CaliciBoost: Performance-driven evaluation of molecular representations for caco-2 permeability prediction.

ABSTRACT: Caco-2= permeability serves as a critical in vitro indicator for predicting the oral absorption of drug candidates= during early-stage drug discovery. To enhance the accuracy and= efficiency of computational predictions, we systematically investigated the impact of eight molecular feature= representation types including 2D/3D descriptors, structural fingerprints, and deep learning-based embeddings combined with automated machine learning techniques to predict Caco-2 permeability. We evaluated model performance across various molecular representations using two datasets differing in scale and chemical diversity, namely the TDC benchmark and curated OCHEM data. Among the tested fingerprints and descriptors, PaDEL, Mordred, and RDKit emerged as particularly effective for predicting Caco-2 per

SUBMITTER: Le HV 

PROVIDER: S-EPMC12752011 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature