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manuela.sabatino@uniroma1.it
Manuela Sabatino
Assegnista di ricerca
Struttura:
DIPARTIMENTO DI CHIMICA E TECNOLOGIE DEL FARMACO
E-mail:
manuela.sabatino@uniroma1.it
Pagina istituzionale corsi di laurea
Curriculum Sapienza
Pubblicazioni
Titolo
Pubblicato in
Anno
Essential Oils from Mediterranean Plants Inhibit In Vitro Monocyte Adhesion to Endothelial Cells from Umbilical Cords of Females with Gestational Diabetes Mellitus
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
2023
Human estrogen receptor α antagonists, part 2: Synthesis driven by rational design, in vitro antiproliferative, and in vivo anticancer evaluation of innovative coumarin-related antiestrogens as breast cancer suppressants
EUROPEAN JOURNAL OF MEDICINAL CHEMISTRY
2022
Targeting the anti-apoptotic Bcl-2 family proteins: Machine learning virtual screening and biological evaluation of new small molecules
THERANOSTICS
2022
Essential Oils Biofilm Modulation Activity and Machine Learning Analysis on Pseudomonas aeruginosa Isolates from Cystic Fibrosis Patients
MICROORGANISMS
2022
In vivo Antiphytoviral Activity of Essential Oils and Hydrosols From Origanum vulgare, Thymus vulgaris, and Rosmarinus officinalis to Control Zucchini Yellow Mosaic Virus and Tomato Leaf Curl New Delhi Virus in Cucurbita pepo L
FRONTIERS IN MICROBIOLOGY
2022
Ligand-based and structure-based studies to develop predictive models for {SARS}-{CoV}-2 main protease inhibitors through the 3d-qsar.com portal
JOURNAL OF COMPUTER-AIDED MOLECULAR DESIGN
2022
Human Estrogen Receptor Alpha Antagonists, Part 3: 3-D Pharmacophore and 3-D QSAR Guided Brefeldin A Hit-to-Lead Optimization toward New Breast Cancer Suppressants
MOLECULES
2022
A rapid and consistent method to identify four SARS-CoV-2 variants during the first half of 2021 by RT-PCR
VACCINES
2022
Machine learning data augmentation as a tool to enhance quantitative composition–activity relationships of complex mixtures. A new application to dissect the role of main chemical components in bioactive essential oils
MOLECULES
2021
A comparative analysis of punicalagin interaction with PDIA1 and PDIA3 by biochemical and computational approaches
BIOMEDICINES
2021
Antitumor effect of Melaleuca alternifolia essential oil and its main component terpinen-4-ol in combination with target therapy in melanoma models
CELL DEATH DISCOVERY
2021
Human Estrogen Receptor α Antagonists. Part 1: 3-D QSAR-Driven Rational Design of Innovative Coumarin-Related Antiestrogens as Breast Cancer Suppressants through Structure-Based and Ligand-Based Studies
JOURNAL OF CHEMICAL INFORMATION AND MODELING
2021
Discovery of the first human arylsulfatase a reversible inhibitor impairing mouse oocyte fertilization
ACS CHEMICAL BIOLOGY
2020
Potent in vitro activity of citrus aurantium essential oil and vitis vinifera hydrolate against gut yeast isolates from irritable bowel syndrome patients—the right mix for potential therapeutic use
NUTRIENTS
2020
Experimental data based machine learning classification models with predictive ability to select in vitro active antiviral and non-toxic essential oils
MOLECULES
2020
Identification of Inhibitors to trypanosoma cruzi sirtuins based on compounds developed to human enzymes
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
2020
Antimicrobial essential oil formulation: chitosan coated nanoemulsions for nose to brain delivery
PHARMACEUTICS
2020
Essential oils biofilm modulation activity, chemical and machine learning analysis. Application on staphylococcus aureus isolates from cystic fibrosis patients
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
2020
Machine learning analyses on data including essential oil chemical composition and in vitro experimental antibiofilm activities against Staphylococcus species.
MOLECULES
2019
Chemical composition and antimicrobial activity of essential oil of Helichrysum italicum (Roth) G. Don fil. (Asteraceae) from Montenegro
NATURAL PRODUCT RESEARCH
2019
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Progetti di Ricerca
Sviluppo di modelli QSAR predittivi mediante tecniche di Machine Learning: applicazione ad inibitori HDAC
Disruptor of Telomeric Silencing 1-Like (DOT1L): identificazione di nuova classe di inibitori non nucleosidici mediante approacci ligand-based e structured-based.
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