A Foundation Chemical Language Model for Comprehensive Fragment-Based Drug Discovery
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908556466323456 |
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| author | Ho, Alexander Lee, Sukyeong Tsai, Francis T. F. |
| author_facet | Ho, Alexander Lee, Sukyeong Tsai, Francis T. F. |
| contents | We introduce FragAtlas-62M, a specialized foundation model trained on the largest fragment dataset to date. Built on the complete ZINC-22 fragment subset comprising over 62 million molecules, it achieves unprecedented coverage of fragment chemical space. Our GPT-2 based model (42.7M parameters) generates 99.90% chemically valid fragments. Validation across 12 descriptors and three fingerprint methods shows generated fragments closely match the training distribution (all effect sizes < 0.4). The model retains 53.6% of known ZINC fragments while producing 22% novel structures with practical relevance. We release FragAtlas-62M with training code, preprocessed data, documentation, and model weights to accelerate adoption. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_19586 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Foundation Chemical Language Model for Comprehensive Fragment-Based Drug Discovery Ho, Alexander Lee, Sukyeong Tsai, Francis T. F. Machine Learning Artificial Intelligence Biomolecules We introduce FragAtlas-62M, a specialized foundation model trained on the largest fragment dataset to date. Built on the complete ZINC-22 fragment subset comprising over 62 million molecules, it achieves unprecedented coverage of fragment chemical space. Our GPT-2 based model (42.7M parameters) generates 99.90% chemically valid fragments. Validation across 12 descriptors and three fingerprint methods shows generated fragments closely match the training distribution (all effect sizes < 0.4). The model retains 53.6% of known ZINC fragments while producing 22% novel structures with practical relevance. We release FragAtlas-62M with training code, preprocessed data, documentation, and model weights to accelerate adoption. |
| title | A Foundation Chemical Language Model for Comprehensive Fragment-Based Drug Discovery |
| topic | Machine Learning Artificial Intelligence Biomolecules |
| url | https://arxiv.org/abs/2509.19586 |