MetaPredictor:基于深度语言模型的药物代谢物的in silico预测,具有快速工程
Keyun Zhu1, Mengting Huang1, Yimeng Wang1
1Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China.
Briefings in bioinformatics
|July 31, 2024
概括
新型计算工具MetaPredictor使用基于提示的深度学习准确预测人类药物代谢物. 这种方法通过提高代谢物预测准确度和减少假阳性来增强药物发现.
科学领域:
- 计算化学是一种计算化学.
- 药理学 药理学是指药理学的学科.
- 人工智能在药物发现中的作用
背景情况:
- 药物代谢显著影响药物的有效性和安全性,需要在药物发现过程中准确预测代谢命运.
- 目前用于药物代谢物预测的计算方法面临着模型概括和高假阳性率的挑战.
研究的目的:
- 开发一种无规则,端到端的计算方法,MetaPredictor,用于预测人类药物代谢物.
- 为了提高代谢物预测的准确性和减少虚假阳性,使用快速工程和转移学习.
主要方法:
- MetaPredictor采用基于提示的深度语言模型方法,将代谢物预测视为序列翻译问题.
- 该方法结合了快速工程来指定代谢部位 (SoM),并利用转移学习来解决有限的代谢数据.
- 一个两阶段的方案使得自动SoM识别和随后的代谢物预测用于非专家使用.
主要成果:
- 与基线模型相比,基于特定SoM的即时预测提高了30.4%的回忆力,并减少了16.8%的错误阳性.
- 在主要酶家族上,MetaPredictor表现出与现有工具相匹配的性能,在较少常见的酶上表现出优越的概括性.
- 该工具通过整合转移和基于提示的学习实现了全面而准确的药物代谢预测.
结论:
- MetaPredictor在计算药物代谢物预测方面取得了重大进展,克服了以前方法的局限性.
- 快速工程和转移学习的整合为了解药物代谢提供了一种强大而可泛化的方法.
- 该工具可以通过提供更准确,更全面的药物代谢途径洞察力,帮助加速药物发现.
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