Leveraging Language Model Multitasking To Predict C-H Borylation Selectivity.
J Chem Inf Model
; 64(10): 4286-4297, 2024 May 27.
Article
en En
| MEDLINE
| ID: mdl-38708520
ABSTRACT
C-H borylation is a high-value transformation in the synthesis of lead candidates for the pharmaceutical industry because a wide array of downstream coupling reactions is available. However, predicting its regioselectivity, especially in drug-like molecules that may contain multiple heterocycles, is not a trivial task. Using a data set of borylation reactions from Reaxys, we explored how a language model originally trained on USPTO_500_MT, a broad-scope set of patent data, can be used to predict the C-H borylation reaction product in different modes product generation and site reactivity classification. Our fine-tuned T5Chem multitask language model can generate the correct product in 79% of cases. It can also classify the reactive aromatic C-H bonds with 95% accuracy and 88% positive predictive value, exceeding purpose-developed graph-based neural networks.
Texto completo:
1
Bases de datos:
MEDLINE
Asunto principal:
Hidrógeno
Idioma:
En
Revista:
J Chem Inf Model
Asunto de la revista:
INFORMATICA MEDICA
/
QUIMICA
Año:
2024
Tipo del documento:
Article
País de afiliación:
Reino Unido