{"total":71,"rows":[{"id":"2209.14734","title":"DiGress: Discrete Denoising diffusion for graph generation","created":"2022-09-29","cited_by_count":520,"area":"diffusion"},{"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":"2110.01219","title":"Hit and Lead Discovery with Explorative RL and Fragment-based Molecule Generation","created":"2021-10-04","cited_by_count":68,"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":"2501.06158","title":"GenMol: A Drug Discovery Generalist with Discrete Diffusion","created":"2025-01-10","cited_by_count":50,"area":"diffusion"},{"id":"2310.10773","title":"Gotta be SAFE: A New Framework for Molecular Design","created":"2023-10-16","cited_by_count":48,"area":null},{"id":"2302.01129","title":"De Novo Molecular Generation via Connection-aware Motif Mining","created":"2023-02-02","cited_by_count":46,"area":null},{"id":"2208.11126","title":"Retrieval-based Controllable Molecule Generation","created":"2022-08-23","cited_by_count":44,"area":null},{"id":"2206.09010","title":"LIMO: Latent Inceptionism for Targeted Molecule Generation","created":"2022-06-17","cited_by_count":40,"area":null},{"id":"2310.19796","title":"Re-evaluating Retrosynthesis Algorithms with Syntheseus","created":"2023-10-30","cited_by_count":27,"area":null},{"id":"2311.02142","title":"Sparse Training of Discrete Diffusion Models for Graph Generation","created":"2023-11-03","cited_by_count":27,"area":"diffusion"},{"id":"2403.13829","title":"DecompOpt: Controllable and Decomposed Diffusion Models for Structure-based Molecular Optimization","created":"2024-03-07","cited_by_count":27,"area":"diffusion"},{"id":"2411.12078","title":"Molecule Generation with Fragment Retrieval Augmentation","created":"2024-11-18","cited_by_count":23,"area":null},{"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":"2310.00841","title":"Drug Discovery with Dynamic Goal-aware Fragments","created":"2023-10-02","cited_by_count":19,"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":"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":"2405.06690","title":"DrugLLM: Open Large Language Model for Few-shot Molecule Generation","created":"2024-05-07","cited_by_count":15,"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":"2301.01829","title":"t-SMILES: A Scalable Fragment-based Molecular Representation Framework for De Novo Molecule Generation","created":"2023-01-04","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":"2211.03808","title":"ToDD: Topological Compound Fingerprinting in Computer-Aided Drug Discovery","created":"2022-11-07","cited_by_count":13,"area":null},{"id":"2502.02216","title":"Flatten Graphs as Sequences: Transformers are Scalable Graph Generators","created":"2025-02-04","cited_by_count":13,"area":null},{"id":"2406.18739","title":"RetroGFN: Diverse and Feasible Retrosynthesis using GFlowNets","created":"2024-06-26","cited_by_count":12,"area":null},{"id":"2303.00313","title":"Deep Learning Methods for Small Molecule Drug Discovery: A Survey","created":"2023-03-01","cited_by_count":10,"area":null},{"id":"2406.08210","title":"Expressivity and Generalization: Fragment-Biases for Molecular GNNs","created":"2024-06-12","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":"2310.03253","title":"Molecule Design by Latent Prompt Transformer","created":"2023-10-05","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":"2305.19303","title":"MAGNet: Motif-Agnostic Generation of Molecules from Shapes","created":"2023-05-30","cited_by_count":7,"area":null},{"id":"2412.05269","title":"Chemist-aligned retrosynthesis by ensembling diverse inductive bias models","created":"2024-12-06","cited_by_count":7,"area":null},{"id":"2502.15805","title":"FragFM: Hierarchical Framework for Efficient Molecule Generation via Fragment-Level Discrete Flow Matching","created":"2025-02-19","cited_by_count":7,"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":"2306.17246","title":"The power of motifs as inductive bias for learning molecular distributions","created":"2023-04-04","cited_by_count":6,"area":null},{"id":"2307.02198","title":"ChiENN: Embracing Molecular Chirality with Graph Neural Networks","created":"2023-07-05","cited_by_count":6,"area":null},{"id":"2309.16235","title":"Language models in molecular discovery","created":"2023-09-28","cited_by_count":6,"area":null},{"id":"2211.04468","title":"An efficient graph generative model for navigating ultra-large combinatorial synthesis libraries","created":"2022-10-19","cited_by_count":5,"area":null},{"id":"2509.26405","title":"Refine Drugs, Don't Complete Them: Uniform-Source Discrete Flows for Fragment-Based Drug Discovery","created":"2025-09-30","cited_by_count":5,"area":null},{"id":"2302.08647","title":"Multiresolution Graph Transformers and Wavelet Positional Encoding for Learning Hierarchical Structures","created":"2023-02-17","cited_by_count":4,"area":null},{"id":"2405.05293","title":"A Review on Fragment-based De Novo 2D Molecule Generation","created":"2024-05-08","cited_by_count":4,"area":null},{"id":"2204.08682","title":"Investigation of a Data Split Strategy Involving the Time Axis in Adverse Event Prediction Using Machine Learning","created":"2022-04-19","cited_by_count":3,"area":null},{"id":"2506.01302","title":"Recent Developments in GNNs for Drug Discovery","created":"2025-06-02","cited_by_count":3,"area":null},{"id":"2601.16955","title":"3D Molecule Generation from Rigid Motifs via SE(3) Flows","created":"2026-01-23","cited_by_count":3,"area":null},{"id":"2310.02066","title":"De Novo Drug Design with Joint Transformers","created":"2023-10-03","cited_by_count":2,"area":null},{"id":"2502.17349","title":"HybridLinker: Topology-Guided Posterior Sampling for Enhanced Diversity and Validity in 3D Molecular Linker Generation","created":"2025-02-24","cited_by_count":2,"area":null},{"id":"2503.12796","title":"A Reinforcement Learning-Driven Transformer GAN for Molecular Generation","created":"2025-03-17","cited_by_count":2,"area":null},{"id":"2604.23307","title":"CombiMOTS: Combinatorial Multi-Objective Tree Search for Dual-Target Molecule Generation","created":"2026-04-25","cited_by_count":2,"area":null},{"id":"2110.05478","title":"An In-depth Summary of Recent Artificial Intelligence Applications in Drug Design","created":"2021-10-10","cited_by_count":1,"area":null},{"id":"2305.03041","title":"Are VAEs Bad at Reconstructing Molecular Graphs?","created":"2023-05-04","cited_by_count":1,"area":null},{"id":"2312.07633","title":"SE(3)-Invariant Multiparameter Persistent Homology for Chiral-Sensitive Molecular Property Prediction","created":"2023-12-12","cited_by_count":1,"area":null},{"id":"2411.06608","title":"MolMiner: Toward Controllable, 3D-Aware, Fragment-Based Molecular Design","created":"2024-11-10","cited_by_count":1,"area":null},{"id":"2602.16954","title":"Neural Proposals, Symbolic Guarantees: Neuro-Symbolic Graph Generative Modeling","created":"2026-02-18","cited_by_count":1,"area":null},{"id":"2210.16307","title":"Investigation of chemical structure recognition by encoder-decoder models in learning progress","created":"2022-10-24","cited_by_count":0,"area":null},{"id":"2308.13066","title":"Objective-Agnostic Enhancement of Molecule Properties via Multi-Stage VAE","created":"2023-08-24","cited_by_count":0,"area":null},{"id":"2309.01788","title":"Hierarchical Grammar-Induced Geometry for Data-Efficient Molecular Property Prediction","created":"2023-09-04","cited_by_count":0,"area":null},{"id":"2401.16771","title":"MolPLA: A Molecular Pretraining Framework for Learning Cores, R-Groups and their Linker Joints","created":"2024-01-30","cited_by_count":0,"area":null},{"id":"2402.17179","title":"Molecule Design by Latent Prompt Transformer","created":"2024-02-27","cited_by_count":0,"area":null},{"id":"2409.08580","title":"Molecular Graph Representation Learning via Structural Similarity Information","created":"2024-09-13","cited_by_count":0,"area":null},{"id":"2503.00426","title":"Auto-encoding Molecules: Graph-Matching Capabilities Matter","created":"2025-03-01","cited_by_count":0,"area":null},{"id":"2503.08160","title":"Concept-Driven Deep Learning for Enhanced Protein-Specific Molecular Generation","created":"2025-03-11","cited_by_count":0,"area":null},{"id":"2510.18122","title":"HyperDiffusionFields (HyDiF): Diffusion-Guided Hypernetworks for Learning Implicit Molecular Neural Fields","created":"2025-10-20","cited_by_count":0,"area":"diffusion"},{"id":"2511.19347","title":"Artificial Intelligence Driven Workflow for Accelerating Design of Novel Photosensitizers","created":"2025-11-24","cited_by_count":0,"area":null},{"id":"2511.20510","title":"FRAGMENTA: End-to-end Fragmentation-based Generative Model with Agentic Tuning for Drug Lead Optimization","created":"2025-11-25","cited_by_count":0,"area":"agents"},{"id":"2602.12162","title":"Amortized Molecular Optimization via Group Relative Policy Optimization","created":"2026-02-12","cited_by_count":0,"area":null},{"id":"2605.14884","title":"AIMing for Standardised Explainability Evaluation in GNNs: A Framework and Case Study on Graph Kernel Networks","created":"2026-05-14","cited_by_count":0,"area":null},{"id":"2607.13155","title":"HEDGEHOG: Hierarchical Evaluation of Drug Generators Through Rigorous Filtration","created":"2026-07-14","cited_by_count":0,"area":null},{"id":"2609.00189","title":"Elite-Weighted Supervised Fine-tuning for Goal-Directed Molecular Optimization","created":"2026-08-31","cited_by_count":0,"area":null},{"id":"2609.13519","title":"Fraglingo: Molecular Design via Attachment-Aware Autoregressive Fragment Generation","created":"2026-09-11","cited_by_count":0,"area":null},{"id":"2610.18438","title":"STAMP as a topology-aware molecular language for fragment-level learning and editing","created":"2026-10-13","cited_by_count":0,"area":null}],"next_cursor":null}