Qsarna: An Online Tool for Smart Chemical Space Navigation in Drug Design

Drug discovery is a lengthy and resource-intensive process
that
requires innovative computational techniques to expedite the transition
from laboratory research to life-saving medications. Here, we introduce
Qsarna, a comprehensive online platform that combines machine learning
for activity prediction with traditional molecular docking to streamline
virtual screening workflows. Our platform employs a fragment-based
generative model, enabling the exploration of novel chemical spaces
with the desired pharmacophoric features. Users can share results
with others, and docking poses can be examined directly within the
platform. In our case study, we successfully identified three new
hits for monoamine oxidase B with nanomolar potency, which were later
confirmed by experimental assays. The user-friendly web interface
requires minimal computational expertise, making advanced virtual
screening accessible to scientists regardless of their main field
of study. Qsarna represents a significant advancement in computational
drug discovery by seamlessly integrating complementary in silico approaches
and democratizing access to advanced virtual screening technologies.

药物研发是一项耗时漫长且资源消耗巨大的工作，亟需创新性计算技术来加速从实验室研究到临床救命药物的转化进程。在此我们推出Qsarna，一款整合了活性预测机器学习技术与传统分子对接（molecular docking）技术的综合性在线平台，旨在优化虚拟筛选（virtual screening）工作流程。本平台采用基于片段的生成模型，可支持探索具备目标药效团（pharmacophore）特征的新型化学空间。用户可与他人共享分析结果，且可直接在平台内查看对接构象。在本研究的案例分析中，我们成功筛选出3个对单胺氧化酶B（monoamine oxidase B）具有纳摩尔级活性的新型命中化合物，后续经实验检测得以证实。该平台界面友好易用，仅需极低的计算领域专业知识门槛，可让不同研究背景的科研人员均可便捷使用先进的虚拟筛选技术。Qsarna通过无缝整合互补的计算机模拟（in silico）研究方法，实现了先进虚拟筛选技术的普惠共享，代表了计算药物研发领域的一项重大进展。