深度学习与几何增强分子表示用于扩大大规模的基于对接的虚拟选
Lan Yu1, Xiao He2,3, Xiaomin Fang4
1School of Science, China Pharmaceutical University, Nanjing 210009, China.
Journal of chemical information and modeling
|October 26, 2023
概括
一个新的深度学习协议GEM-Screen通过使用几何增强分子表示来增强基于结构的虚拟选. 这种方法可以迅速从大量的化学库中识别出最高分的化合物,提高药物发现效率.
科学领域:
- 计算化学的计算化学
- 药物发现 药物发现 药物发现
- 机器学习 机器学习
背景情况:
- 基于结构的虚拟选 (SBVS) 在药物发现中至关重要,但由于高计算成本,它与超大化学图书馆作斗争.
- 现有的SBVS机器学习方法经常忽视关键的3D结构信息,通过将化合物表示为字符串或2D图表.
研究的目的:
- 开发一个新的深度学习协议,GEM-Screen,利用3D结构信息进行高效的虚拟选.
- 为了提高从数十亿条入口的化学图书馆中识别热点化合物的准确性和速度.
主要方法:
- 提出了GEM-Screen,这是一个深度学习协议,利用对接化合物的几何增强分子表示.
- 采用积极学习策略,在图书馆对接分数的很小一部分上训练模型,以预测未见的化合物的结果.
- 将GEM-Screen应用于针对AmpC和D4目标的虚拟选活动.
主要成果:
- 在预测的前4%内,GEM-Screen成功丰富了AmpC的90%以上的击中支架.
- 超过80%的D4的击中支架被确定在前4%的预测中.
- 证明了该协议能够显著减少需要传统对接的化合物数量.
结论:
- GEM-Screen提供了一种快速而准确的方法,可以从超大化学库中发现最高分的化合物.
- 该协议可以与传统的对接集成,以优化计算资源,避免详尽的图书馆选.
- 这种方法显著提高了基于结构的虚拟查在药物发现中的效率.
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