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Mol-PECO: a deep learning model to predict human olfactory perception from molecular structures
While visual and auditory information conveyed by wavelength of light and frequency of sound have been decoded, predicting olfactory information encoded by the combination of odorants remains challenging due to the unknown and potentially discontinuous perceptual space of smells and odorants.
MS$^2$-Transformer: An End-to-End Model for MS/MS-assisted Molecule Identification
Manually interpreting the MS/MS spectrum into the molecules (i. e., the simplified molecular-input line-entry system, SMILES) is often costly and cumbersome, mainly due to the synthesis and labeling of isotopes and the requirement of expert knowledge.
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