代币-Mol 1.0:使用大型语言模型的代币化药物设计.
Jike Wang1, Rui Qin1, Mingyang Wang1
1College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, Zhejiang, China.
Nature communications
|May 13, 2025
概括
一个新的3D药物设计模型Token-Mol有效地集成了2D和3D分子数据. 这种人工智能方法通过显著改善分子生成和属性预测来加速药物发现.
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:
- 大型语言模型 (LLM) 在药物设计方面表现有前途,但往往无法有效地结合3D分子结构.
- 现有的方法在为人工智能模型表示复杂的分子信息方面面临挑战.
研究的目的:
- 介绍Token-Mol,这是一个仅用于3D药物设计的代币模型,它将2D/3D结构和属性编码为离散的代币.
- 使用人工智能增强分子构造生成,属性预测和基于口袋的分子生成.
主要方法:
- 开发了基于变压器解码器的模型Token-Mol,使用因果掩盖.
- 引入了回归任务的高斯交叉损失函数.
- 将2D/3D分子结构和属性编码为离散的令牌.
主要成果:
- 在两个数据集上,Token-Mol在两个数据集上提高了超过10%和20%的分子构造生成.
- 与现有的仅使用代币的模型相比,实现了30%更好的财产预测.
- 提升了药物相似性和合成可访问性,在基于口袋的生成中分别增加了约11%和14%.
- 与扩散模型相比,演示了35倍的速度改进,并提高了现实世界的成功率.
结论:
- 通过整合多样化的分子信息,Token-Mol为3D药物设计提供了强大而高效的解决方案.
- 该模型通过提高性能和速度显著推进了人工智能驱动的药物发现.
- 将Token-Mol与强化学习相结合,进一步优化了关键的药物相似性和亲和力参数.
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