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3DSMILES-GPT:基于3D分子口袋生成,仅使用代币的大型语言模型
Jike Wang1, Hao Luo1, Rui Qin1
1College of Pharmaceutical Sciences, Zhejiang University Hangzhou 310058 Zhejiang China yukang@zju.edu.cn tingjunhou@zju.edu.cn kimhsieh@zju.edu.cn.
Chemical science
|December 12, 2024
概括
3DSMILES-GPT是一种新型语言模型,用于药物发现生成3D分子,克服现有方法的局限性. 它迅速产生高质量,有效和可合成的分子,具有更好的药物相似性和结合亲和力.
科学领域:
- 计算化学和化学信息学
- 人工智能在药物发现中的作用
背景情况:
- 产生有效和高质量的3D分子对于药物发现至关重要,但面临着现有的计算方法的挑战.
- 目前的方法往往会产生具有无效配置,不良药物特性和缓慢生成时间的分子.
研究的目的:
- 推出3DSMILES-GPT,一种基于语言模型的框架,用于高效和准确的3D分子生成.
- 为了解决分子有效性,构型,药物相似性,合成性和生成速度的局限性.
主要方法:
- 在2D和3D分子表示中使用了仅用于令牌的语言模型方法.
- 在数以百万计的类似药物的分子上预先训练模型,然后通过蛋白质口袋/分子结构数据进行微调.
- 采用强化学习来优化生物物理和化学特性.
主要成果:
- 3DSMILES-GPT在结合亲和力,药物相似性 (QED) 和合成可访问性得分 (SAS) 中明显优于现有方法.
- 在保持最先进的结合 afinity 的同时,在药物相似性的定量估计 (QED) 中实现了33%的增强.
- 证明了显著的生成速度,平均每分子0.45秒,比以前的方法增加了三倍.
结论:
- 3DSMILES-GPT为药物发现中的3D分子生成提供了强大而高效的解决方案.
- 基于代币的语言模型驱动的框架有可能加速识别新型候选药物.
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