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Revelación de los límites del acoplamiento covalente y avance en la predicción de afinidad con aprendizaje multitarea
Jiayang Leng1,2, Zhixuan Huang1,2, Lei Zheng3,4
1Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, Guangdong, China.
Physical chemistry chemical physics : PCCP
|February 4, 2026
Resumen
Las herramientas computacionales para inhibidores covalentes dirigidos (TCI) están subdesarrolladas. Este estudio presenta un nuevo marco, CovMTL-DTA, que mejora significativamente la predicción de la afinidad fármaco-diana y la priorización de aciertos para los TCI.
Sus antecedentes:
- Los inhibidores covalentes dirigidos (TCI) son cruciales en el descubrimiento de fármacos, pero la predicción computacional de sus poses de unión y afinidad sigue siendo un desafío.
- Las herramientas computacionales existentes a menudo carecen de especificidad para las interacciones covalentes, lo que dificulta la predicción precisa de la afinidad fármaco-diana (DTA).
Conclusiones:
- Las herramientas actuales de acoplamiento covalente tienen limitaciones en la predicción de poses y la clasificación de afinidad.
- CovMTL-DTA ofrece un avance significativo en la predicción de la afinidad fármaco-diana para los TCI.
- El marco desarrollado mejora la priorización de aciertos y puede acelerar los esfuerzos de descubrimiento de fármacos covalentes.
Palabras clave:
inhibidores covalentes dirigidospredicción de afinidad fármaco-dianaaprendizaje multitareaacoplamiento moleculardescubrimiento de fármacosMás Videos Relacionados
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