概括
我们开发了Frag2Seq,这是一种使用语言模型 (LMs) 进行基于结构的药物设计 (SBDD) 的新方法. 这种方法产生具有高结合亲和力和采样效率的药物样分子.
科学领域:
- 计算化学是一种计算化学.
- 药品化学 药品化学 是一个
- 人工智能在药物发现中的作用
背景情况:
- 基于结构的药物设计 (SBDD) 对向治疗至关重要,但在建模复杂的蛋白质 - 连接体相互作用和探索广的化学空间方面面临挑战.
- 在自然语言处理中成功的语言模型 (LMs) 在SBDD应用中得到了极小的探索.
研究的目的:
- 引入Frag2Seq,一种新的方法,将LMs应用于SBDD,以实现高效和有效的分子生成.
- 通过将蛋白质口袋信息纳入LMs来增强目标感知分子生成.
主要方法:
- Frag2Seq使用SE(3) -等价将3D分子转化为碎片信息序列,并保留几何数据.
- 来自反向折叠模型的蛋白质口袋嵌入被通过交叉注意力集成到LMS中以捕捉蛋白质-连接体相互作用.
- 该方法利用基于片段的生成和蛋白质上下文编码来设计分子.
主要成果:
- Frag2Seq在结合亲和力 (vina评分) 和药物相似性 (QED,Lipinski规则) 中取得了卓越的表现.
- 该模型在产生具有增强与标蛋白结合亲和力的配体方面表现出有效性.
- 与现有的自回归和扩散模型相比,Frag2Seq提供了显著更高的采样效率,速度可达300倍.
结论:
- Frag2Seq通过将基于片段的生成与蛋白质上下文相结合,有效地利用SBDD的LM.
- 该方法有望加速发现特定和有效的候选药物.
- Frag2Seq代表了药物发现计算方法的重大进步,提供了准确性和速度.
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