mlsanityData to 7 Oct 2026

Learning Multimodal Graph-to-Graph Translation for Molecular Optimization

arXiv 1812.01070 ↗First posted 3 Dec 2018cs.LG cs.AI cs.NEcited by 87

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Abstract (arXiv)

We view molecular optimization as a graph-to-graph translation problem. The goal is to learn to map from one molecular graph to another with better properties based on an available corpus of paired molecules. Since molecules can be optimized in different ways, there are multiple viable translations for each input graph. A key challenge is therefore to model diverse translation outputs. Our primary contributions include a junction tree encoder-decoder for learning diverse graph translations along with a novel adversarial training method for aligning distributions of molecules. Diverse output distributions in our model are explicitly realized by low-dimensional latent vectors that modulate the translation process. We evaluate our model on multiple molecular optimization tasks and show that our model outperforms previous state-of-the-art baselines.

Key facts. Cited by 87 papers in the map (data through 7 Oct 2026). First posted on arXiv 3 Dec 2018.

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Citing papers per year: 2021: 25; 2022: 18; 2023: 11; 2024: 12; 2025: 12; 2026: 9.

87 papers.

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  1. 2021Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Developmentcited by 258
  2. 2021Weisfeiler and Lehman Go Cellular: CW Networkscited by 218
  3. 2021MARS: Markov Molecular Sampling for Multi-objective Drug Discoverycited by 125
  4. 2023A Complete Survey on Generative AI (AIGC): Is ChatGPT from GPT-4 to GPT-5 All You Need?cited by 84
  5. 2021Break-It-Fix-It: Unsupervised Learning for Program Repaircited by 71
  6. 2022A Survey on Deep Graph Generation: Methods and Applicationscited by 69
  7. 2022MolGenSurvey: A Systematic Survey in Machine Learning Models for Molecule Designcited by 66
  8. 2023DrugAssist: A Large Language Model for Molecule Optimizationcited by 57
  9. 2021Differentiable Scaffolding Tree for Molecular Optimizationcited by 53
  10. 2022Graph-based Molecular Representation Learningcited by 51
  11. 2025UniGraph2: Learning a Unified Embedding Space to Bind Multimodal Graphscited by 47
  12. 2022Retrieval-based Controllable Molecule Generationcited by 44
38 more
  1. 2022LIMO: Latent Inceptionism for Targeted Molecule Generationcited by 40
  2. 2023A Survey on Graph Diffusion Models: Generative AI in Science for Molecule, Protein and Materialcited by 34
  3. 2021DOCKSTRING: easy molecular docking yields better benchmarks for ligand designcited by 33
  4. 2021JANUS: Parallel Tempered Genetic Algorithm Guided by Deep Neural Networks for Inverse Molecular Designcited by 31
  5. 2022Computer-Aided Multi-Objective Optimization in Small Molecule Discoverycited by 31
  6. 2023Coarse-to-Fine: a Hierarchical Diffusion Model for Molecule Generation in 3Dcited by 24
  7. 2024Learning on Multimodal Graphs: A Surveycited by 22
  8. 2023Domain-Agnostic Molecular Generation with Chemical Feedbackcited by 21
  9. 2022Controllable Data Generation by Deep Learning: A Reviewcited by 20
  10. 2025Adaptation of Agentic AI: A Survey of Post-Training, Memory, and Skillscited by 19
  11. 2021Semi-Supervised Graph-to-Graph Translationcited by 18
  12. 2022Proactive Robot Assistance via Spatio-Temporal Object Modelingcited by 18
  13. 2025NExT-Mol: 3D Diffusion Meets 1D Language Modeling for 3D Molecule Generationcited by 17
  14. 2024Leveraging Biomolecule and Natural Language through Multi-Modal Learning: A Surveycited by 16
  15. 2025ChemMLLM: Chemical Multimodal Large Language Modelcited by 16
  16. 2021C5T5: Controllable Generation of Organic Molecules with Transformerscited by 15
  17. 2022Molecule Generation for Drug Design: a Graph Learning Perspectivecited by 13
  18. 2021Channel Exchanging Networks for Multimodal and Multitask Dense Image Predictioncited by 11
  19. 2022Scalable Fragment-Based 3D Molecular Design with Reinforcement Learningcited by 11
  20. 2024GP-MoLFormer: A Foundation Model For Molecular Generationcited by 11
  21. 2022Improving Small Molecule Generation using Mutual Information Machinecited by 10
  22. 2023Deep Learning Methods for Small Molecule Drug Discovery: A Surveycited by 10
  23. 2021GraphGen-Redux: a Fast and Lightweight Recurrent Model for labeled Graph Generationcited by 9
  24. 2022Autoencoding Hyperbolic Representation for Adversarial Generationcited by 7
  25. 2024Equivariant Denoisers Cannot Copy Graphs: Align Your Graph Diffusion Modelscited by 7
  26. 2021Learning Space Partitions for Path Planningcited by 6
  27. 2021Fragment-based Sequential Translation for Molecular Optimizationcited by 6
  28. 2023Bi-level Contrastive Learning for Knowledge-Enhanced Molecule Representationscited by 6
  29. 2023Geometrically Aligned Transfer Encoder for Inductive Transfer in Regression Taskscited by 6
  30. 2025ExLLM: Experience-Enhanced LLM Optimization for Molecular Design and Beyondcited by 6
  31. 2021PluGeN: Multi-Label Conditional Generation From Pre-Trained Modelscited by 5
  32. 2022Grouping-matrix based Graph Pooling with Adaptive Number of Clusterscited by 5
  33. 2024An Evaluation of Large Language Models in Bioinformatics Researchcited by 5
  34. 2021CCGG: A Deep Autoregressive Model for Class-Conditional Graph Generationcited by 4
  35. 2024PAC-Bayesian Adversarially Robust Generalization Bounds for Graph Neural Networkcited by 4
  36. 2024Implicitly Guided Design with PropEn: Match your Data to Follow the Gradientcited by 4
  37. 2025Graph Diffusion that can Insert and Deletecited by 4
  38. 2022Generative Enriched Sequential Learning (ESL) Approach for Molecular Design via Augmented Domain Knowledgecited by 3

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