BIG DATA ANALYSIS AND ARTIFICIAL INTELLIGENCE IN HIT IDENTIFICATION AND TARGET FISHING FOR NEURODEGENERATIVE DISEASES

The use of in silico techniques, including artificial intelligence algorithms, in drug design is becoming more and more popular. These innovative methods allow to speed up the entire drug development process and to reduce its costs . Furthermore, they allow to address the critical issues associated with animal testing , as well as to manage and process large quantities of data, known as big data, in a faster and more productive way. In this context, this PhD work focused on the identification of new potential candidate drugs using innovative in silico techniques. In particular, the project aimed to identify and design new innovative drugs for the treatment of neurodegenerative diseases, which represent a field of great interest for the medicinal chemistry community . The computational approaches considered and analyzed in this doctoral project include: 1) machine learning (ML) for target fishing, big data analysis and virtual screening 2) structure based methods for hit identification.