Authors: Yinkai Wang, Xiaohui Chen, Liping Liu, Soha Hassoun
Abstract: The annotation (assigning structural chemical identities) of MS/MS spectra
remains a significant challenge due to the enormous molecular diversity in
biological samples and the limited scope of reference databases. Currently, the
vast majority of spectral measurements remain in the “dark chemical space”
without structural annotations. To improve annotation, we propose MADGEN
(Mass-spec Attends to De Novo Molecular GENeration), a scaffold-based method
for de novo molecular structure generation guided by mass spectrometry data.
MADGEN operates in two stages: scaffold retrieval and spectra-conditioned
molecular generation starting with the scaffold. In the first stage, given an
MS/MS spectrum, we formulate scaffold retrieval as a ranking problem and employ
contrastive learning to align mass spectra with candidate molecular scaffolds.
In the second stage, starting from the retrieved scaffold, we employ the MS/MS
spectrum to guide an attention-based generative model to generate the final
molecule. Our approach constrains the molecular generation search space,
reducing its complexity and improving generation accuracy. We evaluate MADGEN
on three datasets (NIST23, CANOPUS, and MassSpecGym) and evaluate MADGEN’s
performance with a predictive scaffold retriever and with an oracle retriever.
We demonstrate the effectiveness of using attention to integrate spectral
information throughout the generation process to achieve strong results with
the oracle retriever.
Source: http://arxiv.org/abs/2501.01950v1
