Abstract
In small molecule identification from tandem mass (MS/MS) spectra, input–output kernel regression (IOKR) currently provides the state-of-the-art combination of fast training and prediction and high identification rates. The IOKR approach can be simply understood as predicting a fingerprint vector from the MS/MS spectrum of the unknown molecule, and solving a pre-image problem to find the molecule with the most similar fingerprint. In this paper, we bring forward the following improvements to the IOKR framework: firstly, we formulate the IOKRreverse model that can be understood as mapping molecular structures into the MS/MS feature space and solving a pre-image problem to find the molecule whose predicted spectrum is the closest to the input MS/MS spectrum. Secondly, we introduce an approach to combine several IOKR and IOKRreverse models computed from different input and output kernels, called IOKRfusion. The method is based on minimizing structured Hinge loss of the combined model using a mini-batch stochastic subgradient optimization. Our experiments show a consistent improvement of top-k accuracy both in positive and negative ionization mode data.
Highlights
In recent years, the massively increased amounts of publicly available reference tandem mass (MS/MS) spectra in databases such as GNPS [1] and MassBank [2] have caused a revolution in small molecule identification
The spectra correspond to LC-MS/MS data measured with Quadrupole-Time of Flight (Q-ToF), Orbitrap, Fourier Transform Ion Cyclotron Resonance (FTICR) and ion trap instruments
We report on the predictive performance of different aggregated models, including the early fusion multiple kernel learning (MKL) approaches and the proposed IOKRfusion approach relying on late fusion
Summary
The massively increased amounts of publicly available reference tandem mass (MS/MS) spectra in databases such as GNPS [1] and MassBank [2] have caused a revolution in small molecule identification. The majority of the machine learning methods rely on the same conceptual scheme [3] introduced with FingerID [4]: predicting molecular fingerprints from MS/MS data and finding the most similar fingerprint from the molecular structure database. This approach has been very successful, for example, CSI:FingerID [8] and CSI:IOKR [9] have been top performers in the most recent CASMI contests (2016: [16] and 2017: [17]). The alternative conceptual approach for small molecule identification, sometimes called in silico fragmentation [3], calls for predicting MS/MS spectra for a set of candidate molecular structures and choosing the most similar predicted MS/MS spectrum to the observed
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