Advancing Drug Discovery through Integrative Computational Models and AI Technologies

The intricate, costly, and time-intensive nature of traditional drug discovery processes hinders the development of novel pharmaceuticals. This research illustrates a comprehensive approach integrating advanced computational models, artificial intelligence (AI), and molecular biology techniques to streamline these processes and enhance pharmacological research. By transcending conventional static models, this project focuses on dynamic protein behavior, multiscale molecular dynamics, and Fully Connected Graph Neural Networks (FCGNN) employment for a more nuanced understanding of drug discovery mechanisms.

This work addresses several critical limitations of current methodologies: the oversimplification of receptor-ligand interactions, the static representation of protein structures, and the neglect of electronic distribution changes in molecular interactions. By developing novel simulation techniques that emphasise protein dynamics, proposing a multiscale analysis for real-time molecular characteristic updates, and applying FCGNN for the comprehensive mapping of molecular effects, this research aims to provide profound insights into the mechanistic processes underlying drug-protein interactions.

Ultimately, this research seeks to establish a new framework for biomedical research and pharmacology, where biological systems' dynamic and complex nature is accurately modelled and understood, leading to the discovery of more effective, tailored medical treatments.