NMR-based screening, especially fragment-based drug discovery is a valuable approach in early-stage drug discovery. Monitoring fragment-binding in protein-detected 2D NMR experiments requires analysis of hundreds of spectra to detect chemical shift perturbations (CSPs) in the presence of ligands screened. Computational tools are available that simplify the tracking of CSPs in 2D NMR spectra. However, to the best of our knowledge, an efficient automated tool for the assessment and binning of multiple spectra for ligand binding has not yet been described. We present a novel and fast approach for analysis of multiple 2D HSQC spectra based on machine-learning-driven statistical discrimination. The CSP Analyzer features a C# frontend interfaced to a Python ML classifier. The software allows rapid evaluation of 2D screening data from large number of spectra, reducing user-introduced bias in the evaluation. The CSP Analyzer software package is available on GitHub https://github.com/rubbs14/CSP-Analyzer/releases/tag/v1.0 under the GPL license 3.0 and is free to use for academic and commercial uses.

基于NMR的筛选，尤其是基于片段的药物发现，是早期药物发现中的一种有价值的方法。在蛋白质检测的2D NMR实验中监测片段结合需要分析数百个光谱，以在存在筛选的配体的情况下检测化学位移扰动（CSP）。可以使用计算工具来简化2D NMR光谱中CSP的跟踪。然而，就我们所知，尚未描述一种用于评估和分装多个光谱以进行配体结合的有效自动化工具。我们提出了一种新颖且快速的方法，用于基于机器学习驱动的统计判别分析多个2D HSQC光谱。CSP分析器具有连接到Python ML分类器的C＃前端。该软件可以快速评估来自大量光谱的2D筛选数据，从而减少了用户引入的评估偏差。CSP Analyzer软件包可在GPL许可3.0下的GitHub https://github.com/rubbs14/CSP-Analyzer/releases/tag/v1.0上获得，可免费用于学术和商业用途。