METHOD, DEVICE, AND COMPUTER PROGRAM FOR PREDICTING INTERACTION BETWEEN COMPOUND AND PROTEIN

A method, a device, and a computer program for predicting the interaction between a compound and a protein are provided. A method for predicting the interaction between a compound and a protein, according to some embodiments of the present disclosure, may include: acquiring compound data for training, protein data for training, and training data including interaction scores; constructing a deep-learning model by using the acquired training data; and predicting the interaction between the given compound and protein by using the constructed deep-learning model. The interaction between the given compound and protein in an in vivo environment can be accurately predicted by training the deep-learning model, while excluding, from an amino acid sequence of the protein for training, amino acid sequences associated with a protein domain having a negative influence on the interaction.

FIELD

The present disclosure relates to a method, a device, and a computer program for predicting disease. More specifically, the present disclosure relates to a method for predicting the presence or extent of interaction between a given compound and protein using a deep-learning model, a device for performing the method, and a computer program in which the method is implemented.

BACKGROUND

By using computational methods and bio-informatics, researchers may find new uses of existing compounds or predict the uses of new compounds. This approach is widely used in the discovery of new drugs.

The discovery and development of new drugs always takes a lot of time and money and goes through a complex process. Accordingly, in recent years, research has been actively carried out to combine disciplines from various fields such as bio-informatics, chemi-informatics, computer science, and computer-aided drug discovery/design (CADD) to reduce the time required for the discovery and development of new drugs and to enhance the effects of new drugs.

However, since the related art employs a rule-based approach, it is impossible to predict a situation in which a rule may not be defined beyond human recognition.

SUMMARY

The technical task of some embodiments of the present disclosure is to provide a method for accurately predicting the presence or extent of interaction between a given compound and protein using a deep-learning model, a device for performing the method, and a computer program in which the method is implemented.

Another technical task of some embodiments of the present disclosure is to provide a method for accurately predicting the presence or extent of interaction between a compound and a protein in an in vivo environment using a deep-learning model, a device for performing the method, and a computer program in which the method is implemented.

Technical tasks of the present disclosure are not limited to those described above, and other technical tasks not mentioned above may also be clearly understood from the descriptions given below by those skilled in the art to which the present disclosure belongs.

A method for predicting interaction between a compound and a protein according to some embodiments of the present disclosure comprises, as a method for predicting interaction between a compound and a protein in a computing device, acquiring training data composed of compound data for learning, protein data for learning, and interaction scores; constructing a deep-learning model by using the acquired training data; predicting interaction between a given compound and protein through the constructed deep-learning model, wherein the protein data for learning may include amino acid sequences of the protein for learning, and the constructing may include 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 based on the first training data.

In some embodiments, the first protein domain may include a transmembrane domain.

In some embodiments, the constructing includes generating second training data consisting of the compound data for learning, amino acid sequence data associated with the first protein domain, and a first interaction score; and training the deep-learning model using the second training data, wherein the first interaction score may be determined based on the extent to which the first protein domain negatively affects the interaction.

In some embodiments, the constructing includes: selecting a first plurality of proteins for learning whose interaction score with a specific compound for learning is above a threshold and a second plurality of proteins for learning whose interaction score is below the threshold from the acquired training data; comparing amino acid sequences of the first plurality of proteins for learning and extracting a first common sequence; comparing amino acid sequences of the second plurality of proteins for learning and extracting a second common sequence; 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 learning data consisting of the second common sequence, the specific compound data for learning, and a second interaction score, wherein the first interaction score may be set to a value higher than an average interaction score of the first plurality of proteins for learning, and the second interaction score may be set to a value lower than the average interaction score of the first plurality of proteins for learning.

In some embodiments, the constructing includes: analyzing the acquired training data to select first protein data for learning whose interaction score with a specific compound for learning is equal to or greater than a threshold and a second protein for learning whose interaction score is equal to or lower than the threshold; comparing an amino acid sequence of the first protein for learning with an amino acid sequence of the second protein for learning to extract a non-common sequence; acquiring a predicted interaction score for the non-common sequence and the specific compound for learning through the deep-learning model; determining an interaction score for learning based on the predicted interaction score; and training the deep-learning model using second training data consisting of the non-common sequence, the specific compound data for learning, and the determined interaction score.

A device for predicting interaction between a compound and a protein according to some embodiments of the present disclosure may comprise a memory storing one or more instructions and a processor performing 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 a deep-learning model through the constructed deep-learning model by executing the one or more stored instructions. In this connection, the protein data for learning may include amino acid sequences of the protein for learning, and the constructing operation may include 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 based on the first training data.

A computer program according to some embodiments of the present disclosure may be stored in a computer-readable recording medium for executing: acquiring training data consisting compound data for learning, protein data for learning, and interaction scores in conjunction with a computing device; 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. In this connection, the protein data for learning may include amino acid sequences of the protein for learning, and the constructing may include 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 based on the first training data.

According to some embodiments of the present disclosure, interaction between a given compound and protein may be accurately predicted through a deep-learning model.

In addition, a deep-learning model may be trained by excluding amino acid sequences associated with protein domains negatively affecting interaction in vivo from amino acid sequences of proteins for learning, or the deep-learning model may be trained separately using associated sequences. As a result, the deep-learning model may accurately predict interaction between a given compound and protein in an actual in vivo environment, thereby greatly improving the utility of the deep-learning model.

The benefits according to the technical principles of the present disclosure are not limited to those described above, and other benefits not mentioned above may be clearly understood from the descriptions given below by those skilled in the art to which the present disclosure belongs.

DESCRIPTION OF EXEMPLARY EMBODIMENTS

Hereinafter, preferred embodiments of the present disclosure will be described in detail with reference to appended drawings. The advantages and features of the present disclosure, and a method for achieving them will be clearly understood with reference to the embodiments described in detail together with appended drawings. However, the technical principles and spirit of the present disclosure are not limited to the embodiments disclosed below but may be implemented in various other forms; rather, the present embodiments are provided to make the present disclosure complete and inform those skilled in the art clearly of the technical scope of the present disclosure, and the technical principles and spirit of the present disclosure may be defined within the technical scope of the appended claims.

In assigning reference symbols to the constituents of each drawing, it should be noted that the same constituents are intended to have the same symbol as much as possible, even when they are shown on different drawings. In addition, in describing the present disclosure, when it is determined that a detailed description of a related known configuration or function incorporated herein unnecessarily obscure the gist of the present disclosure, the detailed description thereof will be omitted.

Unless otherwise defined, all terms (including technical and scientific terms) used herein may be used in a sense commonly understood by those skilled in the art to which the present disclosure belongs. In addition, terms defined in commonly used dictionaries are not ideally or excessively interpreted unless otherwise explicitly defined. The terms used herein are intended to describe embodiments and are not intended to limit the present disclosure. In the present disclosure, a singular expression includes a plural expression unless clearly indicated otherwise in the corresponding phrase.

In addition, in describing the constituents of the present disclosure, terms such as first, second, A, B, (a), and (b) may be used. Such terms are intended only to distinguish one constituent from the others and do not limit the nature, sequence, or order of the constituents. When a constituent is said to be “linked to,” “combined with,” or “connected to” a different constituent, it should be understood that the constituent is linked or connected to the different constituent, but another constituent may be “linked,” “combined,” or “connected” between the two constituents.

The term “comprises” and/or “comprising” used in the present disclosure indicates the existence of a constituent, a stage, an operation, and/or a component described but does not exclude the existence or addition of one or more other constituents, stages, operations, and/or components.

FIG.1illustrates a device10for predicting interaction between a compound and a protein according to some embodiments of the present disclosure and input and output data of the device.

As illustrated inFIG.1, the device10for predicting interaction may be a computing device that predicts and outputs interaction information (e.g., binding affinity) of input compound and protein based on input data such as compound data and protein data. For example, when the input compound is a drug and the input protein is a target protein predicted to induce disease, the device10for predicting interaction may predict drug-target interactions (DTI) information. In this connection, the device10for predicting interaction may be effectively utilized to derive new candidate substances in a drug development process. Hereinafter, for the convenience of descriptions, the device10for predicting interaction is abbreviated to a “prediction device10.”

The computing device may be, but is not limited to, a notebook, a desktop, or a laptop computer, which may include all kinds of devices equipped with computing capabilities.FIG.15shows one example of a computing device.

More specifically, the prediction device10may predict interaction between a compound and a protein using a deep-learning model. In this connection, the deep-learning model may be implemented based on various kinds of neural network models and may be designed in various structures. The neural network model may include, for example, an artificial neural network (ANN), a convolutional neural network (CNN), a recurrent neural network (RNN), or a combination thereof but is not limited thereto. A detailed structure and a learning method of the deep-learning model will be described with reference to the drawings ofFIG.2and subsequent drawings.

The compound data may include, for example, data on the compound formula, functional group, molar mass, members, binding structure, electron number, and acidity of the compound but may further include various data without being limited thereto or may not include any of the data above.

The protein data may include, for example, an amino acid sequence of the protein, amino acid residues, tissue-specific or patient-specific expression patterns of the protein, and the protein's role in a specific cell signal transduction system but may further include various data without being limited thereto or may not include any of the data above.

The interaction information may include scores on various indicators related to the interaction, such as binding affinity, cohesion, and binding force. However, the interaction information is not limited thereto. Hereinafter, for the convenience of understanding, descriptions are continued based on the assumption that the interaction information is a score (“interaction score”) indicating the degree of interaction.

In some embodiments, the prediction device10may further predict the use (or efficacy) of a compound. Specifically, the prediction device10may output a list of proteins that are expected to interact with a given compound using a deep-learning model. For example, the prediction device10may predict the interaction score between a given compound and various proteins by inputting data of the given compound and data of a specific protein into the deep-learning model while changing the specific protein type. The prediction device10may then output a list of proteins consisting of proteins whose predicted interaction score is above a threshold. In addition, the prediction device10may predict the use or efficacy of the compound based on the common characteristics of the proteins included in the protein list. For example, when proteins included in the protein list exhibit different aspects of expression pattern in a patient sample of a particular disease, the prediction device10may predict that the given compound has efficacy for that disease. In addition, when the proteins included in the protein list are associated with a specific side effect, the prediction device10may predict that the given compound is highly likely to be associated with the side effect.

AlthoughFIG.1shows an example in which the prediction device10is implemented by one computing device, the prediction device10may be implemented by a plurality of computing devices. In this connection, a first function of the prediction device may be implemented in a first computing device, and a second function may be implemented in a second computing device. Alternatively, a specific function of the prediction device10may be implemented in a plurality of computing devices.

Hereinbefore, the prediction device10according to some embodiments of the present disclosure and its input and output data have been briefly described with reference toFIG.1. Hereinafter, a method for predicting interaction of a compound and a protein will be described with reference to the drawings ofFIG.2and subsequent drawings.

A computing device may perform each stage of a method for predicting interaction to be described below. In other words, each stage of the method may be implemented using one or more instructions executed by a processor of the computing device. All the stages included in the method may be executed by one physical computing device but may be executed by being distributed over a plurality of physical computing devices. For example, a first computing device may perform first stages of the method, and a second computing device may perform second stages of the method. Hereinafter, descriptions will be given based on the assumption that each stage of the method is performed by the prediction device10illustrated inFIG.1. Accordingly, when a subject is omitted for each operation in the following description, it may be understood that the operation is performed by the illustrated device10. However, in some cases, some stages of the method may be performed on another computing device.

FIG.2is a flow diagram illustrating a method for predicting interaction between a compound and a protein according to some embodiments of the present disclosure. However, the flow diagram is only a preferred embodiment in accordance with an aspect of the present disclosure, and it should be understood that some stages may be added or deleted depending on the needs.

As illustrated inFIG.2, the method for predicting interaction may start at stage S100of acquiring the training data (set). Herein, the training data (or samples constituting the training data) may be composed of compound data for learning, protein data for learning, and interaction information (i.e., ground truth interaction information), wherein the interaction information may be, for example, an interaction score that expresses the presence or extent of the interaction numerically.

The training data may be acquired from a public DB such as DrugBank or Pubchem, which is not limited thereto.

In stage S200, the deep-learning model may be trained using the acquired training data. For example, the prediction device10may train a deep-learning model by acquiring a predicted interaction score by entering each sample constituting the training data into the deep-learning model, calculating a prediction error based on the difference between the predicted interaction score and the ground truth interaction score, and backpropagating the calculated prediction error. Herein, training may mean that weights of the deep-learning model are updated to minimize prediction errors.

As described above, deep-learning models may be implemented (constructed) based on various types of neural network models. For example, as illustrated inFIG.3, a deep-learning model may be implemented based on ANN. In this connection, ANN may be composed of an input layer21, a hidden layer22, and an output layer23, wherein the input layer21is designed to receive compound data and protein data, and the output layer23may be designed to output interaction scores (I-scores) but are not limited thereto. Since those skilled in the art should be fully informed of the functions, operating principles, and basic training methods of each layer that makes up the ANN, detailed descriptions thereof will be omitted.

It should be noted that compound data may be converted into fingerprint data by a compound fingerprint technique such as circular fingerprinting and input to a deep-learning model. However, the technical scope of the present disclosure is not limited thereto.

A detailed structure of a deep-learning model and a specific method for training the deep-learning model according to an embodiment will be described with reference to the drawings ofFIG.4and subsequent drawings.

Again, descriptions will be given with reference toFIG.2.

In stage S300, the interaction between a given compound and protein may be predicted through a deep-learning model constructed. For example, the prediction device10may input data of the given compound and data of the protein into the deep-learning model to predict an interaction score.

Hereinbefore, a method for predicting interaction between a compound and a protein according to some embodiments of the present disclosure has been described with reference toFIG.2. Hereinafter, various embodiments of the present disclosure related to the structure of a deep-learning model and a method for training the deep-learning model will be described.

FIG.4illustrates a structure of a deep-learning model and a method for training the deep-learning model according to a first embodiment of the present disclosure.

As illustrated inFIG.4, a deep-learning model in the present embodiment may include a first neural network31receiving compound data, a second neural network32receiving protein data, and a third neural network33outputting an interaction score.

The first neural network41may be trained to extract feature data of an input compound by performing neural network operations on the input compound data. The first neural network31may be trained to accurately extract feature data unique to the compound by being composed of the second neural network32and an independent network. As mentioned earlier, the compound data may be converted into fingerprint data and input to the first neural network31, but the scope of the present disclosure is not limited thereto. The first neural network41may be implemented based on various types of neural networks, such as ANN, CNN, and RNN.

Next, the second neural network32may be trained to extract feature data of an input protein by performing neural network operations on the input protein data. The second neural network32may be constructed independently of the first neural network31so that it may be trained to accurately extract feature data unique to proteins. The protein data may include, for example, an amino acid sequence of the protein but are not limited thereto. The second neural network32may also be implemented based on various types of neural networks such as ANN, CNN, and RNN.

Next, the third neural network33may be trained to analyze the feature data of the compound and protein comprehensively through neural network operations to predict interaction scores. The third neural network33may be implemented as a fully connected layer but is not limited thereto.

The first to third neural networks31-33may be trained by backpropagating errors based on the difference of a predicted interaction score between the compound for learning and the protein for learning output through the third neural network33from the ground truth interaction score.

Hereinbefore, the structure of a deep-learning model and a method for training the deep-learning model according to a first embodiment of the present disclosure have been described with reference toFIG.4. As described above, by independently constructing a neural network that extracts feature data from compound data and protein data, the performance of a deep-learning model may be improved.

Hereinafter, the structure of a deep-learning model and a method for training the deep-learning model according to a second embodiment of the present disclosure will be described with reference toFIGS.5and6. In addition, hereinafter, for the sake of clarity of the present disclosure, descriptions overlapping with the preceding embodiments will be omitted.

FIG.5illustrates the structure of a deep-learning model according to a second embodiment of the present disclosure.

As illustrated inFIG.5, in the present embodiment, a second neural network42receiving protein data may be implemented based on a CNN. To be precise, at least a portion of the second neural network42receiving amino acid sequence data of the protein may be configured to include a CNN. In addition, the amino acid sequence data may be converted into a two-dimensional image suitable for the CNN through a pre-processing process, and the converted two-dimensional (2-D) image may be input to the second neural network42. However, specific pre-processing methods may vary depending on the embodiment.

In some embodiments, a plurality of n-gram sequences may be extracted from the amino acid sequence of the protein. In addition, a 2-D image may be generated by mapping a plurality of n-gram sequences onto a 2-D plane formed by two axes corresponding to amino acid types or amino acid sequences. In this connection, the pixel values of the image may be set based on the number of n-gram sequences appearing in the amino acid sequence. To further facilitate understanding, additional descriptions will be given with reference to the example ofFIG.6.

FIG.6shows an example of a process in which a bigram sequence extracted from an amino acid sequence (i.e.,51to53when n is 2) is mapped onto a 2-D plane50. For reference, in the following drawings, “AA” refers to an amino acid, and an alphabetic subscript (a, b, c, and the like) that is not a numeric subscript refers to the type of amino acid.

As illustrated inFIG.6, it is supposed that a plurality of bigram sequences51-53have been extracted from the amino acid sequences of a protein. In this connection, a first bigram sequence51(“AAa-AAb”) may be mapped to the coordinates (a, b) on the 2-D plane50. As mapping is progressed, the pixel value at the coordinates (a, b) may be increased by a certain value (e.g., 1). In the same manner, a second bigram sequence52(“AAb-AAa”) and a third bigram sequence53(“AAa-AAc”) and the like may be mapped on the 2-D plane50, and a 2-D image54may eventually be generated as the mapping process is repeated.

The first to third neural networks41-43may be trained by backpropagating errors based on the difference of a predicted interaction score between the compound for learning and the protein for learning output through the third neural network43from the ground truth interaction score. In addition, based on the learning, the second neural network42may be trained to extract local sequence patterns (features) that affect the interaction with a compound from a 2-D input image.

FIG.6shows an example in which n is 2; however, the example is intended only to provide the convenience of understanding, and n may be 3 or more. When n is 3 or more, the X-axis and/or Y-axis may be designed to correspond to a predefined amino acid sequence (e.g., AAa-AAa, AAa-AAb, or AAb-AAa). Alternatively, mapping of n-gram sequences may be made by further utilizing a channel axis of the 2-D image.

Hereinbefore, the structure of a deep-learning model and a method for training the deep-learning model according to the second embodiment of the present disclosure have been described with reference toFIGS.5and6. As described above, an amino acid sequence of a protein may be converted to a 2-D image using the n-gram technique, and local sequence patterns (features) that affect the interaction with a compound may be extracted from a 2-D image through a CNN. Accordingly, the performance of the deep-learning model may be improved.

Hereinafter, the structure of a deep-learning model and a method for training the deep-learning model according to a third embodiment of the present disclosure will be described.

In the present embodiment, a deep-learning model may be implemented based on a CNN. In addition, compound data and protein data may be converted into a 2-D image suitable for the CNN through pre-processing, and the converted 2-D image may be input to the deep-learning model.

Specifically, a pair of a specific compound and a specific protein may be mapped on a 2-D plane formed by a first axis corresponding to the functional group of the compound and a second axis corresponding to the amino acid residue of the protein to produce a 2-D image. In this connection, pixel values of the image may be set based on the degree of binding of the corresponding functional group and the amino acid residue. To provide further convenience of understanding, additional descriptions will be given with reference to the example illustrated inFIG.7.

FIG.7illustrates a process of mapping a pair of a protein and a compound onto the 2-D plane60using information on a residue and a functional group. In the following drawings, “CC” refers to Chemical Compound.

As illustrated inFIG.7, it is supposed that a compound includes a functional group3and a protein includes an amino acid residue3. Then, a value indicating the degree of binding of the functional group3and the amino acid residue3may be assigned to the (functional group3, amino acid residue3) coordinates as a pixel value. As the process is repeated, a 2-D image61may be generated.

The deep-learning model according to the present embodiment may be trained by backpropagating errors based on the difference between the predicted interaction score acquired by inputting a 2-D image (e.g.,61) and the ground truth interaction score. In addition, based on the learning, the CNN of the deep-learning model may be trained to extract the binding pattern of amino acid residues and compound functional groups from a 2-D image.

Hereinbefore, the structure of a deep-learning model and a method for training the deep-learning model according to the third embodiment of the present disclosure have been described with reference toFIG.7. As described above, pairs of proteins and compounds may be converted into a 2-D image by considering the amino acid residues and compound functional groups, and residue-functional group binding patterns (features) that affect the interaction between a protein and a compound may be extracted from the 2-D image. Accordingly, the performance of the deep-learning model may be improved.

Hereinafter, the structure of a deep-learning model and a method for training the deep-learning model according to a fourth embodiment of the present disclosure will be described.

In the present embodiment, a deep-learning model may be configured to include an RNN-based embedding layer and a neural network. To be precise, a neural network receiving amino acid sequence data of a protein (e.g., the second neural network32ofFIG.4) may be configured to include an embedding layer and a neural network layer, the embedding layer may be trained to output an embedding vector, and the neural network layer may be trained to extract feature data of the corresponding protein from the embedding vector through neural network operations. In addition, the amino acid sequence data may be converted into an input data (vector) of the embedding layer through pre-processing. However, specific pre-processing techniques may vary depending on the embodiments.

In some embodiments, a plurality of n-gram sequences may be extracted from the amino acid sequence of the protein. In addition, a plurality of n-gram sequences may be converted to a vector form and input into the embedding layer. To provide further convenience of understanding, additional descriptions will be given with reference to the example illustrated inFIG.8.

FIG.8illustrates a process in which bigram sequences71-73extracted from the amino acid sequence of a protein are converted into embedding vectors (Y1to Yt) through Bi-Long Short Term Memory (LSTM) based embedding layers.

As illustrated inFIG.8, it is supposed that a plurality of bigram sequences71-73have been extracted from the amino acid sequences of a protein. In this connection, each bigram sequence71-73may be converted into a bigram vector and input into an embedding layer. Any technique may be employed for converting a bigram sequence to a vector, where a known technique such as Back of Words (BoW) may be used.

The embedding layer may be trained to output embedding vectors (Y1to Yt) by performing a neural network operation on the input bigram vectors (X1to Xt). In some embodiments, a neural network layer74may be disposed within the embedding layer. In this connection, the neural network layer74may be trained to synthesize the output values of the RNN layer (e.g., a layer consisting of LSTM units) to generate embedding vectors (Y1to Yt).

Hereinbefore, the structure of a deep-learning model and a method for training the deep-learning model according to the fourth embodiment of the present disclosure have been described with reference toFIG.7. As described above, amino acid sequences may be converted into embedding vectors through an RNN-based embedding layer. In this connection, since the RNN may generate an embedding vector conveying features reflecting a sequential arrangement of amino acids, the performance of the deep-learning model may be improved.

Hereinafter, the structure of a deep-learning model and a method for training the deep-learning model according to a fifth embodiment of the present disclosure will be described.

The present embodiment relates to a method for constructing a deep-learning model to predict the interaction more accurately between a given compound and protein in an in vivo environment. Before setting out describing the present embodiment, for the convenience of understanding, the background of the present embodiment will be described briefly with reference toFIG.9.

As mentioned above, training data such as amino acid sequence data and interaction scores of a protein for learning may be acquired from a public DB. For example, amino acid sequence data80for learning of proteins P1 to Pn as illustrated inFIG.9may be acquired from a public DB. Herein, the amino acid sequences for learning of proteins (e.g., P1) may include sequences81-83associated with a specific protein domain, wherein the specific protein domain may include, for example, a transmembrane domain, an extracellular domain, or a subcellular organelles membrane domain and may include various other domains.

It is known that a protein having amino acid sequences associated with a particular protein domain is more likely to be in the corresponding protein domain in vivo. For example, it is known that a protein (e.g., membrane protein including a plasma membrane receptor) having sequences associated with a transmembrane domain is likely to be in the transmembrane site and function as a transmembrane protein. Accordingly, depending on which domain the amino acid sequence of a protein is associated with, the degree of interaction between a compound and the protein in vivo may vary greatly. For example, since a protein with amino acid sequences associated with the transmembrane domain is in the transmembrane site (domain) in vivo, the possibility of the protein interacting with the compound may be significantly decreased. Alternatively, a protein having amino acid sequences associated with an extracellular domain may interact better with a compound as the protein is in the extracellular site (domain) in vivo.

However, since the interaction scores between a compound and a protein provided by public DBs (or sites) are mostly measured in a laboratory environment (in vitro) rather than an in vivo environment, the interaction scores do not reflect the degree of interaction that may vary depending on the protein domains. Accordingly, it is difficult for a deep-learning model constructed based on the original data of the corresponding DB to accurately predict the degree of interaction between a compound and a protein in vivo. For example, a compound and a protein predicted to interact well based on a deep-learning model may not interact as expected in vivo, in which case the utility of the deep-learning model may be significantly reduced, and the development process of new drugs may be delayed, for example.

Hereinafter, the structure of a deep-learning model and a method for training the deep-learning model according to a fifth embodiment of the present disclosure will be described.

In the present embodiment, pre-processing of training data and/or learning the training data may be performed in a manner different from the preceding embodiments to reflect the influence of a protein domain on the degree of interaction with a compound in a deep-learning model. However, specific methods for pre-processing and learning may vary.

In a first example, the interaction score of training data may be adjusted by considering the influence of a protein domain on the degree of interaction. Specifically, the interaction score of a first protein for learning, which includes an amino acid sequence (hereinafter, abbreviated as a “negative amino acid sequence”) associated with a protein domain (hereinafter, abbreviated as a “negative domain” e.g., a transmembrane domain or a subcellular organelles membrane domain) having a negative influence, may be lowered. In contrast, the interaction score of training data may be adjusted. Specifically, the interaction score of a second protein for learning, which includes an amino acid sequence (hereinafter, abbreviated as a “positive amino acid sequence”) associated with a protein domain (hereinafter, abbreviated as a “positive domain”; e.g., an extracellular domain) having a positive influence, may be increased. In this connection, the increment may vary depending on the extent to which a protein domain affects the interaction with a compound, the number of associated amino acid sequences contained in the protein, and so on. In addition, a deep-learning model may be trained using training data with adjusted interaction scores. In this connection, since the deep-learning model is trained through interaction scores adjusted by considering the in vivo environment, the deep-learning model may more accurately predict the interaction in the in vivo environment. Accordingly, the utility of the deep-learning model may be improved.

In a second example, pre-processing may be performed to remove a positive amino acid sequence and/or a negative amino acid sequence from the amino acid sequence data of the protein for learning. As illustrated inFIG.10, the amino acid sequence data90of a protein (P1 to Pn) for learning may be newly constructed (generated) through pre-processing that removes negative amino acid sequences91-93from the amino acid sequences from the amino acid sequences of a protein for learning (P1 to Pn). In addition, a deep-learning model may be trained using training data including amino acid sequence data90, compound data for learning, and interaction scores. In this connection, since a deep-learning model may be prevented from being trained by amino acid sequences that negatively affect the interaction with a compound having high interaction scores, the performance and utility of the deep-learning model may be improved.

In a third example, as illustrated inFIG.11, first training may be performed on the deep-learning model102using the original training data101for which the removal pre-processing has not been performed. In addition, second training may be performed on the deep-learning model102using negative amino acid sequences and/or positive amino acid sequences acquired (or known) through the removal pre-processing. Specifically, the second training may be performed using training data103consisting of negative amino acid sequences and/or positive amino acid sequences, compound data, and interaction scores. In this connection, the interaction score of the training data103may be determined based on the influence on the interaction exerted by the positive domain or negative domain. For example, the interaction score of a negative amino acid sequence associated with a transmembrane domain may be set to a very low value (e.g., a value in the bottom 10%, such as 0). In the present disclosure, the second training may be performed after or before the first training or performed simultaneously with the first training. According to the present example, the extent to which a negative amino acid sequence and/or a positive amino acid sequence affects the interaction may be reflected in the deep-learning model through a separate training process. As a result, since the deep-learning model may make predictions by considering the in vivo environment, the performance and utility of the deep-learning model may be improved.

In a fourth example, post-processing may be performed to adjust interaction scores during the prediction process of the deep-learning model. Specifically, after predicting the interaction score between a given protein and compound through a trained deep-learning model, the prediction device10may adjust the predicted interaction score according to the extent to which the given protein includes a positive amino acid sequence and/or a negative amino acid sequence. In this connection, the amount of adjustment may vary depending on the degree to which a protein domain associated with the given protein affects the interaction with the compound, the number of associated amino acid sequences contained in the protein, and the like.

In a fifth example, a deep-learning model may be trained and utilized based on various combinations of the preceding examples.

Hereinbefore, methods for training a deep-learning model according to the fifth embodiment of the present disclosure have been described with reference toFIGS.9to11. According to the methods, a deep-learning model may be trained by considering the relationship between a protein domain and interaction. As a result, since a deep-learning model may accurately predict interactions in the in vivo environment, the utility of the deep-learning model may be greatly improved.

Hereinafter, a method for training a deep-learning model according to a sixth embodiment of the present disclosure will be described.

The present embodiment relates to a method for further improving the performance of a deep-learning model using analysis results of training data, and more specifically, to a method for training a deep-learning model (e.g., weighted training or additional training) using a common amino acid sequence of proteins showing a strong degree of interaction with a specific compound or a common amino acid sequence of proteins with a weak degree of interaction. Hereinafter, the present embodiment will be described with reference toFIGS.12and13.

FIG.12illustrates a process of analyzing training data and extracting primary amino acid sequences, andFIG.13illustrates a process of training a deep-learning model117using the extracted major amino acid sequences.

As illustrated inFIG.12, a first plurality of proteins for learning PHwhose interaction score I-score with a compound for learning CC1is equal to or greater than a first threshold (e.g., I-scoreH) and a second plurality of proteins PLwhose interaction score is equal to or less than a second threshold (e.g., I-scoreL) may be selected from training data. In addition, common sequences111,113and a non-common sequence112may be extracted by comparatively analyzing amino acid sequences of a plurality of selected proteins PH, PLfor learning.

For example, a first common sequence111may be extracted by comparatively analyzing the amino acid sequences of a first plurality of proteins PHfor learning. Herein, the first common sequence111may include an amino acid sequence commonly found in the first plurality of proteins PHfor learning or an amino acid sequence similar to the commonly found amino acid sequence. Any method may be employed for extracting the first common sequence111.

As another example, a second common sequence113may be extracted by comparatively analyzing the amino acid sequences of a second plurality of proteins PLfor learning. Herein, the second common sequence113may include an amino acid sequence commonly found in the second plurality of proteins PLfor learning or an amino acid sequence similar to the commonly found amino acid sequence. Any method may be employed for extracting the second common sequence113.

As yet another example, a non-common sequence112may be extracted by comparatively analyzing amino acid sequences of the first plurality of proteins PHfor learning and the second plurality of proteins PLfor learning. In other words, a non-common sequence112may be extracted based on the difference between amino acid sequences of the first plurality of proteins PHfor learning and the second plurality of proteins PLfor learning. Any method may be employed for extracting the non-common sequence112.

In some embodiments, a process of selecting amino acid sequences to be used for actual training from the amino acid sequences extracted as described above (e.g.,111to113) may be further performed. For example, from the amino acid sequences (e.g.,111to113), amino acid sequences whose sequence length is equal to or longer than a threshold may be selected as a learning target. This is because the shorter the sequence length, the less likely it is that the corresponding sequence affects the interaction. In another example, a deep-learning model trained for the selection process may be used. Specifically, an amino acid sequence to be learned may be selected based on the predicted interaction score output through the deep-learning model. For example, in the case of the first common sequence111, the sequence may be selected as a learning target when the predicted interaction score is equal to or greater than the first threshold (e.g., I-scoreH). In addition, in the case of the second common sequence113, the sequence may be selected as a learning target when the predicted interaction score is equal to or less than the second threshold (e.g., I-scoreL). In addition, in the case of the non-co-operative sequence112, the sequence may be selected as a learning target when the predicted interaction score is equal to or greater than the first threshold (e.g., I-scoreH) or equal to or less than the second threshold (e.g., I-scoreL).

Hereinafter, a process of training (e.g., additional training) a deep-learning model117will be described with reference toFIG.13.

As illustrated, a deep-learning model117may be trained using compound data CC1for learning and training data made up of a first common sequence111and a first interaction score114. In this connection, the first interaction score114may be set to a value higher than the first threshold (e.g., I-scoreH) or an average interaction score of the first plurality of proteins PH. By doing so, a positive influence of the first common sequence111on the interaction with the compound CC1may be strongly reflected in the deep-learning model117. In some examples, the first interaction score114may be determined based on a predicted interaction score between the compound CC1for learning and the first common sequence111output through the deep-learning model117. For example, the first interaction score114may be determined by a value higher than the predicted interaction score.

Alternatively, the deep-learning model117may be trained using data of the compound for learning CC1and the training data made up of the second common sequence113and the second interaction score116. In this connection, the second interaction score116may be set to a value lower than the first threshold (e.g., I-scoreL) or an average interaction score of the second plurality of proteins PL. By doing so, a negative influence of the second common sequence113on the interaction with the compound CC1may be strongly reflected in the deep-learning model117. In some examples, the second interaction score116may be determined based on a predicted interaction score between the compound CC1for learning and the second common sequence113output through the deep-learning model117. For example, the second interaction score116may be determined by a value lower than the predicted interaction score.

Alternatively, the deep-learning model117may be trained using data of the compound for learning CC1and the training data made up of the non-common sequence112and a third interaction score115. In this connection, the third interaction score115may be determined based on a predicted interaction score of the deep-learning model117. For example, when the predicted interaction score between the compound for learning CC1and the non-common sequence113output through the deep-learning model117is similar to the third threshold (e.g., I-scoreH), the third interaction score115may be determined by a value higher than the predicted interaction score. When the predicted interaction score is similar to the second threshold (e.g., I-scoreL), the third interaction score115may be determined by a value lower than the predicted interaction score.

In some embodiments, weighted training may be performed on the deep-learning model117for each sample that constitutes the training data based on sample weights. In this connection, a sample weight may be determined in various ways. For example, the weight of a first sample to which the first common sequence111belongs may be determined based on the length and/or frequency of appearance of the first common sequence111. As a more specific example, the longer the length of the first common sequence111or the higher the frequency with which the first common sequence111appears in the first plurality of proteins PH, the higher weight may be given to the first sample. In addition, the weight of a second sample to which the second common sequence111belongs may be assigned in the same manner as the first sample. In addition, the weight of a third sample to which the non-common sequence112belongs may be determined based on a predicted interaction score of the deep-learning model117. For example, a higher weight may be assigned to the third sample as the predicted interaction score becomes higher than the first threshold (e.g., I-scoreH) or lower than the second threshold (e.g., I-scoreL).

In the preceding embodiment, a specific method for weighted training may be performed in various ways, for example, by increasing the number of training trials (e.g., the number of training trials is increased as the sample weight becomes higher) or by amplifying the prediction error (e.g., as a sample weight becomes higher, the prediction error of the corresponding sample is amplified), and any method may be employed for performing the weighted training.

Hereinbefore, a method for training a deep-learning model according to the sixth embodiment of the present disclosure has been described with reference toFIGS.12and13. As described above, a deep-learning model may be trained separately using major amino acid sequences (in other words, sequences expected to exert a large influence on the interaction) derived through comparative analysis of training data. Accordingly, the performance of the deep-learning model may be further improved.

The first to sixth embodiments described so far may be combined in various ways. For example, as illustrated inFIG.14, a deep-learning model according to some embodiments may consist of the first to fifth neural networks121-125, and the second neural network122may include an RNN-based embedding layer128(refer to the fourth embodiment). The third123and fourth neural networks124may be implemented based on the CNN, and each neural network123,124may receive images126,127generated according to the second and third embodiments as an input. In addition, the fifth neural network125may synthesize feature data extracted from the first to fourth neural networks121-124to predict interaction scores.

Hereinafter, a computing device130capable of implementing the prediction device10according to some embodiments of the present disclosure will be described.

FIG.15illustrates a hardware structure of the computing device130.

As illustrated inFIG.15, the computing device130may include one or more processors131, a bus133, a communication interface134, a memory132that loads a computer program performed by the processor131, and a storage135that stores the computer programs136. However,FIG.15shows only those constituents related to the embodiment of the present disclosure. Accordingly, it should be understood by those skilled in the art to which the present disclosure belongs that other general-purpose constituents may be further included in addition to the constituents illustrated inFIG.15. In other words, the computing device130may further include various constituents in addition to the constituents illustrated inFIG.15. Alternatively, the computing device130may be composed by excluding some of the constituents illustrated inFIG.15.

The processor131may control the overall operation of each configuration of the computing device130. The processor131may be configured by including at least one of a Central Processing Unit (CPU), a Micro-Processor Unit (MPU), a MicroController Unit (MCU), a Graphics Processing Unit (GPU), or any arbitrary type of processor well known to the technical field of the present disclosure. In addition, the processor131may perform operations on at least one application or program for executing the methods/operations according to embodiments of the present disclosure. The computing device130may be equipped with one or more processors.

Next, the memory132may store various data, instructions, and/or information. The memory132may load one or more computer programs136from the storage135to execute the methods/operations according to the embodiments of the present disclosure. The memory132may be implemented using a volatile memory such as RAM but is not limited thereto.

Next, the bus133may provide a communication function between the constituents of the computing device130. The bus133may be implemented using various types of buses such as an address bus, a data bus, and a control bus.

Next, the communication interface134may support wired and wireless Internet communication of the computing device130. In addition, the communication interface134may support various communication schemes in addition to Internet communication. To this end, the communication interface134may be configured to include a communication module well known in the technical field of the present disclosure.

Next, the storage135may store the one or more programs136nontemporarily. The storage135may be configured to include non-volatile memory such as a Read-Only Memory (ROM), an Erasable Programmable ROM (EPROM), an Electrically Erasable Programmable ROM (EEPROM), and a flash memory; a hard disk; a removable disk; or any type of computer-readable recording medium well known in the technical field to which the present disclosure belongs.

Next, the computer program136, when loaded into the memory132, may include one or more instructions that instruct the processor131to perform the methods/operations according to various embodiments of the present disclosure. In other words, by executing the one or more instructions, the processor131may perform the methods/operations according to various embodiments of the present disclosure.

For example, the computer program136may include instructions that instruct the processor to perform 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 the interaction between a given compound and protein through the constructed deep-learning model. In this connection, the prediction device10according to some embodiments of the present disclosure may be implemented through the computing device130.

The technical principles and spirit of the present disclosure, described so far with reference toFIGS.1to15, may be implemented in computer-readable code on a computer-readable medium. The computer-readable recording medium may include, for example, a removable recording medium (CD, DVD, Blu-ray Disc, USB storage device, removable hard disk), or a stationary recording medium (ROM, RAM, or a built-in computer hard disk). The computer program recorded in a computer-readable recording medium may be transmitted to a different computing device through a network such as the Internet and installed in the different computing device, thereby being used in the different computing device.

In the above, just because all the constituents including an embodiment of the present disclosure are combined into one or operate in combination with each other does not mean that the technical principles and spirit of the present disclosure are necessarily limited to the embodiment. In other words, as long as being within the technical scope of the present disclosure, all the constituents may operate by being selectively integrated into one or more combinations.

Although the operations are illustrated in a particular order in the drawings, it should not be understood that the operations have to be performed in that order or in the sequential order according to which the operations are illustrated or that a desired result may be achieved only when all the illustrated operations are executed. In certain situations, multitasking and parallel processing may be advantageous. Moreover, separation into various configurations in the embodiments described above should not be understood as being required necessarily, and the program components and systems described above may generally be integrated into a single software product or packaged into multiple software products.

Hereinbefore, although the embodiments of the present disclosure have been described with reference to appended drawings, it should be understood by those skilled in the art to which the present disclosure belongs that the present disclosure may be embodied in other specific forms without changing the technical principles or essential characteristics of the present disclosure. Accordingly, the embodiments described above should be regarded as being illustrative rather than restrictive in every aspect. The technical scope of the present disclosure should be determined by the appended claims given below, and it should be understood that all of the technical principles found within the range equivalent to the technical scope of the present disclosure should be interpreted to belong thereto.