3 citations · 4 across the 3 of their papers we have counts for
3 papers
Machine Learning - driven insights for predicting the impact of nanoparticles on the functionality of biomolecules, Illustrated by the case of DNA Damage-Inducible Transcript 3 (CHOP) inhibitors
Mariya L. Ivanova, Michael Nicholls, Nicola Russo +2
This study introduces a pioneering machine learning (ML)-based approach for predicting the impact of nanoparticle (NP) carriers on the functionality of attached small biomolecules.…
Machine learning model leveraging SMILES-derived NMR spectroscopy data to predict dopamine D1 receptor antagonists: a prospective framework for forecasting the impact of engineered nanoparticles on the functionalities of small biomolecules
Mariya L Ivanova, Michael Nichols, Nicola Russo +2
The article proposes a conceptual approach for evaluating the impact of engineered nanoparticles (NPs) on the functionality of small biomolecules. The developed machine learning (M…
In Silico Functional Profiling of Engineered Small Molecules: A Machine Learning Approach Leveraging PubChem Identifiers (CID_SID ML model)
Mariya L. Ivanova, Michael Nicholls, Nicola Russo +2
The article introduces a concept for a time- and cost-effective methodological framework leveraging machine learning (ML) models for both early-stage drug development and clinical…