Summary of Text-guided Molecule Generation with Diffusion Language Model, by Haisong Gong et al.
Text-Guided Molecule Generation with Diffusion Language Model
by Haisong Gong, Qiang Liu, Shu Wu, Liang Wang
First submitted to arxiv on: 20 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computational Engineering, Finance, and Science (cs.CE); Computation and Language (cs.CL); Biomolecules (q-bio.BM)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Text-Guided Molecule Generation with Diffusion Language Model (TGM-DLM) is a novel approach that leverages diffusion models to overcome the limitations of autoregressive methods in generating molecules. By updating token embeddings within the SMILES string collectively and iteratively, TGM-DLM optimizes embeddings from random noise, guided by textual descriptions, and then corrects invalid SMILES strings to form valid molecular representations. This method outperforms MolT5-Base, an autoregressive model, without requiring additional data resources. The results demonstrate the effectiveness of TGM-DLM in generating coherent and precise molecules with specific properties, opening new avenues in drug discovery and related scientific domains. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to generate molecules based on text descriptions. It uses a special type of model called a diffusion language model. This model helps create valid molecular structures by updating the SMILES string iteratively. The results show that this method is better than another popular approach without needing extra data. This can help scientists discover new medicines and understand how molecules work. |
Keywords
* Artificial intelligence * Autoregressive * Diffusion * Language model * Token
