Ivanova, Mariya, Russo, Nicola, Mihaylov, Gueorgui and Konstantin, Nikolic ORCID: https://orcid.org/0000-0002-6551-2977
(2025)
Comparative analysis of computational approaches for predicting human neuronal Transthyretin (TTR) transcription activators and human dopamine D1 receptor antagonists.
Journal of Cellular Biochemistry.
ISSN 0730-2312
(Submitted)
Ivanova, Mariya, Russo, Nicola, Mihaylov, Gueorgui and Konstantin, Nikolic ORCID: https://orcid.org/0000-0002-6551-2977
(2025)
Leveraging Machine Learning and IUPAC names to identify TDP1 inhibitors.
Computational and Structural Biotechnology.
(Submitted)
Ivanova, Mariya, Russo, Nicola, Mihaylov, Gueorgui and Konstantin, Nikolic ORCID: https://orcid.org/0000-0002-6551-2977
(2025)
Leveraging Machine Learning and IUPAC names to identify TDP1 inhibitors.
Computational and Structural Biotechnology.
(Submitted)
Ivanova, Mariya, Russo, Nicola, Mihaylov, Gueorgui and Konstantin, Nikolic ORCID: https://orcid.org/0000-0002-6551-2977
(2025)
Leveraging 13C NMR spectrum data derived from SMILES for machine learning-based prediction of a small biomolecule functionality: a case study on human Dopamine D1 receptor antagonists.
Advance Intelligent Discovery.
(Submitted)
Ivanova, Mariya, Russo, Nicola, Mihaylov, Gueorgui and Konstantin, Nikolic ORCID: https://orcid.org/0000-0002-6551-2977
(2025)
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.
IEEE Transactions on Pattern Analysis and Machine Intelligence.
ISSN 0162-8828
(Submitted)
Ivanova, Mariya, Russo, Nicola, Mihaylov, Gueorgui and Konstantin, Nikolic ORCID: https://orcid.org/0000-0002-6551-2977
(2025)
Predicting novel functional roles of designed small biomolecules: an ML Approach utilizing PubChem Compound and Substance Identifiers (CID-SID ML model).
In Silico Pharmacology.
(Submitted)