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Arxiv
4d
195
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CaliciBoost: Performance-Driven Evaluation of Molecular Representations for Caco-2 Permeability Prediction
- Caco-2 permeability prediction is crucial for drug absorption in early-stage drug discovery.
- Study analyzed the impact of different molecular feature representation types on Caco-2 permeability prediction.
- PaDEL, Mordred, and RDKit descriptors found to be effective for Caco-2 prediction.
- CaliciBoost model, based on AutoML, achieved the best Mean Absolute Error (MAE) performance.
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