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arXiv · 2509.19586

A Foundation Chemical Language Model for Comprehensive Fragment-Based Drug Discovery

Alexander Ho·Sukyeong Lee·Francis T. F. Tsai
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Abstract

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.

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Alexander Ho, Sukyeong Lee, Francis T. F. Tsai. 2025-09-23. A Foundation Chemical Language Model for Comprehensive Fragment-Based Drug Discovery. https://arxiv.org/abs/2509.19586

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