1 citations · 1 across the 2 of their papers we have counts for
6 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…
IUPAC-Induced Computational Approaches for Identifying Boosters of Small Biomolecule Functionality: A Case Study of Human Tyrosyl-DNA Phosphodiesterase 1 (TDP1) Inhibitors
Mariya L. Ivanova, Nicola Russo, Gueorgui Mihaylov +1
This paper introduces several proof-of-concept (PoC) computational methods intended to offer biochemical researchers straightforward, time- and cost-effective strategies to acceler…
A gauge theory of complex adaptive systems
Gueorgui M. Mihaylov, Sergio L. Cacciatori
We introduce a geometric construction of a gauge field theory of a complex adaptive system. It is based on a suitable simplicial formulation of a discrete geometry that manifests r…
Comparative analysis of computational approaches for predicting Transthyretin (TTR) transcription activators and human dopamine D1 receptor antagonists
Mariya L. Ivanova, Nicola Russo, Gueorgui Mihaylov +1
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 spectrosco…