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Comparative analysis of computational approaches for predicting Transthyretin transcription activators and human dopamine D1 receptor antagonists
Mariya L. Ivanova, Nicola Russo, Konstantin Nikolic
https://arxiv.org/abs/2506.01137

Comparative analysis of computational approaches for predicting Transthyretin transcription activators and human dopamine D1 receptor antagonists
The study expands the application of scikit-learn-based machine learning (ML) to the prediction of small biomolecule functionalities based on Carbon 13 isotope (13C) NMR spectroscopy data derived from Simplified Molecular Input Line Entry System (SMILES) notations. The methodology previously demonstrated by predicting dopamine D1 receptor antagonists was upgraded with addition of new molecular features derived from the PubChem database. The enhanced ML model obtained 75.8% Accuracy, 84.2% Preci…