The invention discloses a
protein degradation targeted chimera
linker generation method based on deep
reinforcement learning, which comprises the following steps: expanding a constructed
ZINC data set and a PROTAC
data set by using a data enhancement method, and taking the obtained first
ZINC data set and first PROTAC data set as a supervision
training set; constructing a Transform model, and setting a training
step number, a
network layer number, an attention layer number and optimizer parameters; training a Transform model by using the first
ZINC data set, and training and updating the Transform model by using an objective function and a
back propagation algorithm; the first PROTAC data set is used for training the updated Transform model, the Transform model is migrated to a PROTAC target domain, and a Prior prior model is obtained; inputting the segment pair SMILES into a Prior prior model for batch generation, scoring the generated PROTAC by using a scoring function, introducing a strategy gradient
algorithm of
reinforcement learning, and updating an Agent model; repeating until the PROTAC
score is no longer increased or the training
step number is reached; and the updated Agent model is utilized to realize large-scale generation of the
protein degradation targeted chimera
linker conforming to expected attributes under the condition of given fragment pairs.