The present application relates to a
drug protein binding rate prediction method; first, the influence of
drug protein binding on
protein structure change is characterized by two-dimensional near-
infrared correlation
spectroscopy, and then a
quantitative model is established for predicting the
drug protein binding rate by using the characteristic spectrum area related to the drug protein binding. The two-dimensional correlation
spectroscopy and
machine learning are organically combined to establish a prediction model for the drug protein binding rate in the simulated physiological environment from the aspects of structure characterization
signal extraction, separation and quantitative analysis; in actual operation, the
sample processing method is simple, the time consumption is less, the cost is low, and the technology is suitable for various related scientific research activities, which can not only realize the protein binding rate prediction of one kind of drug (including acid / alkali type, salt type), but also can distinguish the protein binding
rate difference of different products of the same drug, and has important significance for drug
in vitro evaluation,
generic drug consistency evaluation,
pharmacodynamics research and the like.