通过整合物理定律和几何知识来进行强大的蛋白质-连接体相互作用建模,以实现绝对约束的自由能量计算
Qun Su1, Jike Wang1, Qiaolin Gou1
1College of Pharmaceutical Sciences, Zhejiang University Hangzhou 310058 Zhejiang China tingjunhou@zju.edu.cn kimhsieh@zju.edu.cn.
Chemical science
|February 26, 2025
概括
一个新的框架LumiNet,通过结合物理和深度学习,准确地预测蛋白质-连接体结合的自由能量. 这种可解释的AI模型可以以更高的准确性和效率加速药物发现.
科学领域:
- 计算化学是一种计算化学.
- 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:
- 在药物化学中,准确的蛋白质 - 配体 (PL) 结合自由能量估计至关重要.
- 传统的计算方法往往很慢,容易出错.
- 深度学习 (DL) 方法面临着数据稀缺性和通用性的挑战.
研究的目的:
- 开发LumiNet,这是一个用于蛋白质 - 配体相互作用建模的多功能框架.
- 为了弥合基于物理的模型和黑子算法之间的差距.
- 为了提高准确的绝对约束自由能量 (ABFE) 计算,并提供可解释的见解.
主要方法:
- 使用子图形变压器从分子图形中提取多尺度信息.
- 采用几何神经网络来整合PL信息并将原子结构映射到物理参数.
- 实施半监督学习策略,以适应有限数据的新目标.
主要成果:
- 在PDE10A数据集上,LumiNet的性能比最先进的DL模型高18.5%.
- 实现与FEP+相当的性能,计算速度明显更快 (数量级).
- 成功引导了脚手架跳跃,以实现最佳的连接体发现.
结论:
- 路米网提供了一个准确的,可解释的,高效的方法来对PL有约束力的自由能源预测.
- 半监督策略增强了在现实世界药物发现场景中的适用性,数据有限.
- 通过可视化分子间能量贡献和社区使用的网络服务提供实用价值.
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