变压器解码器从预训练的蛋白质语言模型中学习生成具有高 afinity 的配体
Teresa Maria Creanza1, Domenico Alberga2, Cosimo Patruno1
1Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing, Consiglio Nazionale delle Ricerche, Via G. Amendola, 122/d, Bari 70126, Italy.
Journal of chemical information and modeling
|January 28, 2025
概括
深度学习模型Prot2Drug通过学习蛋白质-连接体相互作用来设计新的药物分子. 它通过生成针对特定蛋白质点的高亲和度化合物来加速药物发现,即使数据有限.
科学领域:
- 计算化学是一种计算化学.
- 人工智能在药物发现中的作用
- 分子建模分子建模
背景情况:
- 药物发现是一个漫长而昂贵的过程.
- 深度学习有可能加速识别候选药物.
- 识别能够结合特定蛋白质标的分子对于药物开发至关重要.
研究的目的:
- 介绍一个新的深度学习生成模型,Prot2Drug,用于设计类似药物的分子.
- 为了利用预训练的蛋白质语言模型和转换器能力来实现针对特定目标的配体生成.
- 通过建议对感兴趣的蛋白质的高亲缘关系联体来加速药物发现管道.
主要方法:
- 开发了Prot2Drug,这是一个深度学习生成模型.
- 使用预训练的蛋白质语言模型来获取目标信息.
- 使用变压器从广泛的蛋白质-连接体相互作用数据中学习.
- 产生的新型化合物具有预测有利的物理化学性质和目标亲和力.
主要成果:
- Prot2Drug成功地产生了对特定蛋白质标具有高预测亲和力的新型配体.
- 该模型重现了已知的蛋白质-连接体相互作用.
- 通过对已知的化合物提出新的点,确定了潜在的药物重定向候选人.
- 即使对结构或连接体信息有限的蛋白质点也证明了有效性.
结论:
- Prot2Drug通过产生向连接体来显著加速药物发现.
- 该模型能够从多样化的数据中学习,这使得它能够有效地为具有挑战性的目标设计连接体.
- "Prot2Drug"在识别新疗法和药物重定向机会方面表现有前途.
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