditional Antibody Design as 3D Equivariant Graph Translation","created":"2022-08-12","cited_by_count":75,"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":"2402.03992","title":"Space Group Constrained Crystal Generation","created":"2024-02-06","cited_by_count":73,"area":null},{"id":"2304.05055","title":"A Comprehensive Survey on Deep Graph Representation Learning","created":"2023-04-11","cited_by_count":72,"area":null},{"id":"2112.03041","title":"Keeping it Simple: Language Models can learn Complex Molecular Distributions","created":"2021-12-06","cited_by_count":68,"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":"2203.14500","title":"MolGenSurvey: A Systematic Survey in Machine Learning Models for Molecule Design","created":"2022-03-28","cited_by_count":66,"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":"2312.07168","title":"Equivariant Flow Matching with Hybrid Probability Transport","created":"2023-12-12","cited_by_count":60,"area":"diffusion"},{"id":"2105.07246","title":"An End-to-End Framework for Molecular Conformation Generation via Bilevel Programming","created":"2021-05-15","cited_by_count":59,"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":"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":"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":"2106.11535","title":"Particle Cloud Generation with Message Passing Generative Adversarial Networks","created":"2021-06-22","cited_by_count":46,"area":null},{"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":"2403.15734","title":"Space Group Informed Transformer for Crystalline Materials Generation","created":"2024-03-23","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":"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,"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":"2205.07309","title":"3DLinker: An E(3) Equivariant Variational Autoencoder for Molecular Linker Design","created":"2022-05-15","cited_by_count":33,"area":null},{"id":"2307.10683","title":"Fractional Denoising for 3D Molecular Pre-training","created":"2023-07-20","cited_by_count":33,"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":"2202.01356","title":"Direct Molecular Conformation Generation","created":"2022-02-03","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":"2212.05517","title":"SchNetPack 2.0: A neural network toolbox for atomistic machine learning","created":"2022-12-11","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":"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":"2305.13997","title":"Learning Subpocket Prototypes for Generalizable Structure-based Drug Design","created":"2023-05-22","cited_by_count":25,"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":"2201.12176","title":"Generative Coarse-Graining of Molecular Conformations","created":"2022-01-28","cited_by_count":21,"area":null},{"id":"2209.05582","title":"Graph Neural Networks for Molecules","created":"2022-09-12","cited_by_count":21,"area":null},{"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":"2207.09542","title":"Controllable Data Generation by Deep Learning: A Review","created":"2022-07-19","cited_by_count":20,"area":null},{"id":"2407.09468","title":"Beyond Euclid: An Illustrated Guide to Modern Machine Learning with Geometric, Topological, and Algebraic Structures","created":"2024-07-12","cited_by_count":20,"area":null},{"id":"2401.06155","title":"De novo Drug Design using Reinforcement Learning with Multiple GPT Agents","created":"2023-12-21","cited_by_count":19,"area":null},{"id":"2405.03961","title":"Structure-based drug design by denoising voxel grids","created":"2024-05-07","cited_by_count":19,"area":"diffusion"},{"id":"2403.15441","title":"Unified Generative Modeling of 3D Molecules via Bayesian Flow Networks","created":"2024-03-17","cited_by_count":18,"area":"diffusion"},{"id":"2311.16199","title":"Symphony: Symmetry-Equivariant Point-Centered Spherical Harmonics for 3D Molecule Generation","created":"2023-11-27","cited_by_count":17,"area":null},{"id":"2502.12638","title":"NExT-Mol: 3D Diffusion Meets 1D Language Modeling for 3D Molecule Generation","created":"2025-02-18","cited_by_count":17,"area":"diffusion"},{"id":"2502.02016","title":"A Periodic Bayesian Flow for Material Generation","created":"2025-02-04","cited_by_count":16,"area":null},{"id":"2508.17815","title":"Multi-domain Distribution Learning for De Novo Drug Design","created":"2025-08-25","cited_by_count":15,"area":"diffusion"},{"id":"2305.18046","title":"Implicit Transfer Operator Learning: Multiple Time-Resolution Surrogates for Molecular Dynamics","created":"2023-05-29","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":"2305.10133","title":"Generation of 3D Molecules in Pockets via Language Model","created":"2023-05-17","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":"2510.22503","title":"LLEMA: Evolutionary Search with LLMs for Multi-Objective Materials Discovery","created":"2025-10-26","cited_by_count":13,"area":null},{"id":"2401.02683","title":"Geometric-Facilitated Denoising Diffusion Model for 3D Molecule Generation","created":"2024-01-05","cited_by_count":12,"area":"diffusion"},{"id":"2405.15252","title":"Accelerating 3D Molecule Generation via Jointly Geometric Optimal Transport","created":"2024-05-24","cited_by_count":11,"area":"diffusion"},{"id":"2301.09642","title":"DiffSDS: A language diffusion model for protein backbone inpainting under geometric conditions and constraints","created":"2023-01-22","cited_by_count":10,"area":"diffusion"},{"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":"2505.16379","title":"Materials Generation in the Era of Artificial Intelligence: A Comprehensive Survey","created":"2025-05-22","cited_by_count":10,"area":null},{"id":"2107.05007","title":"Generating stable molecules using imitation and reinforcement learning","created":"2021-07-11","cited_by_count":9,"area":"offlinerl"},{"id":"2205.08306","title":"Accurate Machine Learned Quantum-Mechanical Force Fields for Biomolecular Simulations","created":"2022-05-17","cited_by_count":9,"area":null},{"id":"2401.05402","title":"Vector Field Oriented Diffusion Model for Crystal Material Generation","created":"2023-12-20","cited_by_count":9,"area":"diffusion"},{"id":"2404.10935","title":"Molecular relaxation by reverse diffusion with time step prediction","created":"2024-04-16","cited_by_count":9,"area":null},{"id":"2501.02526","title":"Unified Guidance for Geometry-Conditioned Molecular Generation","created":"2025-01-05","cited_by_count":9,"area":"diffusion"},{"id":"2506.18340","title":"Controlled Generation with Equivariant Variational Flow Matching","created":"2025-06-23","cited_by_count":9,"area":"diffusion"},{"id":"2512.07289","title":"Equivariant Diffusion for Crystal Structure Prediction","created":"2025-12-08","cited_by_count":9,"area":"diffusion"},{"id":"2305.09480","title":"Cross-Gate MLP with Protein Complex Invariant Embedding is A One-Shot Antibody Designer","created":"2023-04-21","cited_by_count":8,"area":null},{"id":"2307.05378","title":"M$^2$Hub: Unlocking the Potential of Machine Learning for Materials Discovery","created":"2023-06-14","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":"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":"2505.13791","title":"Scalable Autoregressive 3D Molecule Generation","created":"2025-05-20","cited_by_count":8,"area":"diffusion"},{"id":"2302.07120","title":"PrefixMol: Target- and Chemistry-aware Molecule Design via Prefix Embedding","created":"2023-02-14","cited_by_count":7,"area":null},{"id":"2506.20743","title":"A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools","created":"2025-06-25","cited_by_count":7,"area":"agents"},{"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":"2106.04464","title":"Augmenting Molecular Deep Generative Models with Topological Data Analysis Representations","created":"2021-06-08","cited_by_count":6,"area":null},{"id":"2402.15864","title":"E(3)-equivariant models cannot learn chirality: Field-based molecular generation","created":"2024-02-24","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":"2503.08918","title":"Multilevel Generative Samplers for Investigating Critical Phenomena","created":"2025-03-11","cited_by_count":6,"area":null},{"id":"2504.08051","title":"Compositional Flows for 3D Molecule and Synthesis Pathway Co-design","created":"2025-04-10","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"}],"next_cursor":"100"}