Abstract:
In recent years, the rise and development of computer-aided drug molecular design(CADD) together with artificial intelligence has greatly accelerated the research paradigm shift in drug discovery. As an indispensable technology in CADD, molecular dynamics simulation(MD) is often used to study the dynamic process of protein-ligand complexes; as a data-driven modeling method, machine learning(ML) has been widely used in various stages of drug discovery. The intrinsic complimentary properties of MD and ML bring up a number of possibilities for their integration. On the one hand, ML can be employed to analyze massive and high-dimensional information generated by MD, revealing key conformations and states through strategies such as feature extraction and dimensionality reduction to elucidate the underlying molecular mechanisms of biological systems. On the other hand, the MD data comprising dynamic information can be utilized to train the ML models and improve their accuracy in predicting thermodynamic and kinetic properties of protein-ligand systems. Therefore, the application of the combination of MD and ML in the field of drug design is of great significance.