Drug target affinity prediction method based on deep learning

TLDR: In this paper, a drug target affinity prediction method based on deep learning was proposed, which relates to the technical field of drug target affinities prediction, and consists the steps of: obtaining a drug compound and target protein data from a Davis data set and a KIBA data set; encoding the compound, and representing the protein by using a position specificity scoring matrix; inputting a compound label code into a CNN model, and performing feature extraction on the compound to obtain molecular representation of the compound; and learning an order relationship between amino acids in a protein sequence.

Abstract: The invention discloses a drug target affinity prediction method based on deep learning, and relates to the technical field of drug target affinity prediction. The method comprises the steps of: obtaining a drug compound and target protein data from a Davis data set and a KIBA data set; encoding the compound, and representing the protein by using a position specificity scoring matrix; inputting acompound label code into a CNN model, and performing feature extraction on the compound to obtain molecular representation of the compound; inputting the position specificity scoring matrix of the protein into an LSTM model, performing feature extraction on a protein sequence, and learning an order relationship between amino acids in a protein structure and a relationship between residues on the protein sequence to obtain the sequence representation of the protein; and simultaneously inputting the molecular representations of the compounds and the sequence representations of the proteins intoa fully linked layer to predict the affinity of the interaction of the compounds and the proteins. The method can predict the affinity relationship between the drug and the target more accurately.