Cieślak Marcin, Łęski Jan, Krzysztyńska-Kuleta Olga, Kalinowska-Tłuścik Justyna, Danel Tomasz
Chemistry Department, Selvita, Kraków 30-394, Poland.
Doctoral School of Exact and Natural Sciences, Jagiellonian University, Kraków 30-348, Poland.
J Chem Inf Model. 2025 Aug 11;65(15):7811-7816. doi: 10.1021/acs.jcim.5c00720. Epub 2025 Jul 29.
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.
药物发现是一个漫长且资源密集的过程，需要创新的计算技术来加速从实验室研究到挽救生命药物的转化。在此，我们介绍Qsarna，这是一个综合在线平台，它将用于活性预测的机器学习与传统分子对接相结合，以简化虚拟筛选工作流程。我们的平台采用基于片段的生成模型，能够探索具有所需药效团特征的新型化学空间。用户可以与他人共享结果，并且可以在平台内直接检查对接姿势。在我们的案例研究中，我们成功鉴定出三种对单胺氧化酶B具有纳摩尔效力的新命中物，随后通过实验测定得到证实。用户友好的网络界面所需的计算专业知识极少，使科学家无论其主要研究领域如何都能进行先进的虚拟筛选。Qsarna通过无缝整合互补的计算机模拟方法并使先进的虚拟筛选技术普及，代表了计算药物发现的重大进步。
