Projecting Molecules into Synthesizable Chemical Spaces
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2024
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866915686994935808 |
|---|---|
| author | Luo, Shitong Gao, Wenhao Wu, Zuofan Peng, Jian Coley, Connor W. Ma, Jianzhu |
| author_facet | Luo, Shitong Gao, Wenhao Wu, Zuofan Peng, Jian Coley, Connor W. Ma, Jianzhu |
| contents | Discovering new drug molecules is a pivotal yet challenging process due to the near-infinitely large chemical space and notorious demands on time and resources. Numerous generative models have recently been introduced to accelerate the drug discovery process, but their progression to experimental validation remains limited, largely due to a lack of consideration for synthetic accessibility in practical settings. In this work, we introduce a novel framework that is capable of generating new chemical structures while ensuring synthetic accessibility. Specifically, we introduce a postfix notation of synthetic pathways to represent molecules in chemical space. Then, we design a transformer-based model to translate molecular graphs into postfix notations of synthesis. We highlight the model's ability to: (a) perform bottom-up synthesis planning more accurately, (b) generate structurally similar, synthesizable analogs for unsynthesizable molecules proposed by generative models with their properties preserved, and (c) explore the local synthesizable chemical space around hit molecules. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_04628 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Projecting Molecules into Synthesizable Chemical Spaces Luo, Shitong Gao, Wenhao Wu, Zuofan Peng, Jian Coley, Connor W. Ma, Jianzhu Computational Engineering, Finance, and Science Quantitative Methods Discovering new drug molecules is a pivotal yet challenging process due to the near-infinitely large chemical space and notorious demands on time and resources. Numerous generative models have recently been introduced to accelerate the drug discovery process, but their progression to experimental validation remains limited, largely due to a lack of consideration for synthetic accessibility in practical settings. In this work, we introduce a novel framework that is capable of generating new chemical structures while ensuring synthetic accessibility. Specifically, we introduce a postfix notation of synthetic pathways to represent molecules in chemical space. Then, we design a transformer-based model to translate molecular graphs into postfix notations of synthesis. We highlight the model's ability to: (a) perform bottom-up synthesis planning more accurately, (b) generate structurally similar, synthesizable analogs for unsynthesizable molecules proposed by generative models with their properties preserved, and (c) explore the local synthesizable chemical space around hit molecules. |
| title | Projecting Molecules into Synthesizable Chemical Spaces |
| topic | Computational Engineering, Finance, and Science Quantitative Methods |
| url | https://arxiv.org/abs/2406.04628 |