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Habilitación del descubrimiento de fármacos multiobjetivo a través de la optimización evolutiva latente y la
Viet Thanh Duy Nguyen1, Phuc Pham1, Truong-Son Hy2
1Department of Computer Science, The University of Alabama at Birmingham, Birmingham, AL, USA.
Communications chemistry
|February 15, 2026
Resumen
EVOSYNTH, un marco novedoso, genera fármacos multiobjetivo combinando la evolución latente y la priorización consciente de la síntesis. Este enfoque supera las limitaciones de las terapias de un solo objetivo y la polifarmacia tradicional para enfermedades complejas.
Área de la Ciencia:
- Descubrimiento de Fármacos
- Química Computacional
- Biología de Sistemas
Sus antecedentes:
- Las enfermedades complejas involucran vías interconectadas, lo que hace que las terapias de un solo objetivo sean insuficientes debido a la redundancia.
- La polifarmacia ofrece una solución pero enfrenta desafíos como interacciones medicamentosas y toxicidad.
Objetivo del estudio:
- Presentar EVOSYNTH, un marco modular para el descubrimiento de fármacos multiobjetivo.
- Mejorar la generación y priorización de candidatos a fármacos con alto potencial traslacional.
Principales métodos:
- Utilizar la evolución latente para navegar por un espacio latente para identificar candidatos multiobjetivo.
- Emplear la priorización consciente de la síntesis para evaluar la viabilidad retrosintética y la relación costo-beneficio.
Principales resultados:
- EVOSYNTH demostró un rendimiento superior en la doble inhibición de JNK3/GSK3β (Alzheimer) y PI3K/PARP1 (cáncer de ovario).
- Se logró una mayor afinidad predicha, una mayor diversidad de andamios y un menor costo de síntesis en comparación con los modelos de referencia.
Conclusiones:
- EVOSYNTH integra eficazmente la generación dirigida por objetivos con la sintetizabilidad práctica.
- Establece un marco escalable para el descubrimiento de fármacos multiobjetivo y polifarmacológicos.
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