ArticleComputational and structural biotechnology journal2020
Introducing the CSP Analyzer: A novel Machine Learning-based application for automated analysis of two-dimensional NMR spectra in NMR fragment-based screening.
Article in Computational and structural biotechnology journal, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Harmonizing Peak Matching between Multidimensional NMR Spectra.Journal of chemical information and modeling · 2026Article
- The Bifunctional Effects of Lactoferrin (LFcinB11) in Inhibiting Neural Cell Adhesive Molecule (NCAM) Polysialylation and the Release of Neutrophil Extracellular Traps (NETs).International journal of molecular sciences · 2024Article
- Fully Automated Characterization of Protein-Peptide Binding by Microfluidic 2D NMR.Journal of the American Chemical Society · 2023Article
- Fragment-to-Lead Medicinal Chemistry Publications in 2020.Journal of medicinal chemistry · 2022Review
- Automated Determination of Nuclear Magnetic Resonance Chemical Shift Perturbations in Ligand Screening Experiments: The PICASSO Web Server.Journal of chemical information and modeling · 2021Article
- Latency, thermal stability, and identification of an inhibitory compound of mirolysin, a secretory protease of the human periodontopathogenJournal of enzyme inhibition and medicinal chemistry · 2021Article
- Applications of Solution NMR in Drug Discovery.Molecules (Basel, Switzerland) · 2021Review
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Authors and funding
6 authors.
Funding
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Abstract
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.
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