Gianfranco Cavallaro
Tutor: Cosimo Gianluca FORTUNA
PhD student in Chemical Sciences at the University of Catania, with a solid background in Organic and Bio-Organic Chemistry. Qualified as a Chemist, I combine chemistry and computer science through molecular modeling and the design of new molecules for the drug-receptor study. Experience in organic synthesis and extraction techniques of natural compounds. I obtained my master’s degree in chemical sciences at the University of Catania on 19/11/2021 with a grade of 110/110 laude. I had the following professional experiences: VOLUNTEER RESEARCH AND DEVELOPMENT COLLABORATOR (Dec. 2022 - Jul. 2023) at the Department of Chemical Sciences of the University of Catania where I deepened and continued the topics addressed during the previous research fellowship; ‘’Giovani Ricercatori’’ research scholarship of the CEUR Foundation (Feb. 2022 – Nov. 2022) at the Department of Chemical Sciences of the University of Catania "Design of new molecules potentially active for COVID". I have developed the following skills: MS Office, MAC & PC Systems, Linux, Data analysis software (MagicPlot, OriginLab, Excel and Simca), High skills in molecular modeling with related software (FLAP, Volsurf+, PyMOL, Autodock Vina), Bibliographic research, Synthesis and retrosynthesis of organic compounds, Extraction techniques of natural compounds, Mastery with various laboratory instruments, UV spectrophotometer and fluorescence, Chromatography and NMR and related reading software, Basic skills in the in vitro anti-proliferative evaluation of synthesized compounds against cancer cells with MTT.
[04.04.2025]
Thesis title: In silico design and synthesis of new potential antibacterial drugs and ligand-protein interaction studies for new molecules active against COX-1 receptor.
Keywords: In-silico studies, Docking, Molecular Modeling, Virtual Screening, COX-1
Abstract: The design of new drugs is a cornerstone of the pharmaceutical industry. The primary goals of pharmaceutical research are to expand the therapeutic range of existing bioactive molecules and to develop new therapies. Recently, the study of COX-1 has garnered significant attention, particularly due to its overexpression in certain human pathologies. Designing new compounds is both challenging and time-consuming, as identifying modifications to enhance ligand-protein interactions is often difficult, leading to considerable waste of time and resources in laboratories. Molecular docking could provide a solution, as it is an effective and efficient tool for in-silico screening, allowing the simultaneous evaluation of numerous compounds. The search for compounds begins with online databases. This process involves two approaches. The first approach uses similarity searches with four known COX-1 inhibitors. The second approach employs Knime machine learning software, which, through specific parameters (such as Lipinski's rule, natural source, etc.),selects the most promising compounds. The natural source chosen for this study is the green pistachio, which is rich in bioactive compounds. Many of these molecules show promise as starting points for the development of new antibiotics, antibacterials, and anti-inflammatory drugs. The first extraction technique was developed to obtain a comprehensive profile of polyphenolic components and enrich the database. These compounds were then imported into FLAP software, which identifies Molecular Interaction Fields (MIFs) computed in GRID. FLAP operates in a structure-based virtual screening (SBVS) mode, analyzing interactions between the molecules under study and the calculated protein pockets. These virtual screenings can be excellent starting points for the construction of a large database of compounds. The project’s novelty lies in applying in silico methods to uncover the physicochemical and ADME properties of these compounds, aiding the design, synthesis, or extraction of new antibacterial and anti-inflammatory drugs, including potential COX-1 inhibitors.
