{"total":305,"rows":[{"id":"2209.00796","title":"Diffusion Models: A Comprehensive Survey of Methods and Applications","created":"2022-09-02","cited_by_count":1114,"area":"diffusion"},{"id":"2203.02923","title":"GeoDiff: a Geometric Diffusion Model for Molecular Conformation Generation","created":"2022-03-06","cited_by_count":516,"area":"diffusion"},{"id":"2106.04399","title":"Flow Network based Generative Models for Non-Iterative Diverse Candidate Generation","created":"2021-06-08","cited_by_count":392,"area":null},{"id":"2202.02514","title":"Score-based Generative Modeling of Graphs via the System of Stochastic Differential Equations","created":"2022-02-05","cited_by_count":283,"area":"diffusion"},{"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":"2102.10757","title":"Self-Supervised Learning of Graph Neural Networks: A Unified Review","created":"2021-02-22","cited_by_count":184,"area":null},{"id":"2102.01189","title":"GraphDF: A Discrete Flow Model for Molecular Graph Generation","created":"2021-02-01","cited_by_count":180,"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":"2105.03902","title":"Learning Gradient Fields for Molecular Conformation Generation","created":"2021-05-09","cited_by_count":163,"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":"2209.05481","title":"A Molecular Multimodal Foundation Model Associating Molecule Graphs with Natural Language","created":"2022-09-12","cited_by_count":131,"area":null},{"id":"2103.10432","title":"MARS: Markov Molecular Sampling for Multi-objective Drug Discovery","created":"2021-03-18","cited_by_count":125,"area":null},{"id":"2204.09410","title":"Generating 3D Molecules for Target Protein Binding","created":"2022-04-19","cited_by_count":104,"area":null},{"id":"2211.15779","title":"Revisiting Over-smoothing and Over-squashing Using Ollivier-Ricci Curvature","created":"2022-11-28","cited_by_count":98,"area":null},{"id":"2206.07632","title":"Exploring Chemical Space with Score-based Out-of-distribution Generation","created":"2022-06-06","cited_by_count":95,"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":"2102.00546","title":"GraphEBM: Molecular Graph Generation with Energy-Based Models","created":"2021-01-31","cited_by_count":85,"area":null},{"id":"2307.08849","title":"Autoregressive Diffusion Model for Graph Generation","created":"2023-07-17","cited_by_count":83,"area":"diffusion"},{"id":"2406.04843","title":"Variational Flow Matching for Graph Generation","created":"2024-06-07","cited_by_count":83,"area":"diffusion"},{"id":"2103.12608","title":"DIG: A Turnkey Library for Diving into Graph Deep Learning Research","created":"2021-03-23","cited_by_count":76,"area":null},{"id":"2203.06714","title":"A Survey on Deep Graph Generation: Methods and Applications","created":"2022-03-13","cited_by_count":69,"area":null},{"id":"2206.13602","title":"Molecular Geometry Pretraining with SE(3)-Invariant Denoising Distance Matching","created":"2022-06-27","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":"2406.16976","title":"Efficient Evolutionary Search Over Chemical Space with Large Language Models","created":"2024-06-23","cited_by_count":61,"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":"2104.07644","title":"ExplaGraphs: An Explanation Graph Generation Task for Structured Commonsense Reasoning","created":"2021-04-15","cited_by_count":58,"area":null},{"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":"2302.03596","title":"Graph Generation with Diffusion Mixture","created":"2023-02-07","cited_by_count":55,"area":null},{"id":"2106.15098","title":"Molecule Generation by Principal Subgraph Mining and Assembling","created":"2021-06-29","cited_by_count":54,"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":"2109.10469","title":"Differentiable Scaffolding Tree for Molecular Optimization","created":"2021-09-22","cited_by_count":53,"area":null},{"id":"2107.00096","title":"Improving black-box optimization in VAE latent space using decoder uncertainty","created":"2021-06-30","cited_by_count":51,"area":null},{"id":"2207.04869","title":"Graph-based Molecular Representation Learning","created":"2022-07-08","cited_by_count":51,"area":null},{"id":"2202.01338","title":"Regression Transformer: Concurrent sequence regression and generation for molecular language modeling","created":"2022-02-01","cited_by_count":50,"area":null},{"id":"2410.03601","title":"How Discrete and Continuous Diffusion Meet: Comprehensive Analysis of Discrete Diffusion Models via a Stochastic Integral Framework","created":"2024-10-04","cited_by_count":48,"area":"diffusion"},{"id":"2502.00234","title":"Fast Solvers for Discrete Diffusion Models: Theory and Applications of High-Order Algorithms","created":"2025-02-01","cited_by_count":47,"area":"diffusion"},{"id":"2211.16508","title":"Reinforced Genetic Algorithm for Structure-based Drug Design","created":"2022-11-28","cited_by_count":46,"area":null},{"id":"2301.00427","title":"Conditional Diffusion Based on Discrete Graph Structures for Molecular Graph Generation","created":"2023-01-01","cited_by_count":46,"area":null},{"id":"2302.02615","title":"Rethinking Out-of-distribution (OOD) Detection: Masked Image Modeling is All You Need","created":"2023-02-06","cited_by_count":46,"area":null},{"id":"2111.01186","title":"Combining Latent Space and Structured Kernels for Bayesian Optimization over Combinatorial Spaces","created":"2021-11-01","cited_by_count":45,"area":null},{"id":"2202.08320","title":"TorchDrug: A Powerful and Flexible Machine Learning Platform for Drug Discovery","created":"2022-02-16","cited_by_count":45,"area":null},{"id":"2212.01842","title":"GraphGDP: Generative Diffusion Processes for Permutation Invariant Graph Generation","created":"2022-12-04","cited_by_count":43,"area":null},{"id":"2104.06344","title":"The Future is not One-dimensional: Complex Event Schema Induction by Graph Modeling for Event Prediction","created":"2021-04-13","cited_by_count":40,"area":null},{"id":"2206.09010","title":"LIMO: Latent Inceptionism for Targeted Molecule Generation","created":"2022-06-17","cited_by_count":40,"area":null},{"id":"2306.10045","title":"Efficient Approximations of Complete Interatomic Potentials for Crystal Property Prediction","created":"2023-06-12","cited_by_count":37,"area":null},{"id":"2211.08892","title":"Fast Graph Generation via Spectral Diffusion","created":"2022-11-16","cited_by_count":36,"area":"diffusion"},{"id":"2304.01565","title":"A Survey on Graph Diffusion Models: Generative AI in Science for Molecule, Protein and Material","created":"2023-04-04","cited_by_count":35,"area":"diffusion"},{"id":"2106.06189","title":"Order Matters: Probabilistic Modeling of Node Sequence for Graph Generation","created":"2021-06-11","cited_by_count":33,"area":null},{"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":"2106.04011","title":"JANUS: Parallel Tempered Genetic Algorithm Guided by Deep Neural Networks for Inverse Molecular Design","created":"2021-06-07","cited_by_count":32,"area":null},{"id":"2406.06449","title":"Cometh: A continuous-time discrete-state graph diffusion model","created":"2024-06-10","cited_by_count":32,"area":"diffusion"},{"id":"2310.04925","title":"Crystal-GFN: sampling crystals with desirable properties and constraints","created":"2023-10-07","cited_by_count":31,"area":null},{"id":"2310.09267","title":"Genetic algorithms are strong baselines for molecule generation","created":"2023-10-13","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":"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":"2106.05386","title":"Artificial Intelligence in Drug Discovery: Applications and Techniques","created":"2021-06-09","cited_by_count":28,"area":null},{"id":"2201.01702","title":"Bringing Your Own View: Graph Contrastive Learning without Prefabricated Data Augmentations","created":"2022-01-04","cited_by_count":28,"area":null},{"id":"2307.01646","title":"SwinGNN: Rethinking Permutation Invariance in Diffusion Models for Graph Generation","created":"2023-07-04","cited_by_count":28,"area":"diffusion"},{"id":"2112.12542","title":"How Much Space Has Been Explored? Measuring the Chemical Space Covered by Databases and Machine-Generated Molecules","created":"2021-12-22","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":"2405.11868","title":"Towards Graph Contrastive Learning: A Survey and Beyond","created":"2024-05-20","cited_by_count":26,"area":null},{"id":"2406.08511","title":"Diffusion Models in $\\textit{De Novo}$ Drug Design","created":"2024-06-07","cited_by_count":26,"area":"diffusion"},{"id":"2402.02518","title":"Unifying Generation and Prediction on Graphs with Latent Graph Diffusion","created":"2024-02-04","cited_by_count":25,"area":"diffusion"},{"id":"2402.03687","title":"Pard: Permutation-Invariant Autoregressive Diffusion for Graph Generation","created":"2024-02-06","cited_by_count":25,"area":"diffusion"},{"id":"2407.01648","title":"Aligning Target-Aware Molecule Diffusion Models with Exact Energy Optimization","created":"2024-07-01","cited_by_count":25,"area":"diffusion"},{"id":"2205.11691","title":"High-Order Pooling for Graph Neural Networks with Tensor Decomposition","created":"2022-05-24","cited_by_count":24,"area":null},{"id":"2106.05856","title":"MolGrow: A Graph Normalizing Flow for Hierarchical Molecular Generation","created":"2021-02-03","cited_by_count":22,"area":null},{"id":"2202.03813","title":"Learning to Predict Graphs with Fused Gromov-Wasserstein Barycenters","created":"2022-02-08","cited_by_count":22,"area":null},{"id":"2301.11259","title":"Domain-Agnostic Molecular Generation with Chemical Feedback","created":"2023-01-26","cited_by_count":22,"area":null},{"id":"2106.07451","title":"Noise-robust Graph Learning by Estimating and Leveraging Pairwise Interactions","created":"2021-06-14","cited_by_count":21,"area":null},{"id":"2112.04624","title":"Deep Molecular Representation Learning via Fusing Physical and Chemical Information","created":"2021-11-28","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":"2508.12629","title":"FlowMol3: Flow Matching for 3D De Novo Small-Molecule Generation","created":"2025-08-18","cited_by_count":21,"area":"diffusion"},{"id":"2106.00967","title":"Multiresolution Equivariant Graph Variational Autoencoder","created":"2021-06-02","cited_by_count":20,"area":null},{"id":"2110.13939","title":"CausalAF: Causal Autoregressive Flow for Safety-Critical Driving Scenario Generation","created":"2021-10-26","cited_by_count":20,"area":null},{"id":"2207.09542","title":"Controllable Data Generation by Deep Learning: A Review","created":"2022-07-19","cited_by_count":20,"area":null},{"id":"2310.00841","title":"Drug Discovery with Dynamic Goal-aware Fragments","created":"2023-10-02","cited_by_count":19,"area":null},{"id":"2202.04829","title":"Target-aware Molecular Graph Generation","created":"2022-02-10","cited_by_count":18,"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":"2403.15441","title":"Unified Generative Modeling of 3D Molecules via Bayesian Flow Networks","created":"2024-03-17","cited_by_count":18,"area":"diffusion"},{"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":"2108.10307","title":"C5T5: Controllable Generation of Organic Molecules with Transformers","created":"2021-08-23","cited_by_count":16,"area":null},{"id":"2207.03928","title":"Accelerating Material Design with the Generative Toolkit for Scientific Discovery","created":"2022-07-08","cited_by_count":16,"area":null},{"id":"2301.10857","title":"Improving Graph Generation by Restricting Graph Bandwidth","created":"2023-01-25","cited_by_count":16,"area":null},{"id":"2312.02230","title":"A Simple and Scalable Representation for Graph Generation","created":"2023-12-04","cited_by_count":16,"area":null},{"id":"2405.17066","title":"Saturn: Sample-efficient Generative Molecular Design using Memory Manipulation","created":"2024-05-27","cited_by_count":15,"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":"2203.00526","title":"Multi-Objective Latent Space Optimization of Generative Molecular Design Models","created":"2022-03-01","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":"2402.16302","title":"Graph Diffusion Policy Optimization","created":"2024-02-26","cited_by_count":13,"area":"diffusion"},{"id":"2406.09357","title":"Advancing Graph Generation through Beta Diffusion","created":"2024-06-13","cited_by_count":13,"area":"diffusion"},{"id":"2502.02216","title":"Flatten Graphs as Sequences: Transformers are Scalable Graph Generators","created":"2025-02-04","cited_by_count":13,"area":null},{"id":"2203.00411","title":"Disentangled Spatiotemporal Graph Generative Models","created":"2022-02-28","cited_by_count":12,"area":null},{"id":"2402.06330","title":"Continual Learning on Graphs: A Survey","created":"2024-02-09","cited_by_count":12,"area":null},{"id":"2110.03372","title":"Unifying Likelihood-free Inference with Black-box Optimization and Beyond","created":"2021-10-06","cited_by_count":11,"area":null},{"id":"2210.06032","title":"Modular Flows: Differential Molecular Generation","created":"2022-10-12","cited_by_count":11,"area":null}],"next_cursor":"100"}