虚拟筛选
化学
药物发现
高通量筛选
锡尔图因
计算生物学
小分子
体外
化学图书馆
渲染(计算机图形)
组合化学
计算机科学
生物化学
酶
生物
人工智能
NAD+激酶
作者
Anastasiia Gryniukova,Florian Kaiser,Iryna Myziuk,Diana Alieksieieva,Christoph Leberecht,Peter Paul Heym,Olga O. Tarkhanova,Yurii S. Moroz,Petro Borysko,V. Joachim Haupt
标识
DOI:10.1021/acs.jmedchem.3c00128
摘要
The discovery of new scaffolds and chemotypes via high-throughput screening is tedious and resource intensive. Yet, there are millions of small molecules commercially available, rendering comprehensive in vitro tests intractable. We show how smart algorithms reduce large screening collections to target-specific sets of just a few hundred small molecules, allowing for a much faster and more cost-effective hit discovery process. We showcase the application of this virtual screening strategy by preselecting 434 compounds for Sirtuin-1 inhibition from a library of 2.6 million compounds, corresponding to 0.02% of the original library. Multistage in vitro validation ultimately confirmed nine chemically novel inhibitors. When compared to a competitive benchmark study for Sirtuin-1, our method shows a 12-fold higher hit rate. The results demonstrate how AI-driven preselection from large screening libraries allows for a massive reduction in the number of small molecules to be tested in vitro while still retaining a large number of hits.
科研通智能强力驱动
Strongly Powered by AbleSci AI