Patent Publication Number: US-11664094-B2

Title: Drug-screening system and drug-screening method

Description:
TECHNICAL FIELD 
     The present disclosure relates to a drug-screening system and a drug-screening method. 
     BACKGROUND 
     The process from screening a drug to becoming a truly useful drug may take much time and expense. A lead compound optimization is an important stage in the drug development process, and the average cost of a drug developing process is $414 million (about 23% of the drug development process), and the average time of the drug developing process is two years (about 15% of the drug development process). In the past, optimization models using simulation software or expert experience have been difficult to cope with increasingly complex compound structures, as the design of the simulation software will take much time and the success rate is low. 
     Accordingly, how to reduce drug development time and cost effectively has become an important issue. 
     SUMMARY 
     The present disclosure provides a drug-screening system, which includes an encoding module, a candidate-drug generating module and a drug-ranking module. The encoding module is configured to encode a drug expression and at least one drug-ranking indicator to generate a first encoding variable. The candidate-drug generating module is configured to train a generative adversarial network according to the first encoding variable to generate a plurality of candidate drugs, wherein each of the candidate drugs has a generative drug expression and at least one generative drug-ranking indicator. The drug-ranking module is configured to rank strengths of the candidate drugs according to the generative drug-ranking indicator of each of the candidate drugs. 
     The present disclosure provides a drug-screening method, which includes the following step. A drug expression and at least one drug-ranking indicator are encoded to generate a first encoding variable. A generative adversarial network is trained according to the first encoding variable to generate a plurality of candidate drugs, wherein each of the candidate drugs has a generative drug expression and at least one generative drug-ranking indicator. The candidate drugs are ranked according to the generative drug-ranking indicator of each of the candidate drugs. 
    
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
       The present disclosure can be more fully understood by reading the subsequent detailed description and examples with references made to the accompanying drawings, wherein: 
         FIG.  1    is a schematic view of a drug-screening system according to an embodiment of the present disclosure; 
         FIG.  2    is a schematic view of encoding variable according to an embodiment of the present disclosure; 
         FIG.  3    is a schematic view of a calculation of the drug structure distribution according to an embodiment of the represent disclosure; 
         FIG.  4    is a detailed schematic view of the candidate-drug generating module in  FIG.  1   ; 
         FIG.  5    is a flowchart of a drug-screening method according to an embodiment of the present disclosure; and 
         FIG.  6    is a detailed flowchart of step S 504  in  FIG.  5   . 
     
    
    
     DETAILED DESCRIPTION OF DISCLOSED EMBODIMENTS 
     Technical terms of the disclosure are based on their general definition in the technical field of the disclosure. If the disclosure describes or explains one or some terms, definition of the terms is based on the description or explanation of the disclosure. Each of the disclosed embodiments has one or more technical features. In possible implementation, a person skilled in the art would selectively implement all or some technical features of any embodiment of the disclosure or selectively combine all or some technical features of the embodiments of the disclosure. 
     In each of the following embodiments, the same reference number represents the same or a similar element or component. 
       FIG.  1    is a schematic view of a drug-screening system according to an embodiment of the present disclosure. Please refer to  FIG.  1   . The drug-screening system  100  includes an encoding module  110 , a candidate-drug generating module  120  and a drug-ranking module  130 . 
     The encoding module  110  is configured to encode a drug expression and at least one drug-ranking indicator to generate first encoding variable  200 . In the embodiment, the encoding module  110  may be, for example, an encoder. The first encoding variable is, for example, a latent variable, and includes the drug expression and the drug-ranking indicator as shown in  FIG.  2   . The latent variable may be decoded into the drug expression as a molecular structure of a candidate drug. In  FIG.  2   , the drug expression is encoded in a region  210  and the drug-ranking indicator is encoded in a region  220  followed the region  210 . The drug expression and the drug-ranking indicator may be obtained from a known compound, such as active assays. 
     In one embodiment, the drug expression includes, for example, a simplified molecular input line entry specification (SMILES) or a compound fingerprint, but the present disclosure is not limited thereto. The SMILES is a specification that explicitly describes the molecular structure in an ASCII string. That is, the SMILES is expressed as a string, such as c1c(O)c1(NC(═O)). The compound fingerprint extracts important structural features from the SMILE. That is, the compound fingerprint is expressed as a vector, such as [1, 1, 0, 1, 0, 0 . . . ]. The drug expression uniquely corresponds to a particular compound (such as a real compound of MCF-7 (Breast Cancer) or other compounds). 
     In addition, the drug-ranking indicator includes a drug structure distribution or a pharmacological property. In one embodiment, the drug structure distribution may include, for example, a number of nearest neighbor of the compound. For example, as shown in  FIG.  3   , the similarity between each compound and neighbor nodes is calculated. Then, a number N of the compound located within a similarity threshold K is calculated based on the compound  310 . A binarization is performed on the number N of the compound. If N&gt;0, a value of N is set as 1. As shown in  FIG.  3   , there are six compounds located within the similarity threshold K, and therefore the value of N is 1. Afterward, the value of N serves as the drug structure distribution. In another embodiment, the pharmacological property includes, for example, IC50, GI, etc., but the present disclosure is not limited thereto. 
     The candidate-drug generating module  120  is coupled to the encoding module  110 . In the embodiment, the candidate-drug generating module  120  may be, for example, a microprocessor or a microcontroller. The candidate-drug generating module  120  is configured to train a generative adversarial network (GAN) according to the first encoding variable to generate a plurality of candidate drugs, wherein each of the candidate drugs has a generative drug expression and at least one generative drug-ranking indicator. In the embodiment, the candidate drugs may be, for example, lead compounds. The generative drug expressions respectively include, for example, a simplified molecular input line entry specification (SMILES) or a compound fingerprint, but the present disclosure is not limited thereto. The generative drug-ranking indicators respectively include a drug structure distribution or a pharmacological property. The description of the generative drug expressions and the generative drug-ranking indicators may refer to the description of the drug expressions and the drug-ranking indicator, and the description thereof is not repeated herein. 
     After the candidate-drug generating module  120  receives the first encoding variable generated by the encoding module  110 , the candidate-drug generating module  120  trains the generative adversarial network according to the first encoding variable. For example, the candidate-drug generating module  120  may generate a plurality of fake drug expressions and a plurality of fake drug-ranking indicators according to a random value. In the example, the fake drug expressions and the fake drug-ranking indicators thereof are different from each other. The fake drug expressions respectively include, for example, a simplified molecular input line entry specification (SMILES) or a compound fingerprint, but the present disclosure is not limited thereto. The fake drug-ranking indicators respectively include a drug structure distribution or a pharmacological property. The description of the fake drug expressions and the fake drug-ranking indicators may refer to the description of the drug expressions and the drug-ranking indicator, and the description thereof is not repeated herein. 
     Then, the candidate-drug generating module  120  may transmit the fake drug expressions and the fake drug-ranking indicators to the encoding module  110 . Afterward, The encoding module  110  encodes the fake drug expressions and the fake drug-ranking indicators to generate a plurality of second encoding variables. Then, the encoding module  110  transmits the second encoding variables to the candidate-drug generating module  120 . Afterward, the candidate-drug generating module  120  may compare the first encoding variables with the second encoding variables to generate determination values. The determination values indicate the probability of similarities between the drug expression and the drug-ranking indicator included in the first encoding variable with the fake drug expressions and the fake drug-ranking indicators included in the second encoding variables. 
     Then, the candidate-drug generating module  120  may determine whether the determination values approach a predetermined value. In the embodiment, the predetermined value is, for example, 50%, but the present disclosure is not limited thereto. When it is determined that the determination values approach the predetermined value, this indicates that the determination is converged, and fake drug expressions and the fake drug-ranking indicators in the second encoding variables are similar to the drug expression and the first ranking indicator included in the first encoding variable. Then, the candidate-drug generating module  120  may generate the candidate drugs according to the fake drug expressions and the fake drug-ranking indicators corresponding to the second encoding variables, wherein the generative drug expressions correspond to the fake drug expressions and the generative drug-ranking indicators correspond to the fake drug-ranking indicators. 
     When it is determined that the determination values do not approach the predetermined value, this indicates that the determination is not converged, and the fake drug expressions and the fake drug-ranking indicators included in the second encoding variables are not similar to the drug expression and the first ranking indicator included in the first encoding variable. Then, the candidate-drug generating module  120  may update the fake drug expressions and the fake drug-ranking indicators. For example, the candidate-drug generating module  120  may modify the fake drug expressions and the fake drug-ranking indicators according to the determination values to update the fake drug expressions and the fake drug-ranking indicators. 
     Then, the candidate-drug generating module  120  may again transmits the updated fake drug expressions and the updated fake drug-ranking indicators to the encoding module  110 . Afterward, the encoding module  110  encodes the updated fake drug expressions and the updated fake drug-ranking indicators to generate the updated second encoding variables. Then, the candidate-drug generating module  120  may compare the first encoding variable with the updated second encoding variables to generate determination values. Afterward, the candidate-drug generating module  120  may again determine whether the determination values approach the predetermined value. Then, the candidate-drug generating module  120  may repeatedly perform the above method until the determination values approach the predetermined value and the candidate-drug generating module  120  generates the candidate drugs according to the fake drug expressions and the fake drug-ranking indicators corresponding to the second encoding information. 
     The drug-ranking module  130  is coupled to the candidate-drug generating module  120 . In the embodiment, the drug-ranking module  130  may be, for example, a microprocessor or a microcontroller. The drug-ranking module  130  is configured to rank strengths of the candidate drugs according to the generative drug-ranking indicator of each of the candidate drugs. That is, when the drug-ranking module  130  receives the candidate drugs, the drug-ranking module  130  may obtain the generative drug expressions and the generative drug-ranking indictors from the candidate drugs. 
     Then, the drug-ranking module  130  may analyze the generative drug expressions and the generative drug-ranking indictors of the candidate drugs. For example, the drug-ranking module  130  may analyze the strengths of the candidate drugs according to the generative drug-ranking indictors. Then, the drug-ranking module  130  may rank the order of strengths of the candidate drugs according to the strengths of the generative drug-ranking indictors and generates a list of the order of the candidate drugs. Therefore, the embodiment of the present disclosure may effectively reduce drug development time and expense, and accelerate the development process. 
     Furthermore, the drug-screening system  100  further includes a display module  140 . In the embodiment, the display module  140  may be, for example, a display or a monitor, but the present disclosure is not limited thereto. The display module  140  is coupled to the encoding module  110 , the candidate-drug generating module  120  and the drug-ranking module  130 . The display module  140  is configured to display the first encoding variables, the candidate drugs, the generative drug expressions, the generative drug-ranking indicators and strengths of the candidate drugs through a graphic user interface. Therefore, the user may quickly know the operation of the drug-screening system  100  and the generation of the candidate drugs, thereby effectively reduce the time and expense of drug development, and accelerating the development process. 
       FIG.  4    is a detailed schematic view of the candidate-drug generating module in  FIG.  1   . Please refer to  FIG.  4   . In the embodiment, the candidate-drug generating module  120  includes a fake-drug generating module  410  and a determination module  420 . 
     The fake-drug generating module  410  is coupled to the encoding module  110 . The fake-drug generating module  410  is configured to generate a plurality of fake drug expressions and a plurality of fake drug-ranking indicators respectively according to a random value. The fake drug expressions and the fake drug-ranking indicators are used to train the generative adversarial network. In the embodiment, the random value may be preset, for example, by the user. Then, the fake-drug generating module  410  transmits the fake drug expressions and the fake drug-ranking indicators to the encoding module  110 . Afterward, the encoding module  110  may encode the fake drug expressions and the fake drug-ranking indicators to generate the second encoding variables. 
     The determination module  420  is coupled to the encoding module  110  and the fake-drug generating module  410 . The determination module  420  is configured to receive the first encoding variables and the second encoding variables, and compare the first encoding variable with the second encoding variables to generate determination values. 
     After the fake-drug generating module  410  receives the determined values, the fake-drug generating module  410  may determine whether the determination values approach a predetermined value. When it is determined that the determination values approach the predetermined value, this indicates that the fake drug expressions and the fake drug-ranking indicators included in the second encoding variables are similar to the drug expression and the drug-ranking indicator included in the first encoding variable. Then, the fake-drug generating module  410  may generate the candidate drugs according to the fake drug expressions and the fake drug-ranking indicators corresponding to the second variables, wherein the generative drug expressions correspond to the fake drug expressions and the generative drug-ranking indicators correspond to the fake drug-ranking indicators. 
     When it is determined that the determination values do not approach the predetermined value, this indicates that the fake drug expressions and the fake drug-ranking indicators included in the second encoding variables are not similar to the drug expression and the drug-ranking indicator included in the first encoding variable. Then, the fake-drug generating module  410  may update the fake drug expressions and the fake drug-ranking indicators. For example, the fake-drug generating module  410  may modify the fake drug expressions and the fake drug-ranking indicators according to the determination values to update the fake drug expressions and the fake drug-ranking indicators. 
     Then, the fake-drug generating module  410  may again transmits the updated fake drug expressions and the updated fake drug-ranking indicators to the encoding module  110 . Afterward, the encoding module  110  encodes the updated fake drug expressions and the updated fake drug-ranking indicators to generate the updated second encoding variables. Then, the determination module  420  may again compare the first encoding variable with the updated second encoding variables to generate determination values. The determination values may be transmitted to the fake generating module  410 . Afterward, the fake-drug generating module  410  may again determine whether the determination values approach the predetermined value. Then, the fake-drug generating module  410  and the determination module  420  may repeatedly perform the above method until the determination values approach the predetermined value and the fake-drug generating module  410  generates the candidate drugs according to the fake drug expressions and the fake drug-ranking indicators. 
     Furthermore, the fake-drug generating module  410  and the determination module  420  are coupled to the display module  140 . Accordingly, the display module  140  may further display the fake drug expressions, the fake drug-ranking indicators, the second encoding variables, the determination values and a comparing result through the graphic user interface. Therefore, the user may quickly know the operation of the drug-screening system  100  and the generation of the candidate drugs, thereby effectively reduce the time and expense of drug development, and accelerating the development process. 
     According to the above-mentioned description, the above embodiments may provide a drug-screening method.  FIG.  5    is a flowchart of a drug-screening method according to an embodiment of the present disclosure. 
     In step S 502 , the method involves encoding a drug expression and at least one drug-ranking indicator to generate a first encoding variable. In step S 504 , the method involves training a generative adversarial network according to the first encoding variable to generate a plurality of candidate drugs, wherein each of the candidate drugs has a generative drug expression and at least one generative drug-ranking indicator. 
     In step S 506 , the method involves ranking strengths of the candidate drugs according to the generative drug-ranking indicator of each of the candidate drugs. In step S 508 , the method involves displaying the first encoding variables, the candidate drugs, the generative drug expressions, the generative drug-ranking indicators and strengths of the candidate drugs. In the embodiment, the drug expression and the generative drug expression respectively include a simplified molecular input line entry specification (SMILES) or a fingerprint, but the present disclosure is not limited thereto. In addition, the drug-ranking indicator and the generative drug-ranking indicator include a drug structure distribution or a pharmacological property. The drug structure distribution includes a number of nearest neighbor of a compound, and the pharmacological property includes IC50, GI, but the present disclosure is not limited thereto. Furthermore, the drug expression uniquely corresponds to a particular compound. 
       FIG.  6    is a detailed flowchart of step S 504  in  FIG.  5   . In step S 602 , the method involves generating a plurality of fake drug expressions and a plurality of fake drug-ranking indicators according to a random value. In step S 604 , the method involves encoding the fake drug expressions and fake drug-ranking indicators to generate a plurality of second encoding variables. 
     In step S 606 , the method involves receiving the first encoding variable and the second encoding variables, and comparing the first variable and second variables to generate determination values. 
     In step S 608 , the method involves determining whether the determination values approach a predetermined value. When determining that the determination values approach the predetermined value, the method performs step S 610 . In step S 610 , the method involves generating the candidate drugs according to the fake drug expressions and the fake drug-ranking indicators corresponding to the second encoding variables. 
     When determining that the determination values do not approach the predetermined value, the method performs step S 612 . In step S 612 , the method involves updating the fake drug expressions and the fake drug-ranking indicators. Then, after step S 612  is performed, the method goes to step S 606  to compare the drug expression and the drug-ranking indicator with the updated fake drug expressions and the updated fake drug-ranking indicators to generate determination values again. In the embodiment, the fake drug expressions respectively include a simplified molecular input line entry specification (SMILES) or a compound fingerprint, but the present disclosure is not limited thereto. In addition, the fake drug-ranking indicators respectively include a drug structure distribution or a pharmacological property. The drug structure distribution includes a number of nearest neighbor of the compound, and the pharmacological property includes IC50, GI, but the present disclosure is not limited thereto. 
     In summary, according to the drug-screening system and the drug-screening method of the embodiments of the present disclosure, the drug expression and the drug-ranking indicator are encoded to generate a first encoding variable. The generative adversarial network is trained according to the first encoding variable to generate a plurality of candidate drugs, wherein each of the candidate drugs has a generative drug expression and at least one generative drug-ranking indicator. The candidate drugs are ranked the generative drug-ranking indicator of each of the candidate drugs. Therefore, the embodiment of the present disclosure may effectively reduce the time and expense of drug development, and accelerate the development process. 
     While the disclosure has been described by way of example and in terms of the embodiments, it should be understood that the disclosure is not limited to the disclosed embodiments. On the contrary, it is intended to cover various modifications and similar arrangements (as would be apparent to those skilled in the art). Therefore, the scope of the appended claims should be accorded the broadest interpretation so as to encompass all such modifications and similar arrangements.