在口袋中的3D图表增强了在生成小分子创造中的干-标兼容性:多巴胺D2受体模型系统模型
Seung-Gu Kang1,2, Jeffrey K Weber1, Joseph A Morrone1
1Computational Biology, IBM Thomas J. Watson Research Center, 1101 Kitchawan Road, Yorktown Heights, New York 10594, United States.
The journal of physical chemistry. B
|March 4, 2026
概括
本研究介绍了一种基于3D图形的药物发现生成模型. 它通过结合蛋白质-配体相互作用来增强小分子生成,以获得更好的目标兼容性.
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
- 结构生物学和分子建模.
背景情况:
- 基于结构的药物发现依赖于蛋白质连接体复合体,但3D信息通常在生成模型中缺失.
- 开发包含明确3D结构背景的生成模型对于改善药物设计至关重要.
- 多巴胺D2受体 (DD2R) 作为一个相关的模型系统,用于评估蛋白质-配体相互作用.
研究的目的:
- 介绍一种基于图形的新型生成建模技术,编码明确的3D蛋白质-连接体接触.
- 用多巴胺D2受体 (DD2R) 作为模型来评估这种3D生成方法的有效性.
- 为了证明如何结合结构上下文增强小分子生成在一个现实的结合环境.
主要方法:
- 开发了一个基于图形的生成模型,使用条件变化自编码器来生成特定活动分子.
- 集成的假定接触生成以预测目标结合口袋内的分子相互作用.
- 根据对接分数,立体化学和化学数据库中的可恢复性评估生成的分子.
主要成果:
- 使用3D方法生成的分子与2D方法相比,与DD2R结合口袋的兼容性更高.
- 在商业数据库中,3D方法产生了更好的对接分数,预期的立体化学和更高的可恢复性.
- 预测的蛋白质连接体接触经常是排名最高的对接姿势之一,复原率高.
结论:
- 拟议的基于3D图形的生成模型有效地编码蛋白质-连接体接触,以增强药物发现.
- 将蛋白质标的结构上下文纳入其中,可以显著改善用于现实的结合环境的小分子的生成.
- 这种方法为利用深度学习推进基于结构的药物设计提供了一个有希望的方向.
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