The invention relates to the technical field of computer-aided
drug design, in particular to a
drug molecule screening and optimizing method based on
artificial intelligence prediction, which comprises the following steps: S1, obtaining a dynamic
protein conformation set and molecular multi-dimensional characterization: obtaining a dynamic conformation set of a
target protein and a physicochemical property
spatial distribution diagram of a
binding pocket of the dynamic conformation set, a two-dimensional molecular map topological structure and three-dimensional conformation coordinates of the
drug molecules are obtained; s2, multi-
modal fusion prediction is carried out; s3, generating interpretable optimization guidance; and S4, automatic iterative optimization: performing batch prediction and screening on the new candidate molecular structure, taking the screened optimal molecule as a new starting point, repeatedly executing the
interpretability optimization guidance generation step and the step until an iteration termination condition is met, and outputting a final optimized molecule
list. Through the multi-
modal fusion
deep learning model, the
interaction strength of the drug molecules and the
target protein can be quickly and accurately predicted, and the screening efficiency of the drug molecules is greatly improved.