BARTReact: SELFIES-driven precision in reaction modeling
Daniel Farfán,
Carolina Gómez-Márquez,
Dania Sandoval-Nuñez,
Omar Paredes,
J. Alejandro Morales
Affiliations
Daniel Farfán
Biodigital Innovation Lab, Centro Universitario de Ciencias Exactas e Ingenierías (CUCEI), Universidad de Guadalajara, Guadalajara, Mexico
Carolina Gómez-Márquez
Biodigital Innovation Lab, Centro Universitario de Ciencias Exactas e Ingenierías (CUCEI), Universidad de Guadalajara, Guadalajara, Mexico
Dania Sandoval-Nuñez
Biodigital Innovation Lab, Centro Universitario de Ciencias Exactas e Ingenierías (CUCEI), Universidad de Guadalajara, Guadalajara, Mexico
Omar Paredes
Biodigital Innovation Lab, Centro Universitario de Ciencias Exactas e Ingenierías (CUCEI), Universidad de Guadalajara, Guadalajara, Mexico
J. Alejandro Morales
Corresponding author.; Biodigital Innovation Lab, Centro Universitario de Ciencias Exactas e Ingenierías (CUCEI), Universidad de Guadalajara, Guadalajara, Mexico
We introduce Bidirectional and Auto-Regressive Transformer for Reactions (BARTReact), a self-supervised deep learning model designed to predict chemical reactions. Built on the powerful Bidirectional and Auto-Regressive Transformer (BART) architecture, BARTReact is trained using the SELF-referencIng Embedded Strings (SELFIES), a molecular representation that ensures the production of only viable molecules, achieving an outstanding accuracy of 98.6 %.
