Research: Computer Aided Drug Design and Discovery of Novel Antibacterial and Antineoplastic Agents.
Title of the research project: Computer Aided Drug Design and Discovery of Novel Antibacterial and Antineoplastic Agents.
Thematic area : Medicinal Chemistry.
SSD: CHIM/08
Education: -18/10/2022. Single Cycle Degree in Pharmaceutical Chemistry and Technology, Sapienza University of Rome, 110/110 cum laude; - 07/2016. Classical diploma, Liceo Classico Statale “Anco Marzio”, 100/100.
Working experience: -11/2022 - in corso. Dottorato di Ricerca in Scienze delle Vita, Sapienza Università di Roma; -02/2022 – 10/2022. Tesi Sperimentale di Laurea in Chimica Farmaceutica, Sapienza Università di Roma, Dipartimento di Chimica e Tecnologia Farmaceutiche; -07/2021 – 02/2022. Farmacista Tirocinante - “Farmacia Benni S.a.s”, Roma.
Brief description of the research project:
The main objective of my research is the discovery and design of novel antibacterial and antineoplatic compounds using chemo-informatics techniques such as molecular docking and molecular dynamics (conventional and enhanced sampling methods). At the moment, the application of these methodologies resulted in the rationalization of the activity of a new class of human carbonic anhydrase and Wnt/β-catenin pathway inhibitors and in the identification of putative molecules endowed with bactericidal properties (ongoing biological testing).
Attendance at schools, meetings, seminars and courses:
- Scuole: 02/06/2023-10/06/2023, International School of Crystallography, Structural Drug Design 2023: Biology, Chemistry and Computers.
- Congressi: 02-04/05/2023, SBDD23 Computational Advances in Drug Discovery.
- Corsi: 02/02/2023-15/02/2023, Fundamentals of Enzyme Kinetics, Roberto Contestabile, Francesco Malatesta & Serena Rinaldo; 04/05/2023, Preparing artwork for scientific papers getting started in scientific illustration, Giorgio Giardina & Stefano Gianni.
- Seminari: 03/03/2023, Cristian Ripoli, Engineering proteins to boost LTP and memory; 09/03/2023, Anna Marabotti, Application of deep learning to the protein structure prediction: the tale of a “gigantic leap”; 17/03/2023, Elena Enzo, Deciphering self-renewal traits in epidermal stem cells; 23/03/2023, Eugenio Barone, Insulin Signaling in Alzheimer’s Disease Brain and Models Thereof; 14/04/2023, Velia Siciliano, Synthetic Biology: what, why and how; 21/04/2023, Simona Giunta, Protecting our genome: mechanisms to maintain DNA repeats stability in human cells; 12/05/2023, Amnon Horowitz, The chaperonin GroEL nano-machine: allostery and function; 19/05/2023, Giampietro Schiavo, The axonal transport machinery and its dysfunctions in neurodegenerative diseases; 01/06/2023, Massimiliano Aschi, Enzymes by the point of view of a computational chemist; 26/09/2023, Jean Quancard, Andra Unzue Lopez, Anita Wegert, Rob Young,Best Practices in hit-to-lead Optimization; 28/11/2023, Sandra Handschuh, Theodor Theis, Real World Use of FEP+ Calculations - Lessons Learned at Boehringer Ingelheim; 30/11/2023, Anna Vulpetti, Chris Phillips, Discovery of IL-1β and DCAF1 Ligands: from Fragment Hits to Cellular Proof of Concept, CryoEM in Industry: Small Proteins, Big Protein complexes, Supramolecular Protein Assemblies. Tales from the AstraZeneca microscope.