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基于强化学习的特定目标De Novo 循环结合剂的设计

Fanhao Wang¹, Tiantian Zhang², Jintao Zhu¹

¹Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China.

Journal of medicinal chemistry

|August 18, 2025

概括

CYC_BUILDER是一个新的计算工具,用于设计难以达到治疗目标的循环. 这种强化学习框架成功生成了TNFα的强有力的循环结合剂,其中四种具有显著的结合和细胞活性.

科学领域:

计算化学是一种计算化学.
药物发现药物发现
类治疗药物类治疗药物

背景情况:

循环对诸如蛋白质与蛋白质相互作用等具有挑战性的标显示出治疗前景.
循环结合剂的计算设计仍然是药物发现的重大障碍.

研究的目的:

介绍CYC_BUILDER,一个用于设计循环的新型强化学习框架.
评估CYC_BUILDER在再生已知的循环蛋白复合体和设计新型结合剂方面的表现.

主要方法:

CYC_BUILDER使用强化学习和蒙特卡罗树搜索来进行碎片组装和循环 (胺基或二硫化键).
该框架指导碎片选择,生长和结构改进.
性能与现有的方法比较,例如AfCycDesign和Anchor Extension.

主要成果:

CYC_BUILDER成功地再生了已知的循环-蛋白质复合体的原生结合序列和姿势.
与其他方法相比,为TNFα设计的循环结合剂显示出更高的结合能,结构多样性和效率.
实验测试证实了9种设计的中的4种具有强烈的结合和细胞活性.

结论:

CYC_BUILDER提供了一种有效的计算工具,用于发现循环.
该框架在治疗开发和合成生物学中具有广泛的潜在应用.

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