The invention discloses a
protein generation model optimization method based on
deep learning, and relates to the technical field of
protein generation. The optimization method comprises the following steps: generating a candidate
protein electron density distribution diagram based on the structural characteristics of a target spot by adopting a pre-trained
diffusion model; converting the candidate protein
electron density distribution diagram into a corresponding
amino acid sequence; determining a functional index value corresponding to the
amino acid sequence, and constructing a feedback
data set; setting a reward threshold value, and marking the feedback
data set as a
positive sample data set and a
negative sample data set according to the reward threshold value; constructing a utility function taking the reward threshold as a reference point, and respectively calculating utility values of the
positive sample data set and the
negative sample data set; and performing iterative optimization on the
diffusion model according to the utility value. By adopting the technology provided by the invention, the dependence on preference
paired data of large-scale and high-quality protein sequences can be avoided, and the performance of the protein generation model can be effectively and continuously improved.