mlsanityData to 7 Oct 2026

Objective-Reinforced Generative Adversarial Networks (ORGAN) for Sequence Generation Models

arXiv 1705.10843 ↗First posted 30 May 2017stat.ML cs.LGcited by 116

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Abstract (arXiv)

In unsupervised data generation tasks, besides the generation of a sample based on previous observations, one would often like to give hints to the model in order to bias the generation towards desirable metrics. We propose a method that combines Generative Adversarial Networks (GANs) and reinforcement learning (RL) in order to accomplish exactly that. While RL biases the data generation process towards arbitrary metrics, the GAN component of the reward function ensures that the model still remembers information learned from data. We build upon previous results that incorporated GANs and RL in order to generate sequence data and test this model in several settings for the generation of molecules encoded as text sequences (SMILES) and in the context of music generation, showing for each case that we can effectively bias the generation process towards desired metrics.

Key facts. Cited by 116 papers in the map (data through 7 Oct 2026). First posted on arXiv 30 May 2017.

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Citing papers per year: 2020: 1; 2021: 23; 2022: 27; 2023: 23; 2024: 23; 2025: 12; 2026: 7.

116 papers.

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  1. 2022Exploring Chemical Space with Score-based Out-of-distribution Generationcited by 95
  2. 2023Return of Unconditional Generation: A Self-supervised Representation Generation Methodcited by 95
  3. 2022MolGenSurvey: A Systematic Survey in Machine Learning Models for Molecule Designcited by 66
  4. 2023On the Constrained Time-Series Generation Problemcited by 52
  5. 2022Reinforced Genetic Algorithm for Structure-based Drug Designcited by 46
  6. 2022LIMO: Latent Inceptionism for Targeted Molecule Generationcited by 40
  7. 20233D molecule generation by denoising voxel gridscited by 38
  8. 2021Quantum Generative Models for Small Molecule Drug Discoverycited by 37
  9. 2024MusicRL: Aligning Music Generation to Human Preferencescited by 32
  10. 2021JANUS: Parallel Tempered Genetic Algorithm Guided by Deep Neural Networks for Inverse Molecular Designcited by 31
  11. 2021PcDGAN: A Continuous Conditional Diverse Generative Adversarial Network For Inverse Designcited by 30
  12. 2021Artificial Intelligence in Drug Discovery: Applications and Techniquescited by 28
38 more
  1. 2022Data-Efficient Graph Grammar Learning for Molecular Generationcited by 26
  2. 2024SynFlowNet: Design of Diverse and Novel Molecules with Synthesis Constraintscited by 26
  3. 2024Large Language Models in Drug Discovery and Development: From Disease Mechanisms to Clinical Trialscited by 24
  4. 2021Creativity and Machine Learning: A Surveycited by 21
  5. 2022Graph Neural Networks for Moleculescited by 21
  6. 2024Evaluating Creative Short Story Generation in Humans and Large Language Modelscited by 21
  7. 2023De novo Drug Design using Reinforcement Learning with Multiple GPT Agentscited by 19
  8. 2022Target-aware Molecular Graph Generationcited by 18
  9. 2021A Survey on Deep Reinforcement Learning for Audio-Based Applicationscited by 17
  10. 2022MGCVAE: Multi-objective Inverse Design via Molecular Graph Conditional Variational Autoencodercited by 16
  11. 2022A survey on text generation using generative adversarial networkscited by 16
  12. 2024Graph Reinforcement Learning for Combinatorial Optimization: A Survey and Unifying Perspectivecited by 16
  13. 2021C5T5: Controllable Generation of Organic Molecules with Transformerscited by 15
  14. 2022Interpretable Molecular Graph Generation via Monotonic Constraintscited by 15
  15. 2022Accelerating Material Design with the Generative Toolkit for Scientific Discoverycited by 15
  16. 2021Assigning Confidence to Molecular Property Predictioncited by 14
  17. 2024Saturn: Sample-efficient Generative Molecular Design using Memory Manipulationcited by 14
  18. 2023t-SMILES: A Scalable Fragment-based Molecular Representation Framework for De Novo Molecule Generationcited by 13
  19. 2022Scalable Fragment-Based 3D Molecular Design with Reinforcement Learningcited by 11
  20. 2023Reinforcement Learning for Generative AI: A Surveycited by 11
  21. 2021Hybrid quantum-classical machine learning for generative chemistry and drug designcited by 10
  22. 2021Unifying Likelihood-free Inference with Black-box Optimization and Beyondcited by 10
  23. 2023Reinforcement Learning for Generative AI: State of the Art, Opportunities and Open Research Challengescited by 10
  24. 2024Generative Adversarial Model-Based Optimization via Source Critic Regularizationcited by 10
  25. 2024Diversity-Rewarded CFG Distillationcited by 9
  26. 2022Conditional β-VAE for De Novo Molecular Generationcited by 8
  27. 2022Probabilistic Generative Transformer Language models for Generative Design of Moleculescited by 8
  28. 2022Probabilistic Transformer: Modelling Ambiguities and Distributions for RNA Folding and Molecule Designcited by 7
  29. 2023Augmented Memory: Capitalizing on Experience Replay to Accelerate De Novo Molecular Designcited by 7
  30. 2024Creativity in AI: Progresses and Challengescited by 7
  31. 2021Game of GANs: Game-Theoretical Models for Generative Adversarial Networkscited by 6
  32. 2021Fragment-based Sequential Translation for Molecular Optimizationcited by 6
  33. 2022Graph Machine Learning for Design of High-Octane Fuelscited by 6
  34. 2024Entropy-Reinforced Planning with Large Language Models for Drug Discoverycited by 6
  35. 2021Spatial Graph Attention and Curiosity-driven Policy for Antiviral Drug Discoverycited by 5
  36. 2023Target Specific De Novo Design of Drug Candidate Molecules with Graph Transformer-based Generative Adversarial Networkscited by 5
  37. 2023Application of quantum-inspired generative models to small molecular datasetscited by 5
  38. 2023Beam Enumeration: Probabilistic Explainability For Sample Efficient Self-conditioned Molecular Designcited by 5

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