试点:通过重要抽样进行多目标指导的口袋调节的de novo配体生成等效扩散
Julian Cremer1,2, Tuan Le1,3, Frank Noé3,4
1Machine Learning & Computational Sciences, Pfizer Worldwide R&D Berlin Germany julian.cremer@pfizer.com tuan.le@pfizer.com.
Chemical science
|August 30, 2024
概括
我们开发了PILOT,这是一种用于 de novo 3D 联体生成的 in silico 方法. 这种方法优化了药物发现的分子结合亲和力和合成可访问性.
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
- 药物发现 药物发现
背景情况:
- 基于结构的药物设计面临的挑战是产生具有针对蛋白质口袋量身定制的特定性质的配体.
- 开发具有高结合亲和力和合成可访问性的新型分子对于高效的药物发现管道至关重要.
研究的目的:
- 推出PILOT,一个用于*de novo* 3D联体生成的等价扩散模型.
- 为了将口袋调节与大规模的预训练和属性指导相结合,以优化连接体设计.
- 通过确保合成可访问性来增强用于药物发现的生成分子的实用性.
主要方法:
- 使用等效扩散模型 (PILOT) 进行*in silico*连接物生成.
- 实施了基于多目标轨迹的重要性抽样策略.
- 集成的口袋调节,大规模的预培训和财产指导.
主要成果:
- 与CrossDocked2020基准数据集的现有方法相比,PILOT表现优越.
- 从Kinodata-3D数据集中生成了以前未见的蛋白质口袋的新联体.
- 产生的配体的预测IC50值表明强烈的生物活性.
结论:
- 飞行员是一个强大的工具,用于*de novo*基于结构的药物设计.
- 该模型有效地产生具有所需结合亲和力和合成可访问性的配体.
- 试点显示了加速药物发现过程的巨大潜力.
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