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Aprendizaje del uso terapéutico de fármacos a partir de la información espacial tridimensional de su estructura molecular con redes neuronales convolucionales
Learning the therapeutic use of drugs from the three-dimensional spatial information of their molecular structure with convolutional neural networks
DOI:
https://doi.org/10.15446/dyna.v88n219.92778Palabras clave:
Redes neuronales convolucionales, usos terapéuticos, Moléculas, vistas 3D, Fármacos (es)convolutional neural networks;, drugs;, therapeutic use;, 3D views;, molecules (en)
El desarrollo de nuevas moléculas es un proceso que requiere de múltiples etapas y los ensayos clínicos para verificar su eficacia cuesta miles de millones de dólares cada año. El aprendizaje automático es una herramienta que está avanzando rápidamente en el reconocimiento de imágenes, voz y texto, y trabajar In silico aumentaría la capacidad de predecir y priorizar la función de un medicamento. En esta investigación nos preguntamos si la función de los medicamentos de uso terapéutico se puede predecir a partir de la configuración estereoquímica de la molécula. Nosotros usamos redes neuronales convolucionales para predecir el uso terapéutico de fármacos, entrenadas tanto con información bidimensional como con información tridimensional de su estructura química. El modelo entrenado solamente con seis vistas de la información 3D de la estructura molecular mejoró la exactitud en un 10 respecto al modelo entrenado con la información 2D.
The development of new molecules is a multi-stage process and clinical trials to verify their efficacy cost billions of dollars each year. Machine learning is a tool that is rapidly advancing in image, voice, and text recognition, and working in silico would increase the ability to predict and prioritize a drug's function. In this research we asked whether the function of therapeutic drugs can be predicted from the stereochemical configuration of the molecule. We use convolutional neural networks to predict the therapeutic use of drugs, trained with both two-dimensional and three-dimensional information of their chemical structure. The model trained with only six views of the 3D information of the molecular structure improved the accuracy by 10 over the model trained with the 2D information.
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