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)