ArticleDigital discovery2026
Evaluating 2D and 3D graph encoders for infrared-based organic structure verification.
Article in Digital discovery, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
4 authors.
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Abstract
Infrared (IR) spectrum prediction is typically judged by global spectral error, but the practical task in analytical chemistry is structure verification: given a spectrum, can predicted spectra rank the correct structure highly among many candidates? We train Phase-Aware Spectrum Generative Network (PASGeN) models to predict experimental IR spectra and evaluate their predictions using both spectral similarity and retrieval metrics. Using 13 568 experimental IR spectra in various phases, we train models with a 2D message-passing neural network (MPNN) and a 3D equivariant encoder (PaiNN), and fine-tune the 3D model on inexpensive force-field geometries to reduce inference cost. Across large-scale retrieval tests, the 3D encoder significantly outperforms the 2D baseline (73.9%
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.