Contacts:
Supervisor(s):
Prof. Tiziano Tuccinardi
PhD project Title:
Machine Learning Models for the Virtual Screening of Hydrolase Inhibitors: A Computational Approach to Drug Target Identification
Abstract of the PhD project:
This research focuses on the development of innovative computational tools for hydrolase-targeted drug discovery. Project 1 involves the creation of a novel scoring function specifically designed for molecular docking with hydrolases. Molecular docking plays a pivotal role in drug discovery and molecular modeling, and hydrolases such as fatty acid amide hydrolase (FAAH), monoacylglycerol lipase (MAGL), and soluble epoxide hydrolase (sEH) are critical targets for the treatment of cardiovascular, neurological, and inflammatory diseases. Developing a dedicated scoring function for hydrolases holds significant potential for advancing therapeutic strategies for these conditions. Project 2 aims to establish machine learning and deep learning models for the virtual screening of hydrolase inhibitors. Hydrolases have been implicated in coronary artery dysfunction, inflammation, and mitochondrial dysfunction associated with heart disease, making them promising drug targets. By leveraging machine learning approaches, this project seeks to identify novel hydrolase inhibitors, accelerating the discovery of potential therapeutics.
Publications link:
IT_IT 
EN