Motif2Mol:使用生物化学语言模型,根据蛋白质中连接体结合部位的序列基因预测新的活性化合物
Atsushi Yoshimori1, Jürgen Bajorath2
1Institute for Theoretical Medicine, Inc., 26-1 Muraoka-Higashi 2-Chome, Fujisawa 251-0012, Japan.
Biomolecules
|May 27, 2023
概括
从蛋白质序列中预测活性药物化合物是具有挑战性的. 一个新的生物化学语言模型,Motif2Mol,利用深度学习将蛋白质序列图案翻译成新的化学结构,在药物设计中显示出前景.
科学领域:
- 生物化学 生物化学
- 计算机化药物发现.
- 生物信息学是一种生物信息学.
背景情况:
- 从蛋白质序列中预测活性化合物是药物设计中的一个关键挑战.
- 蛋白质序列相似性并不总是与连接体结合亲和关系.
- 深度学习和自然语言处理为基于序列的预测提供了新的方法.
研究的目的:
- 引入一种新的生物化学语言模型,用于从蛋白质序列数据中预测活性化合物.
- 开发一种基于变压器的模型,用于将蛋白质序列图案转化为化学结构.
- 为了证明该模型在用于激酶抑制剂的药物设计中的能力.
主要方法:
- 开发了一个使用变压器架构的生物化学语言模型 (Motif2Mol).
- 采用机器翻译原理,将氨基酸序列与化学结构联系起来.
- 应用该模型来预测200多个人类激酶的抑制剂.
主要成果:
- 莫蒂夫2莫尔模型展示了有前途的学习能力.
- 该模型成功地复制了各种激酶的已知抑制剂.
- 从序列图案中预测活性化合物的前所未有的能力.
结论:
- Motif2Mol模型代表了从蛋白质序列预测活性化合物的重大进步.
- 这种方法有可能加速药物发现和设计.
- 生物化学语言模型可以有效地弥合蛋白质序列和化学结构信息之间的差距.
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