mlsanityData to 14 Oct 2026

Exploring Chemical Space with Score-based Out-of-distribution Generation

arXiv 2206.07632 ↗First posted 6 Jun 2022q-bio.BM cs.LG physics.chem-phcited by 95

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Cited by 35 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

A well-known limitation of existing molecular generative models is that the generated molecules highly resemble those in the training set. To generate truly novel molecules that may have even better properties for de novo drug discovery, more powerful exploration in the chemical space is necessary. To this end, we propose Molecular Out-Of-distribution Diffusion(MOOD), a score-based diffusion scheme that incorporates out-of-distribution (OOD) control in the generative stochastic differential equation (SDE) with simple control of a hyperparameter, thus requires no additional costs. Since some novel molecules may not meet the basic requirements of real-world drugs, MOOD performs conditional generation by utilizing the gradients from a property predictor that guides the reverse-time diffusion process to high-scoring regions according to target properties such as protein-ligand interactions, drug-likeness, and synthesizability. This allows MOOD to search for novel and meaningful molecules rather than generating unseen yet trivial ones. We experimentally validate that MOOD is able to explore the chemical space beyond the training distribution, generating molecules that outscore ones found with existing methods, and even the top 0.01% of the original training pool. Our code is available at https://github.com/SeulLee05/MOOD.

Key facts. Cited by 95 papers in the map (data through 14 Oct 2026). Builds on 50 of its 79 references that are in the map. First posted on arXiv 6 Jun 2022.

Builds on

50 of 79 references.

References · 50

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38 more
  1. 2021ILVR: Conditioning Method for Denoising Diffusion Probabilistic Modelscited by 635
  2. 2021Solving Inverse Problems in Medical Imaging with Score-Based Generative Modelscited by 521
  3. 2022GeoDiff: a Geometric Diffusion Model for Molecular Conformation Generationcited by 516
  4. 2021SRDiff: Single Image Super-Resolution with Diffusion Probabilistic Modelscited by 415
  5. 2019Your Classifier is Secretly an Energy Based Model and You Should Treat it Like Onecited by 405
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  7. 2021Flow Network based Generative Models for Non-Iterative Diverse Candidate Generationcited by 392
  8. 2018Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generationcited by 364
  9. 2021Score-based diffusion models for accelerated MRIcited by 311
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  13. 2022Score-based Generative Modeling of Graphs via the System of Stochastic Differential Equationscited by 283
  14. 2020Permutation Invariant Graph Generation via Score-Based Generative Modelingcited by 270
  15. 2017Molecular De Novo Design through Deep Reinforcement Learningcited by 267
  16. 2020Hierarchical Generation of Molecular Graphs using Structural Motifscited by 208
  17. 2018Optimization of Molecules via Deep Reinforcement Learningcited by 200
  18. 2018Fr'echet ChemNet Distance: A metric for generative models for molecules in drug discoverycited by 194
  19. 2018Constrained Graph Variational Autoencoders for Molecule Designcited by 185
  20. 2020MoFlow: An Invertible Flow Model for Generating Molecular Graphscited by 185
  21. 2022Pocket2Mol: Efficient Molecular Sampling Based on 3D Protein Pocketscited by 183
  22. 2021GraphDF: A Discrete Flow Model for Molecular Graph Generationcited by 180
  23. 2021Learning Gradient Fields for Molecular Conformation Generationcited by 163
  24. 2022A 3D Generative Model for Structure-Based Drug Designcited by 149
  25. 2021UNIT-DDPM: UNpaired Image Translation with Denoising Diffusion Probabilistic Modelscited by 133
  26. 2021MARS: Markov Molecular Sampling for Multi-objective Drug Discoverycited by 125
  27. 2017Objective-Reinforced Generative Adversarial Networks (ORGAN) for Sequence Generation Modelscited by 117
  28. 2021A Conditional Point Diffusion-Refinement Paradigm for 3D Point Cloud Completioncited by 100
  29. 2021GraphEBM: Molecular Graph Generation with Energy-Based Modelscited by 85
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  31. 2022Generating High Fidelity Data from Low-density Regions using Diffusion Modelscited by 78
  32. 2018Molecular generative model based on conditional variational autoencoder for de novo molecular designcited by 73
  33. 2021Hit and Lead Discovery with Explorative RL and Fragment-based Molecule Generationcited by 68
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  35. 2020Guiding Deep Molecular Optimization with Genetic Explorationcited by 51
  36. 2021DOCKSTRING: easy molecular docking yields better benchmarks for ligand designcited by 35
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 1; 2023: 11; 2024: 35; 2025: 30; 2026: 18.

95 papers.

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  1. 2022Diffusion Models: A Comprehensive Survey of Methods and Applicationscited by 1,114
  2. 2024Derivative-Free Guidance in Continuous and Discrete Diffusion Models with Soft Value-Based Decodingcited by 136
  3. 2023Generative Diffusion Models on Graphs: Methods and Applicationscited by 81
  4. 2024Discrete-state Continuous-time Diffusion for Graph Generationcited by 76
  5. 2025GenMol: A Drug Discovery Generalist with Discrete Diffusioncited by 50
  6. 2024Graph Diffusion Transformers for Multi-Conditional Molecular Generationcited by 45
  7. 2024Genetic-guided GFlowNets for Sample Efficient Molecular Optimizationcited by 39
  8. 2023Don't Play Favorites: Minority Guidance for Diffusion Modelscited by 37
  9. 2025Dynamic Search for Inference-Time Alignment in Diffusion Modelscited by 37
  10. 2024Diffusion Models in textitDe Novo Drug Designcited by 26
  11. 2023Learning Subpocket Prototypes for Generalizable Structure-based Drug Designcited by 25
  12. 2024Molecule Generation with Fragment Retrieval Augmentationcited by 23
38 more
  1. 2024Generative Flows on Synthetic Pathway for Drug Designcited by 20
  2. 2023Drug Discovery with Dynamic Goal-aware Fragmentscited by 19
  3. 2023Searching for High-Value Molecules Using Reinforcement Learning and Transformerscited by 18
  4. 2024ParetoFlow: Guided Flows in Multi-Objective Optimizationcited by 17
  5. 2023DiffusionNAG: Predictor-guided Neural Architecture Generation with Diffusion Modelscited by 16
  6. 2024Saturn: Sample-efficient Generative Molecular Design using Memory Manipulationcited by 15
  7. 2025GOLD: Graph Out-of-Distribution Detection via Implicit Adversarial Latent Generationcited by 15
  8. 2025Offline Model-Based Optimization: Comprehensive Reviewcited by 15
  9. 2024Graph Diffusion Policy Optimizationcited by 13
  10. 2024TAGMol: Target-Aware Gradient-guided Molecule Generationcited by 12
  11. 2024Context-Guided Diffusion for Out-of-Distribution Molecular and Protein Designcited by 11
  12. 2024Graph Representation Learning via Causal Diffusion for Out-of-Distribution Recommendationcited by 11
  13. 2024LLM-Based Multi-Agent Systems are Scalable Graph Generative Modelscited by 11
  14. 2025Iterative Distillation for Reward-Guided Fine-Tuning of Diffusion Models in Biomolecular Designcited by 11
  15. 2023Lo-Hi: Practical ML Drug Discovery Benchmarkcited by 10
  16. 2023Identifying Semantic Component for Robust Molecular Property Predictioncited by 10
  17. 2024Design Editing for Offline Model-based Optimizationcited by 10
  18. 2024Neural Graph Generator: Feature-Conditioned Graph Generation using Latent Diffusion Modelscited by 8
  19. 2025ExLLM: Experience-Enhanced LLM Optimization for Molecular Design and Beyondcited by 8
  20. 2025Learning Flexible Forward Trajectories for Masked Molecular Diffusioncited by 7
  21. 2025Controllable Graph Generation with Diffusion Models via Inference-Time Tree Search Guidancecited by 7
  22. 2023GraphGUIDE: interpretable and controllable conditional graph generation with discrete Bernoulli diffusioncited by 6
  23. 2024Chemistry-Inspired Diffusion with Non-Differentiable Guidancecited by 6
  24. 2024GDDA: Semantic OOD Detection on Graphs under Covariate Shift via Score-Based Diffusion Modelscited by 5
  25. 2025Graph-structured Small Molecule Drug Discovery Through Deep Learning: Progress, Challenges, and Opportunitiescited by 5
  26. 2026Graph-GRPO: Training Graph Flow Models with Reinforcement Learningcited by 5
  27. 2024Synthetic Data from Diffusion Models Improve Drug Discovery Predictioncited by 4
  28. 2024Diffusion Twigs with Loop Guidance for Conditional Graph Generationcited by 4
  29. 2025Pretraining Generative Flow Networks with Inexpensive Rewards for Molecular Graph Generationcited by 4
  30. 2024Improving Molecule Generation and Drug Discovery with a Knowledge-enhanced Generative Modelcited by 3
  31. 2024Guided Multi-objective Generative AI to Enhance Structure-based Drug Designcited by 3
  32. 2024Regressor-free Molecule Generation to Support Drug Response Predictioncited by 3
  33. 2024Any-Property-Conditional Molecule Generation with Self-Criticism using Spanning Treescited by 3
  34. 2024TurboHopp: Accelerated Molecule Scaffold Hopping with Consistency Modelscited by 3
  35. 2025Gen-C: Populating Virtual Worlds with Generative Crowdscited by 3
  36. 2026ToolMol: Evolutionary Agentic Framework for Multi-objective Drug Discoverycited by 3
  37. 2023Diffusion Models for Interferometric Satellite Aperture Radarcited by 2
  38. 2023TacoGFN: Target-conditioned GFlowNet for Structure-based Drug Designcited by 2

50 most cited of 95 All 95 (JSON)