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使用ChatGPT进行分子性质预测的全面评估

Xibao Cai¹, Houtim Lai², Xing Wang²

¹Department of Computer Science, Hunan University, China.

Methods (San Diego, Calif.)

|January 19, 2024

概括

聊天GPT在预测药物发现的分子性质方面表现有前途,在使用优化提示时,可以与专业模型取得具有竞争力的结果. 然而,性能取决于示例质量,影响现实世界的适用性.

科学领域:

人工智能的人工智能
计算化学计算化学
药物发现药物发现药物发现

背景情况:

像ChatGPT这样的大型语言模型 (LLM) 具有广泛的任务多功能性.
它们在专门的科学领域的应用,如药物发现,需要进行彻底的评估.

研究的目的:

综合评估ChatGPT在预测药物发现分子特性方面的能力.
调查快速工程和数据采样对预测准确性的影响.
与现有的专业模型相比,分析聊天GPT的潜力和局限性.

主要方法:

在53个ADMET相关的终点上评估了ChatGPT,用于分子性质预测.
研究了各种提示设置的影响,包括使用脚手架采样进行几次射击学习.
将ChatGPT的性能与已建立的任务特定模型进行比较.

主要成果:

在优化的快速条件下,ChatGPT取得了令人满意的预测结果,与专业模型相竞争.
快速设置,特别是少数镜头样本选择和支架采样,显著影响了性能.
预测的准确性主要取决于所提供的示例的质量.

结论:

通过适当的快速策略,ChatGPT显示了在药物发现中对分子性质预测的巨大潜力.
依赖于示例质量对广泛的实际应用提出了限制.
这项研究为未来的LLM发展和科学领域的评估提供了洞察力.

关键词:

聊天GPT 聊天在GPT 聊天评价评价评价只有几次射击.分子属性预测分子属性预测快速地提醒了他们.

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