# Text-Guided Molecule Generation with Diffusion Language Model

arXiv: https://arxiv.org/abs/2402.13040 · HTML: https://mlsanity.com/paper/2402.13040/

## Key facts

- Cited by 31 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 14 of its 37 references that are in the map.
- First posted on arXiv 20 Feb 2024.

## Placed in

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

## Builds on (most cited first, 14 of 14)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [Sparks of Artificial General Intelligence: Early experiments with GPT-4](https://mlsanity.com/paper/2303.12712/) (2023)
- [DiffWave: A Versatile Diffusion Model for Audio Synthesis](https://mlsanity.com/paper/2009.09761/) (2020)
- [Diffusion-LM Improves Controllable Text Generation](https://mlsanity.com/paper/2205.14217/) (2022)
- [Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation](https://mlsanity.com/paper/1806.02473/) (2018)
- [DiffusionBERT: Improving Generative Masked Language Models with Diffusion Models](https://mlsanity.com/paper/2211.15029/) (2022)
- [Graph neural networks for materials science and chemistry](https://mlsanity.com/paper/2208.09481/) (2022)
- [Multi-modal Molecule Structure-text Model for Text-based Retrieval and Editing](https://mlsanity.com/paper/2212.10789/) (2022)
- [Uncovering Neural Scaling Laws in Molecular Representation Learning](https://mlsanity.com/paper/2309.15123/) (2023)

## Built on by (most cited first, 30 of 31)

- [A Survey on Diffusion Language Models](https://mlsanity.com/paper/2508.10875/) (2025)
- [Leveraging Biomolecule and Natural Language through Multi-Modal Learning: A Survey](https://mlsanity.com/paper/2403.01528/) (2024)
- [Periodic Materials Generation using Text-Guided Joint Diffusion Model](https://mlsanity.com/paper/2503.00522/) (2025)
- [LDMol: A Text-to-Molecule Diffusion Model with Structurally Informative Latent Space Surpasses AR Models](https://mlsanity.com/paper/2405.17829/) (2024)
- [Text-guided multi-property molecular optimization with a diffusion language model](https://mlsanity.com/paper/2410.13597/) (2024)
- [Beyond Efficiency: Molecular Data Pruning for Enhanced Generalization](https://mlsanity.com/paper/2409.01081/) (2024)
- [MolSpectra: Pre-training 3D Molecular Representation with Multi-modal Energy Spectra](https://mlsanity.com/paper/2502.16284/) (2025)
- [Simplifying Bayesian Optimization Via In-Context Direct Optimum Sampling](https://mlsanity.com/paper/2505.23913/) (2025)
- [How to Make Large Language Models Generate 100% Valid Molecules?](https://mlsanity.com/paper/2509.23099/) (2025)
- [Instruction-Based Molecular Graph Generation with Unified Text-Graph Diffusion Model](https://mlsanity.com/paper/2408.09896/) (2024)
- [Unraveling the Potential of Diffusion Models in Small Molecule Generation](https://mlsanity.com/paper/2507.08005/) (2025)
- [MolEdit: Knowledge Editing for Multimodal Molecule Language Models](https://mlsanity.com/paper/2511.12770/) (2025)
- [Diffusion-based Evolutionary Optimization for 3D Multi-Objective Molecular Generation](https://mlsanity.com/paper/2505.11037/) (2025)
- [CombiMOTS: Combinatorial Multi-Objective Tree Search for Dual-Target Molecule Generation](https://mlsanity.com/paper/2604.23307/) (2026)
- [TrustLDM: Benchmarking Trustworthiness in Language Diffusion Models](https://mlsanity.com/paper/2606.00023/) (2026)
- [Training Text-to-Molecule Models with Context-Aware Tokenization](https://mlsanity.com/paper/2509.04476/) (2025)
- [Transport-Coupled Bayesian Flows for Molecular Graph Generation](https://mlsanity.com/paper/2510.10211/) (2025)
- [MolSafeEval: A Benchmark for Uncovering Safety Risks in AI-Generated Molecules](https://mlsanity.com/paper/2607.00464/) (2026)
- [Chemical Language Model Linker: blending text and molecules with modular adapters](https://mlsanity.com/paper/2410.20182/) (2024)
- [Automatic Annotation Augmentation Boosts Translation between Molecules and Natural Language](https://mlsanity.com/paper/2502.06634/) (2025)
- [TextOmics-Guided Diffusion for Hit-like Molecular Generation](https://mlsanity.com/paper/2507.09982/) (2025)
- [Cross-Modality Controlled Molecule Generation with Diffusion Language Model](https://mlsanity.com/paper/2508.14748/) (2025)
- [Valid Property-Enhanced Contrastive Learning for Targeted Optimization & Resampling for Novel Drug Design](https://mlsanity.com/paper/2509.00684/) (2025)
- [Chain-of-Generation: Progressive Latent Diffusion for Text-Guided Molecular Design](https://mlsanity.com/paper/2511.11894/) (2025)
- [MolRGen: A Training and Evaluation Setting for De Novo Molecular Generation with Reasonning Models](https://mlsanity.com/paper/2603.18256/) (2026)
- [MolDA: Molecular Understanding and Generation via Large Language Diffusion Model](https://mlsanity.com/paper/2604.04403/) (2026)
- [BiMol-Diff: A Unified Diffusion Framework for Molecular Generation and Captioning](https://mlsanity.com/paper/2604.24089/) (2026)
- [Agentic Molecular Recovery via Molecule-Aware Exploration](https://mlsanity.com/paper/2606.05847/) (2026)
- [Gene Expression-Informed Jointly Controlled Generative Modeling for Precision Molecular Design](https://mlsanity.com/paper/2607.11978/) (2026)
- [HEDGEHOG: Hierarchical Evaluation of Drug Generators Through Rigorous Filtration](https://mlsanity.com/paper/2607.13155/) (2026)

## Abstract (arXiv)

Text-guided molecule generation is a task where molecules are generated to match specific textual descriptions. Recently, most existing SMILES-based molecule generation methods rely on an autoregressive architecture. In this work, we propose the Text-Guided Molecule Generation with Diffusion Language Model (TGM-DLM), a novel approach that leverages diffusion models to address the limitations of autoregressive methods. TGM-DLM updates token embeddings within the SMILES string collectively and iteratively, using a two-phase diffusion generation process. The first phase optimizes embeddings from random noise, guided by the text description, while the second phase corrects invalid SMILES strings to form valid molecular representations. We demonstrate that TGM-DLM outperforms MolT5-Base, an autoregressive model, without the need for additional data resources. Our findings underscore the remarkable effectiveness of TGM-DLM in generating coherent and precise molecules with specific properties, opening new avenues in drug discovery and related scientific domains. Code will be released at: https://github.com/Deno-V/tgm-dlm.