A deep position-encoding model for predicting olfactory perception from molecular structures and electrostatics.
NPJ Syst Biol Appl
; 10(1): 76, 2024 Jul 17.
Article
en En
| MEDLINE
| ID: mdl-39019918
ABSTRACT
Predicting olfactory perceptions from odorant molecules is challenging due to the complex and potentially discontinuous nature of the perceptual space for smells. In this study, we introduce a deep learning model, Mol-PECO (Molecular Representation by Positional Encoding of Coulomb Matrix), designed to predict olfactory perceptions based on molecular structures and electrostatics. Mol-PECO learns the efficient embedding of molecules by utilizing the Coulomb matrix, which encodes atomic coordinates and charges, as an alternative of the adjacency matrix and its Laplacian eigenfunctions as positional encoding of atoms. With a comprehensive dataset of odor molecules and descriptors, Mol-PECO outperforms traditional machine learning methods using molecular fingerprints and graph neural networks based on adjacency matrices. The learned embeddings by Mol-PECO effectively capture the odor space, enabling global clustering of descriptors and local retrieval of similar odorants. This work contributes to a deeper understanding of the olfactory sense and its mechanisms.
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Percepción Olfatoria
/
Electricidad Estática
/
Odorantes
Límite:
Humans
Idioma:
En
Revista:
NPJ Syst Biol Appl
Año:
2024
Tipo del documento:
Article
País de afiliación:
China