Saved in:
| Main Authors: | , , , , , , , , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2501.01835 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866913634481864704 |
|---|---|
| author | Tu, Zhengkai Choure, Sourabh J. Fong, Mun Hong Roh, Jihye Levin, Itai Yu, Kevin Joung, Joonyoung F. Morgan, Nathan Li, Shih-Cheng Sun, Xiaoqi Lin, Huiqian Murnin, Mark Liles, Jordan P. Struble, Thomas J. Fortunato, Michael E. Liu, Mengjie Green, William H. Jensen, Klavs F. Coley, Connor W. |
| author_facet | Tu, Zhengkai Choure, Sourabh J. Fong, Mun Hong Roh, Jihye Levin, Itai Yu, Kevin Joung, Joonyoung F. Morgan, Nathan Li, Shih-Cheng Sun, Xiaoqi Lin, Huiqian Murnin, Mark Liles, Jordan P. Struble, Thomas J. Fortunato, Michael E. Liu, Mengjie Green, William H. Jensen, Klavs F. Coley, Connor W. |
| contents | The advancement of machine learning and the availability of large-scale reaction datasets have accelerated the development of data-driven models for computer-aided synthesis planning (CASP) in the past decade. Here, we detail the newest version of ASKCOS, an open source software suite for synthesis planning that makes available several research advances in a freely available, practical tool. Four one-step retrosynthesis models form the basis of both interactive planning and automatic planning modes. Retrosynthetic planning is complemented by other modules for feasibility assessment and pathway evaluation, including reaction condition recommendation, reaction outcome prediction, and auxiliary capabilities such as solubility prediction and quantum mechanical descriptor prediction. ASKCOS has assisted hundreds of medicinal, synthetic, and process chemists in their day-to-day tasks, complementing expert decision making. It is our belief that CASP tools like ASKCOS are an important part of modern chemistry research, and that they offer ever-increasing utility and accessibility. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_01835 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ASKCOS: an open source software suite for synthesis planning Tu, Zhengkai Choure, Sourabh J. Fong, Mun Hong Roh, Jihye Levin, Itai Yu, Kevin Joung, Joonyoung F. Morgan, Nathan Li, Shih-Cheng Sun, Xiaoqi Lin, Huiqian Murnin, Mark Liles, Jordan P. Struble, Thomas J. Fortunato, Michael E. Liu, Mengjie Green, William H. Jensen, Klavs F. Coley, Connor W. Artificial Intelligence The advancement of machine learning and the availability of large-scale reaction datasets have accelerated the development of data-driven models for computer-aided synthesis planning (CASP) in the past decade. Here, we detail the newest version of ASKCOS, an open source software suite for synthesis planning that makes available several research advances in a freely available, practical tool. Four one-step retrosynthesis models form the basis of both interactive planning and automatic planning modes. Retrosynthetic planning is complemented by other modules for feasibility assessment and pathway evaluation, including reaction condition recommendation, reaction outcome prediction, and auxiliary capabilities such as solubility prediction and quantum mechanical descriptor prediction. ASKCOS has assisted hundreds of medicinal, synthetic, and process chemists in their day-to-day tasks, complementing expert decision making. It is our belief that CASP tools like ASKCOS are an important part of modern chemistry research, and that they offer ever-increasing utility and accessibility. |
| title | ASKCOS: an open source software suite for synthesis planning |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2501.01835 |