# Diffusion Models for Molecules: A Survey of Methods and Tasks

arXiv: https://arxiv.org/abs/2502.09511 · HTML: https://mlsanity.com/paper/2502.09511/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Placed in Flow matching, time series and science, one of 14 directions in Diffusion models.
- Builds on 0 of its 74 references that are in the map.
- First posted on arXiv 13 Feb 2025.

## Placed in

- Diffusion models / [Flow matching, time series and science](https://mlsanity.com/diffusion/flow-matching-time-series-and-science/)

## Built on by (most cited first, 19 of 19)

- [A Survey of Graph Transformers: Architectures, Theories and Applications](https://mlsanity.com/paper/2502.16533/) (2025)
- [Materials Generation in the Era of Artificial Intelligence: A Comprehensive Survey](https://mlsanity.com/paper/2505.16379/) (2025)
- [Composition and Alignment of Diffusion Models using Constrained Learning](https://mlsanity.com/paper/2508.19104/) (2025)
- [MolSpectra: Pre-training 3D Molecular Representation with Multi-modal Energy Spectra](https://mlsanity.com/paper/2502.16284/) (2025)
- [Reference-guided Policy Optimization for Molecular Optimization via LLM Reasoning](https://mlsanity.com/paper/2603.05900/) (2026)
- [DiffSpectra: Molecular Structure Elucidation from Spectra using Diffusion Models](https://mlsanity.com/paper/2507.06853/) (2025)
- [Generative Models for Crystalline Materials](https://mlsanity.com/paper/2511.22652/) (2025)
- [Predictive Feature Caching for Training-free Acceleration of Molecular Geometry Generation](https://mlsanity.com/paper/2510.04646/) (2025)
- [Diffusion Graph Neural Networks and Dataset for Robust Olfactory Navigation in Hazard Robotics](https://mlsanity.com/paper/2506.00455/) (2025)
- [EnzyControl: Adding Functional and Substrate-Specific Control for Enzyme Backbone Generation](https://mlsanity.com/paper/2510.25132/) (2025)
- [Look the Other Way: Designing 'Positive' Molecules with Negative Data via Task Arithmetic](https://mlsanity.com/paper/2507.17876/) (2025)
- [3D-GSRD: 3D Molecular Graph Auto-Encoder with Selective Re-mask Decoding](https://mlsanity.com/paper/2510.16780/) (2025)
- [Chemistry-Enhanced Diffusion-Based Framework for Small-to-Large Molecular Conformation Generation](https://mlsanity.com/paper/2511.12182/) (2025)
- [Conditional Denoising Model as a Physical Surrogate Model](https://mlsanity.com/paper/2601.21021/) (2026)
- [MacroGuide: Topological Guidance for Macrocycle Generation](https://mlsanity.com/paper/2602.14977/) (2026)
- [Reading the Cell, Designing the Cure: Perturbation-Conditioned Molecular Diffusion for Function-Oriented Drug Design](https://mlsanity.com/paper/2605.15243/) (2026)
- [GenSBI: Generative Methods for Simulation-Based Inference in JAX](https://mlsanity.com/paper/2605.27499/) (2026)
- [Can We Predict The Human Preference For Text-to-Image Content Prior To Generation And Is It Even Useful To Do So?](https://mlsanity.com/paper/2606.05478/) (2026)
- [Physics-Guided Conditional Diffusion Model for Rare Event Synthesis and Diagnosis for the Water-Gas Shift Reaction](https://mlsanity.com/paper/2609.35499/) (2026)

## Abstract (arXiv)

Generative tasks about molecules, including but not limited to molecule generation, are crucial for drug discovery and material design, and have consistently attracted significant attention. In recent years, diffusion models have emerged as an impressive class of deep generative models, sparking extensive research and leading to numerous studies on their application to molecular generative tasks. Despite the proliferation of related work, there remains a notable lack of up-to-date and systematic surveys in this area. Particularly, due to the diversity of diffusion model formulations, molecular data modalities, and generative task types, the research landscape is challenging to navigate, hindering understanding and limiting the area's growth. To address this, this paper conducts a comprehensive survey of diffusion model-based molecular generative methods. We systematically review the research from the perspectives of methodological formulations, data modalities, and task types, offering a novel taxonomy. This survey aims to facilitate understanding and further flourishing development in this area. The relevant papers are summarized at: https://github.com/AzureLeon1/awesome-molecular-diffusion-models.