mlsanityData to 14 Oct 2026

Mol-CycleGAN - a generative model for molecular optimization

arXiv 1902.02119 ↗First posted 6 Feb 2019cs.LG physics.chem-ph stat.MLcited by 66

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Abstract (arXiv)

Designing a molecule with desired properties is one of the biggest challenges in drug development, as it requires optimization of chemical compound structures with respect to many complex properties. To augment the compound design process we introduce Mol-CycleGAN - a CycleGAN-based model that generates optimized compounds with high structural similarity to the original ones. Namely, given a molecule our model generates a structurally similar one with an optimized value of the considered property. We evaluate the performance of the model on selected optimization objectives related to structural properties (presence of halogen groups, number of aromatic rings) and to a physicochemical property (penalized logP). In the task of optimization of penalized logP of drug-like molecules our model significantly outperforms previous results.

Key facts. Cited by 66 papers in the map (data through 14 Oct 2026). First posted on arXiv 6 Feb 2019.

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Citing papers per year: 2021: 11; 2022: 16; 2023: 14; 2024: 16; 2025: 6; 2026: 3.

66 papers.

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  1. 2023A Complete Survey on Generative AI (AIGC): Is ChatGPT from GPT-4 to GPT-5 All You Need?cited by 84
  2. 2023Autoregressive Diffusion Model for Graph Generationcited by 83
  3. 2023Generative Diffusion Models on Graphs: Methods and Applicationscited by 81
  4. 2022A Survey on Deep Graph Generation: Methods and Applicationscited by 69
  5. 2022MolGenSurvey: A Systematic Survey in Machine Learning Models for Molecule Designcited by 66
  6. 2022Retrieval-based Controllable Molecule Generationcited by 44
  7. 2024RGFN: Synthesizable Molecular Generation Using GFlowNetscited by 41
  8. 2021DOCKSTRING: easy molecular docking yields better benchmarks for ligand designcited by 35
  9. 2023A Survey on Graph Diffusion Models: Generative AI in Science for Molecule, Protein and Materialcited by 35
  10. 2021JANUS: Parallel Tempered Genetic Algorithm Guided by Deep Neural Networks for Inverse Molecular Designcited by 32
  11. 2022Data-Efficient Graph Grammar Learning for Molecular Generationcited by 26
  12. 2024ArtFormer: Controllable Generation of Diverse 3D Articulated Objectscited by 22
38 more
  1. 2022Graph Neural Networks for Moleculescited by 21
  2. 2023Data-Centric Learning from Unlabeled Graphs with Diffusion Modelcited by 19
  3. 2022Target-aware Molecular Graph Generationcited by 18
  4. 2021C5T5: Controllable Generation of Organic Molecules with Transformerscited by 16
  5. 2022MGCVAE: Multi-objective Inverse Design via Molecular Graph Conditional Variational Autoencodercited by 16
  6. 2023A Simple and Scalable Representation for Graph Generationcited by 16
  7. 2024Exploring the Potential of Large Language Models in Graph Generationcited by 15
  8. 2025Multi-domain Distribution Learning for De Novo Drug Designcited by 15
  9. 2021Assigning Confidence to Molecular Property Predictioncited by 14
  10. 2022Molecule Generation for Drug Design: a Graph Learning Perspectivecited by 13
  11. 2024RetroGFN: Diverse and Feasible Retrosynthesis using GFlowNetscited by 12
  12. 2022Modular Flows: Differential Molecular Generationcited by 11
  13. 2024GP-MoLFormer: A Foundation Model For Molecular Generationcited by 11
  14. 2024LLM-Based Multi-Agent Systems are Scalable Graph Generative Modelscited by 11
  15. 2024Text-guided multi-property molecular optimization with a diffusion language modelcited by 11
  16. 2024Generative AI and Process Systems Engineering: The Next Frontiercited by 9
  17. 2021Polygrammar: Grammar for Digital Polymer Representation and Generationcited by 8
  18. 2022Conditional β-VAE for De Novo Molecular Generationcited by 8
  19. 2023Hybrid quantum cycle generative adversarial network for small molecule generationcited by 8
  20. 20243M-Diffusion: Latent Multi-Modal Diffusion for Language-Guided Molecular Structure Generationcited by 8
  21. 2022Power of Quantum Generative Learningcited by 6
  22. 2023ChiENN: Embracing Molecular Chirality with Graph Neural Networkscited by 6
  23. 2021PluGeN: Multi-Label Conditional Generation From Pre-Trained Modelscited by 5
  24. 2025Graph-structured Small Molecule Drug Discovery Through Deep Learning: Progress, Challenges, and Opportunitiescited by 5
  25. 2021Graph Energy-based Model for Substructure Preserving Molecular Designcited by 4
  26. 2023Optimizing Drug Design by Merging Generative AI With Active Learning Frameworkscited by 4
  27. 2024Cell Morphology-Guided Small Molecule Generation with GFlowNetscited by 4
  28. 2024Diffusion Twigs with Loop Guidance for Conditional Graph Generationcited by 4
  29. 2021A Binded VAE for Inorganic Material Generationcited by 3
  30. 2023Graph Generation with K2-treescited by 3
  31. 2023Statistically Optimal Generative Modeling with Maximum Deviation from the Empirical Distributioncited by 3
  32. 2024Quantum Machine Learning in Drug Discovery: Applications in Academia and Pharmaceutical Industriescited by 3
  33. 2025Backdoor Attacks on Discrete Graph Diffusion Modelscited by 3
  34. 2022PGMG: A Pharmacophore-Guided Deep Learning Approach for Bioactive Molecular Generationcited by 2
  35. 2023Kernel-Elastic Autoencoder for Molecular Designcited by 2
  36. 2024Large Generative Graph Modelscited by 2
  37. 2021An In-depth Summary of Recent Artificial Intelligence Applications in Drug Designcited by 1
  38. 2021Neuronal Learning Analysis using Cycle-Consistent Adversarial Networkscited by 1

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