adme-prediction

You can predict ADME properties by inputting SMILES strings into Python or CLI interfaces. The tool converts them into Morgan fingerprints and uses Random Forest models to evaluate Caco-2, PAMPA, HIA, Pgp, bioavailability, and lipophilicity.

Yes, batch ADME predictions are supported via both CLI and Python API. You can process a library of SMILES strings to filter drug discovery candidates with favorable permeability and oral bioavailability before synthesis.

This ADME prediction tool uses Random Forest machine learning models. It transforms molecular structures into Morgan fingerprints using RDKit and scikit-learn to evaluate pharmacokinetic traits across six key properties.

Yes, RDKit and scikit-learn are required dependencies to calculate Morgan fingerprints for ADME prediction. The implementation also relies on numpy, pandas, and tdc to load models and output JSON or human-readable tables.

The best way to screen small molecules is using Random Forest models on Morgan fingerprints. This approach rapidly evaluates pharmacokinetic traits like Caco-2 permeability, PAMPA, HIA, and oral bioavailability from SMILES strings.

Morgan fingerprint-based prediction works by converting SMILES strings into numerical molecular representations. Random Forest models then map these fingerprints to predict six key ADME properties, accelerating early-stage drug discovery evaluation.