Avances recientes en el descubrimiento y desarrollo de fármacos a través del aprendizaje automático: un diseño racional de medicamentos (2000-2025)
Resumen
El proceso tradicional de descubrimiento de fármacos –dificultado a menudo por los altos costos y largos periodos de desarrollo– ha tenido una transformación profunda por el aprendizaje automatizado (Machine Learning, ML). El presente trabajo resalta los mayores logros desde el año 2000 hasta 2024, ilustrando cómo el ML ha revolucionado el diseño y uso racional de los medicamentos, desarrollando predicciones precisas de propiedades farmacológicas desde el enlace objetivo de toxicidad clínica. Mediante un análisis de QSAR y literatura sobre diseños farmacológicos desde asistentes computacionales (CADD), se demuestra que los modelos de aprendizaje automático ML logran significantemente una exactitud más alta en la predicción de actividad biológica (ΔAUC > 0.25 comparada con métodos tradicionales), perfiles de ADMET, y efectos colaterales. Estos avances son el eje de innovaciones, tales como redes neutrales basados en la atención e informes físicos de modelos generativos. Sin embargo, diferentes retos persisten en el traslado clínico: (1) modelos de caja negra con interoperabilidad limitada que dificulta la aprobación regulatoria; (2) formación de datos parcial que perpetúa imprecisiones en la predicción poli-farmacológica; y (3) sobreajuste de las bases de datos sintéticas que genera fallas en la captura de la verdadera complejidad biológica. Los casos de estudio analizados revelan que el ML-predice candidatos que, ocasionalmente, defraudan in vivo debido a la simplificación de las hipótesis sobre las dinámicas de proteína-ligando. A pesar de dichas limitaciones, la trayectoria en general permanece bastante prometedora. Metodologías emergentes –como el aprendizaje federal para compartir datos multi-institucionales, computación cuántica hibrida de ML para moleculas dinámicas y embeddings mecanismos– están generando una interrupción entre predicciones in silico y resultados experimentales. Teniendo en cuenta esos avances, no solo se acelerará el descubrimiento de farmacos, sino que también se redefinirá. Las pruebas experimentales recientes reportan un 40% de la reducción en las tasas de fallas pre-clínicas y están comenzando a emerger medicamentos de primera clase, incluyendo los inebidores de quinasas diseñados por ML. Por ultimo, este articulo argumenta que el mejor valor de los ML recide en el empoderamiento de quimicos medicos con decisiones basadas en herramientas que intengran la escalabilidad computaciona y la exactitud bioquímica –marcando una nueva era en la diseño de farmacos de forma precisa.
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Datos de los fondos
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Universidad El Bosque
Números de la subvención PCI 2024-0011
Derechos de autor 2025 Revista Facultad de Ciencias Básicas
Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-SinDerivadas 4.0.
