作为一个以缩放规律为导向的基础模型,ChemFM预先培训了信息化化学品的基础模型
Feiyang Cai1, Katelin Zacour2, Tianyu Zhu3
1School of Computing, Clemson University, Clemson, SC, USA.
Communications chemistry
|December 18, 2025
概括
化学的新基础模型ChemFM使用了对1.78亿个分子的自我监督学习. 这种人工智能 (AI) 模型显著提高了各种化学任务的性能,推进了药物发现和材料科学.
科学领域:
- 人工智能的人工智能
- 计算化学的计算化学
- 药物发现 药物发现 药物发现
背景情况:
- 传统的AI模型需要特定任务的设计,限制了可扩展性和通用性.
- 开发用于各种化学应用的多功能AI仍然是一个挑战.
研究的目的:
- 介绍ChemFM,一个用于化学应用的大型基础模型.
- 与任务特定模型相比,证明ChemFM的优越性能和数据效率.
主要方法:
- 使用UniChem数据库和自我监督的因果语言建模,对1.78亿个分子进行了30亿参数模型 (ChemFM) 的预训练.
- 微调的ChemFM用于下游任务,包括属性预测,分子生成和反应预测.
- 评估了34个属性预测基准,分子生成任务和4个反应预测数据集的性能.
主要成果:
- 在所有测试任务中,ChemFM在所有测试任务中都超过了最先进的任务特定AI模型.
- 在物业预测方面实现了高达67.48%的性能改进,在反应预测方面达到3.7%.
- 在预测抗生素活性和细胞毒性方面表现出卓越的性能,具有显著的数据效率.
结论:
- 化学FM提供了一个强大的,可通用的化学基础模型.
- 该模型推进了化学研究中的AI应用,加速了新分子和材料的发现.
- ChemFM的适应性和数据效率为更广泛的AI在化学领域的采用铺平了道路.
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