The invention discloses an anti-
hepatitis B
virus drug metabolism kinetics prediction method based on a Transform model, and belongs to the crossing field of
artificial intelligence and
pharmacokinetics. The prediction method comprises the following steps: acquiring pharmacokinetic related data of three TFV prodrugs inside and outside a mouse, a beagle and a
human body to form a
data set; building liver
cell PK and in-vivo mPBPK models, and developing
liver injury mouse /
human cell PK and mPBPK models on the basis; based on the experimental
data set and the parameters of the
cell PK and mPBPK models, constructing a
data set for Transform model training;
feature coding is carried out on a data set used for training, feature vectors are obtained through splicing, then the feature vectors are spliced with position codes to obtain input features to construct a Transform model, and the input features are input into the Transform model for training; and predicting the concentration of TFV-DP in
human liver cells by using the trained Transform model, thereby accurately predicting the concentration of drugs in the
human liver.