PPGB-MS2 is a tool for predicting the MS/MS of small molecules.
A comparison was made between PPGB-MS2 and two commonly used high-performance MS/MS prediction tools, namely 3DMolMS and CFM-ID. The following is the relevant introduction of 3DMolMS and CFM-ID.
3D Molecular Network for Mass Spectra Prediction (3DMolMS) is a deep neural network model to predict the MS/MS spectra of compounds from their 3D conformations. This model's molecular representation, learned through MS/MS prediction tasks, can be further applied to enhance performance in other molecular-related tasks, such as predicting retention times (RT) and collision cross sections (CCS).
Read paper in Bioinformatics | Try online service at GNPS | Try model on Konia | Install from PyPI
🆕 3DMolMS v1.1.10 is now available for inference on Konia, GNPS, and PyPI!
Please cite: Yuhui Hong, Sujun Li, Christopher J Welch, Shane Tichy, Yuzhen Ye, Haixu Tang, 3DMolMS: prediction of tandem mass spectra from 3D molecular conformations, Bioinformatics, Volume 39, Issue 6, June 2023, btad354, https://doi.org/10.1093/bioinformatics/btad354
CFM-ID is a widely used high-performance MS/MS prediction tool. It also has a web server available for use. Currently, it has been updated to version 4.0. CFM-ID 4.0 incorporates a more robust machine learning process that utilizes a novel tensor representation to describe the topology of chemical structures. It also adopts a new approach for dealing with ring cleavage and new rule-based methods, which improve the MS/MS spectral prediction for specific classes of chemicals where the machine-learned model performs poorly.
Read paper in Analytical Chemistry | Read paper in Nucleic Acids Research
Please cite:
Wang F, Allen D, Tian S, Oler E, Gautam V, Greiner R, Metz TO, and Wishart DS. (2022) CFM-ID 4.0–a web server for accurate MS-based metabolite identification. Nucleic Acids Research 50 (W1), W165-W174.
Wang F, Liigand J, Tian S, Arndt D, Greiner R, and Wishart D. (2021) CFM-ID 4.0: More Accurate ESI MS/MS Spectral Prediction and Compound Identification. Anal Chem. 93(34):11692-11700.
Djoumbou-Feunang Y, Pon A, Karu N, Zheng J, Li C, Arndt D, Gautam M, Allen F, and Wishart DS. (2019) Significantly Improved ESI-MS/MS Prediction and Compound Identification. Metabolites. 9(4):72.
Allen F, Greiner R, and Wishart DS (2016) Computational prediction of electron ionization mass spectra to assist in GC-MS compound identification. Anal Chem. 88(15):7689-97. Supporting Data
Allen F, Greiner R, and Wishart D (2015) Competitive fragmentation modeling of ESI-MS/MS spectra for putative metabolite identification. Metabolomics. 11:98–110. Supporting Data
Allen F, Pon A, Wilson M, Greiner R, and Wishart DS (2014). CFM-ID: a web server for annotation, spectrum prediction and metabolite identification from tandem mass spectra. Nucleic Acids Res. 42(Web Server issue):W94-9.