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Técnicas de Inteligencia Artificial y Matemáticas en el Descubrimiento Moderno de Fármacos: Una Revisión

Akansha Agrwal¹, Rohit Kumar², Swati Maheshwari³

¹Department of Applied Sciences & Humanities, KIET Group of Institutions, Ghaziabad, India.

Drug development research

|December 23, 2025

Resumen

La inteligencia artificial (IA) y la modelización matemática están revolucionando el descubrimiento de fármacos, haciéndolo más rápido, más barato y más preciso. La integración de estas tecnologías en todas las etapas del desarrollo acelera la investigación y reduce los riesgos y costos de los ensayos clínicos.

Área de la Ciencia:

Investigación y desarrollo farmacéutico
Biología computacional
Química medicinal

Sus antecedentes:

El descubrimiento tradicional de fármacos es un proceso largo y costoso, que a menudo se basa en procesos manuales.
La inteligencia artificial (IA) y la modelización matemática ofrecen un potencial transformador para la industria farmacéutica.
La integración de enfoques de IA y matemáticos puede abordar los desafíos y desbloquear nuevas posibilidades en el desarrollo de fármacos.

Objetivo del estudio:

Revisar la literatura sobre IA y modelización matemática en el descubrimiento y desarrollo de fármacos.
Explorar la aplicación sinérgica de la IA y los marcos matemáticos en diversas etapas del desarrollo de fármacos.
Discutir los desafíos actuales, las herramientas disponibles, los conjuntos de datos y las tendencias futuras en el descubrimiento de fármacos impulsado por la IA.

Principales métodos:

Revisión de la literatura sobre IA y modelización matemática en el descubrimiento de fármacos.
Análisis de técnicas de IA que incluyen aprendizaje automático (ML), aprendizaje profundo (DL), aprendizaje por refuerzo (RL), procesamiento del lenguaje natural (NLP) y aprendizaje por transferencia (TL).
Examen de marcos matemáticos como álgebra lineal, optimización, modelado estadístico, teoría de grafos y ecuaciones diferenciales.

Principales resultados:

La IA acelera significativamente la fabricación de fármacos, reduce los costos y aumenta la especificidad.
Los enfoques combinados de IA y matemáticos pueden agilizar la investigación y mitigar los riesgos y costos en los ensayos clínicos.
La revisión destaca la integración de diversas técnicas de IA con marcos matemáticos para un desarrollo integral de fármacos.

Conclusiones:

La IA y las matemáticas son cruciales para el futuro de la investigación de fármacos, permitiendo tratamientos innovadores y efectivos.
La aplicación sinérgica de la IA y la modelización matemática agiliza el proceso de desarrollo de fármacos.
Los avances continuos en IA y la disponibilidad de datos prometen revolucionar aún más la I+D farmacéutica.

Palabras clave:

inteligencia artificial aprendizaje profundo descubrimiento de fármacos aprendizaje automático modelado matemático red neuronal

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