Method, device, and computer program for predicting interaction between compound and protein

A device for predicting interaction between a compound and a protein, the device comprising:

• a memory storing one or more instructions;
• and
• a processor configured to perform, by executing the stored one or more instructions, an operation of acquiring training data consisting of compound data for learning, protein data for learning, and interaction scores, an operation of constructing a deep-learning model using the acquired training data, and an operation of predicting interaction between a given compound and protein through the constructed deep-learning model, wherein
• the protein data for learning includes amino acid sequences of the protein for learning,
• the constructing operation includes an operation of generating first training data by excluding amino acid sequences associated with a first protein domain having a negative influence on the interaction from the amino acid sequences of the protein for learning;
• and
• an operation of training the deep-learning model using the first training data,
• wherein the constructing operation includes:
• selecting, from the acquired training data, a first plurality of proteins for learning whose interaction score with a specific compound for learning is equal to or greater than a threshold and a second plurality of proteins for learning whose interaction scores are equal to or less than the threshold;
• extracting a first common sequence by comparing amino acid sequences of the first plurality of proteins for learning;
• extracting a second common sequence by comparing amino acid sequences of the second plurality of proteins for learning;
• training the deep-learning model using second training data consisting of the first common sequence, the specific compound data for learning, and a first interaction score;
• and
• training the deep-learning model using third training data consisting of the second common sequence, the specific compound data for learning, and a second interaction score, and
• wherein the first interaction score is set to a value higher than an average interaction score of the first plurality of proteins for learning, and the second interaction score is set to a value lower than an average interaction score of the first plurality of proteins for learning.

A computer program stored in a computer-readable recording medium, the computer program, being combined with a computing device, comprising:

• acquiring training data consisting of compound data for learning, protein data for learning, and interaction scores;
• constructing a deep-learning model using the acquired training data;
• and
• predicting interaction between a given compound and protein through the constructed deep-learning model, wherein
• the protein data for learning includes amino acid sequences of the protein for learning,
• the constructing includes generating first training data by excluding amino acid sequences associated with a first protein domain having a negative influence on the interaction from the amino acid sequences of the protein for learning;
• and
• training the deep-learning model using the first training data,
• wherein the constructing includes:
• selecting, from the acquired training data, a first plurality of proteins for learning whose interaction score with a specific compound for learning is equal to or greater than a threshold and a second plurality of proteins for learning whose interaction scores are equal to or less than the threshold;
• extracting a first common sequence by comparing amino acid sequences of the first plurality of proteins for learning;
• extracting a second common sequence by comparing amino acid sequences of the second plurality of proteins for learning;
• training the deep-learning model using second training data consisting of the first common sequence, the specific compound data for learning, and a first interaction score;
• and
• training the deep-learning model using third training data consisting of the second common sequence, the specific compound data for learning, and a second interaction score, and
• wherein the first interaction score is set to a value higher than an average interaction score of the first plurality of proteins for learning, and the second interaction score is set to a value lower than an average interaction score of the first plurality of proteins for learning.