一种药理学指导的深度学习方法,用于生物活性分子生成
Huimin Zhu1, Renyi Zhou1, Dongsheng Cao2
1School of Computer Science and Engineering, Central South University, Changsha, 410083, China.
Nature communications
|October 6, 2023
概括
这项研究引入了一种药理学指导的深度学习方法,用于生物活性分子生成 (PGMG) 来设计新药候选药物. PGMG有效地产生具有高结合亲和力和新性的分子,加速药物发现.
科学领域:
- 药用化学 医学化学
- 计算化学计算化学
- 药物发现 药物发现 药物发现
背景情况:
- 对新生物活性进行合理的分子设计具有挑战性,特别是在研究不足的目标上.
- 现有的方法往往缺乏灵活性,并且难以产生多样化,高效的分子.
研究的目的:
- 开发一种新的深度学习方法,即用于生物活性分子生成 (PGMG) 的药物导向深度学习,以加速药物发现.
- 增强具有理想性质的生物活性分子的生成,包括高对接亲和度和新性.
主要方法:
- PGMG使用图形神经网络来编码化学特征,并使用变压器解码器来生成分子.
- 纳入了一个潜在变量来管理药和分子之间的复杂映射,改善多样性.
- 该方法整合了用于灵活和有针对性的分子设计的药理学指南.
主要成果:
- 与现有方法相比,PGMG在产生具有强烈对接亲和性的分子方面表现出卓越的性能.
- 产生的分子在有效性,独特性和新性方面表现出高分.
- 基于联体和基于结构的新药设计的案例研究验证了PGMG的有效性.
结论:
- PGMG提供了一种灵活有效的计算工具,可以加速药物发现过程.
- 该方法成功地解决了设计新型生物活性分子用于新型和未研究的目标的挑战.
- PGMG能够产生多样化和强大的分子的能力使其对药物化学和药物开发具有价值.
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