FFLOM:基于流量的自回归模型,用于fragment-to-lead优化.
Jieyu Jin1, Dong Wang1, Guqin Shi2
1Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China.
Journal of medicinal chemistry
|July 20, 2023
概括
一种新的深度学习模型,FFLOM,在设计药物分子方面表现出色,特别是在有限数据的基于片段的药物设计 (FBDD) 中. 它在生成有效,新和准确的分子结构方面展示了最先进的性能.
科学领域:
- 计算化学计算化学
- 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:
- 深度生成模型显示了基于碎片的药物设计 (FBDD) 的前景.
- 目前的模型难以产生具有特定性质的分子,特别是有限的数据.
- 设计链接器和R组对于优化候选药物至关重要.
研究的目的:
- 引入FFLOM,一种基于流量的新型自回归模型,用于链接器和R组设计.
- 评估FFLOM在产生具有所需性质的分子方面的表现.
- 评估FFLOM在实际药物设计场景中的适用性.
主要方法:
- 基于流量的自回归模型FFLOM的开发.
- 使用ZINC,CASF和PDBbind数据集进行大规模基准评估.
- 评估分子生成指标:有效性,独特性,新性和恢复性.
主要成果:
- 在基准数据集中,FFLOM实现了最先进的性能.
- 该模型显示了高分子回收率 (在PDBbind上超过92%).
- FFLOM成功重建了基本真相化合物,并产生了具有潜在更高结合亲和力的新片段.
结论:
- FFLOM是基于片段的药物设计的强大工具,特别是在低数据的系统中.
- 该模型显示了实践应用的巨大潜力,如碎片链接和PROTAC设计.
- FFLOM能够产生新的分子碎片,推动药物发现工作.
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