Machine learning method for molecular dynamics simulation of interactions between low-temperature plasma and drugs

This study aims to explore the interaction between low-temperature plasma and drug molecules and perform molecular dynamics simulations with the help of machine learning methods. We selected 200 drug samples for experiments, and the experimental environment was specific plasma conditions, including temperature, density, ionization degree and plasma type. By observing at successive time points, we record the rate at which drug molecules change, the types and number of chemical bonds affected, the amount of drug molecules remaining and the new compounds that may be produced. In addition, we considered other possible influencing factors such as pH, solvent type, relative humidity and temperature in the laboratory. Machine learning models are trained to predict the dynamics and reaction trends of drug molecules with plasma. Preliminary results show that machine learning methods demonstrate a high degree of accuracy and effectiveness in predicting and understanding complex interactions between plasmas and drugs.