SIBILA
Automated, interpretable machine learning on HPC platforms.
About
SIBILA is an automated machine-learning (AutoML) platform focused on building interpretable predictive models. It handles the full pipeline — preprocessing, model selection, hyperparameter tuning and explanation — and runs natively on HPC infrastructure.
Applications span ADMET prediction, cardiovascular risk modelling, hospital-readmission prediction, drought monitoring, and other tabular forecasting tasks.
Lead authors: Antonio J. Banegas-Luna
Key features
AutoML on HPC
Automated model search optimised for supercomputing platforms.
Interpretability first
Feature attribution and explanation alongside predictions, not as an afterthought.
Multi-domain
Used for ADMET, clinical risk, environmental and other tabular problems.
Access
HPC suite
Source code on GitHub
Open source · private SaaS deployment on demand
https://github.com/bio-hpc/sibila →Cite this tool
SIBILA: Automated Machine-Learning-Based Development of Interpretable Machine-Learning Models on High-Performance Computing Platforms