Simplified, interpretable graph convolutional neural networks for small molecule activity prediction

We here present a streamlined, explainable graph convolutional neural network (gCNN) architecture for small molecule activity prediction. We first conduct a hyperparameter optimization across nearly 800 protein targets that produces a simplified gCNN QSAR architecture, and we observe that such a mod...

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Detalles Bibliográficos
Autores principales: Weber, Jeffrey K., Morrone, Joseph A., Bagchi, Sugato, Pabon, Jan D. Estrada, Kang, Seung-gu, Zhang, Leili, Cornell, Wendy D.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9325818/
https://www.ncbi.nlm.nih.gov/pubmed/34817762
http://dx.doi.org/10.1007/s10822-021-00421-6

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9325818/
https://www.ncbi.nlm.nih.gov/pubmed/34817762
http://dx.doi.org/10.1007/s10822-021-00421-6

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