Closed loop continuous aptamer development system

Abstract

The present disclosure relates to a closed loop aptamer development system that identifies one or more aptamers observed experimentally and implements machine-learning models to identify other aptamers not observed experimentally. Particularly, aspects of the present disclosure are directed to receiving a query concerning one or more targets, acquiring a library of aptamers that potential satisfy the query, identifying a first set of aptamers from the library of aptamers that substantially or completely satisfy the query, obtaining sequence data for the first set of aptamers, generating, by a prediction model, a third set of aptamers derived from the sequence data for the first set of aptamers, validating the third set of aptamers that substantially or completely satisfy the query, and upon validating the third set of aptamers and in response to the query, providing the third set of aptamers as a result to the query.

What is claimed is: 
     
         1 . A computer-implemented method comprising:
 receiving a query concerning one or more targets;   acquiring a library of aptamers that potential satisfy the query;   identifying a first set of aptamers from the library of aptamers that substantially or completely satisfy the query and a second set of aptamers from the library of aptamers that does not substantially or completely satisfy the query;   obtaining sequence data for the first set of aptamers;   generating, by a prediction model, a third set of aptamers derived from the sequence data for the first set of aptamers;   validating the third set of aptamers that substantially or completely satisfy the query; and   upon validating the third set of aptamers and in response to the query, providing the third set of aptamers as a result to the query.   
     
     
         2 . The computer-implement method of  claim 1 , wherein the providing the result to the query further comprising providing the third set of aptamers and the first set of aptamers as the result to the query. 
     
     
         3 . The computer-implement method of  claim 1 , further comprising obtaining sequence data for the second set of aptamers, wherein the third set of aptamers is generated as being derived from the sequence data for first set of aptamers and the sequence data for the second set of aptamers. 
     
     
         4 . The computer implemented method of  claim 1 , further comprising recording the third set of aptamers in a data structure in association with the one or more targets. 
     
     
         5 . The computer-implement method of  claim 1 , further comprising obtaining analysis data for the first set of aptamers, and wherein the third set of aptamers are generated as being derived from the sequence data for first set of aptamers and the analysis data. 
     
     
         6 . The computer implemented method of  claim 5 , further comprising:
 predicting, by another prediction model, an analysis for each aptamer of the third set of aptamers derived from the sequence data for first set of aptamers and the analysis data for the first set of aptamers; and   recording the third set of aptamers in a data structure in association with the one or more targets and the predicted analysis for each aptamer of the third set of aptamers.   
     
     
         7 . The computer implemented method of  claim 6 , wherein the analysis data for the first set of aptamers includes a binary classifier or a multiclass classifier selected based on the query, and the predicted analysis for the third set of aptamers includes the binary classifier or the multiclass classifier. 
     
     
         8 . The computer implemented method of  claim 7 , wherein: (i) the binary classifier indicates that each aptamer from the first set of aptamers functionally inhibited the one or more targets, functionally did not inhibit the one or more targets, bound to the one or more targets, or did not bound to the one or more targets, or (ii) the multiclass classifier indicates a level of functional inhibition or a gradient scale for binding affinity with respect to each aptamer from the first set of aptamers and the one or more targets. 
     
     
         9 . The computer-implement method of  claim 1 , further comprising obtaining count data for the first set of aptamers, wherein the count data for the first set of aptamers indicates a count of each aptamer within the first set of aptamers, and wherein the third set of aptamers are generated as being derived from the first set of aptamers and the count data. 
     
     
         10 . The computer implemented method of  claim 9 , further comprising:
 predicting, by another prediction model, a count for each aptamer of the third set of aptamers derived from the sequence data for the first set of aptamers and the count data for the first set of aptamers; and   recording the third set of aptamers in a data structure in association with the one or more targets and the predicted count for each aptamer of the third set of aptamers.   
     
     
         11 . A computer-program product tangibly embodied in a non-transitory machine-readable storage medium, including instructions configured to cause one or more data processors to perform processing comprising:
 receiving a query concerning one or more targets;   acquiring a library of aptamers that potential satisfy the query;   identifying a first set of aptamers from the library of aptamers that substantially or completely satisfy the query and a second set of aptamers from the library of aptamers that does not substantially or completely satisfy the query;   obtaining sequence data for the first set of aptamers;   generating, by a prediction model, a third set of aptamers derived from the sequence data for the first set of aptamers;   validating the third set of aptamers that substantially or completely satisfy the query; and   upon validating the third set of aptamers and in response to the query, providing the third set of aptamers as a result to the query.   
     
     
         12 . The computer-program product of  claim 11 , wherein the processing further comprises obtaining analysis data for the first set of aptamers, and wherein the third set of aptamers are generated as being derived from the sequence data for first set of aptamers and the analysis data. 
     
     
         13 . The computer-program product of  claim 12 , wherein the processing further comprises:
 predicting, by another prediction model, an analysis for each aptamer of the third set of aptamers derived from the sequence data for first set of aptamers and the analysis data for the first set of aptamers; and   recording the third set of aptamers in a data structure in association with the one or more targets and the predicted analysis for each aptamer of the third set of aptamers.   
     
     
         14 . The computer-program product of  claim 13 , wherein the analysis data for the first set of aptamers includes a binary classifier or a multiclass classifier selected based on the query, and the predicted analysis for the third set of aptamers includes the binary classifier or the multiclass classifier. 
     
     
         15 . The computer-program product of  claim 14 , wherein: (i) the binary classifier indicates that each aptamer from the first set of aptamers functionally inhibited the one or more targets, functionally did not inhibit the one or more targets, bound to the one or more targets, or did not bound to the one or more targets, or (ii) the multiclass classifier indicates a level of functional inhibition or a gradient scale for binding affinity with respect to each aptamer from the first set of aptamers and the one or more targets. 
     
     
         16 . A system comprising:
 one or more data processors; and   a non-transitory computer readable storage medium containing instructions which, when executed on the one or more data processors, cause the one or more data processors to perform processing comprising:   receiving a query concerning one or more targets;   acquiring a library of aptamers that potential satisfy the query;   identifying a first set of aptamers from the library of aptamers that substantially or completely satisfy the query and a second set of aptamers from the library of aptamers that does not substantially or completely satisfy the query;   obtaining sequence data for the first set of aptamers;   generating, by a prediction model, a third set of aptamers derived from the sequence data for the first set of aptamers;   validating the third set of aptamers that substantially or completely satisfy the query; and upon validating the third set of aptamers and in response to the query, providing the third set of aptamers as a result to the query   
     
     
         17 . The system of  claim 16 , wherein the processing further comprises obtaining analysis data for the first set of aptamers, and wherein the third set of aptamers are generated as being derived from the sequence data for first set of aptamers and the analysis data. 
     
     
         18 . The system of  claim 17 , wherein the processing further comprises:
 predicting, by another prediction model, an analysis for each aptamer of the third set of aptamers derived from the sequence data for first set of aptamers and the analysis data for the first set of aptamers; and   recording the third set of aptamers in a data structure in association with the one or more targets and the predicted analysis for each aptamer of the third set of aptamers.   
     
     
         19 . The system of  claim 18 , wherein the analysis data for the first set of aptamers includes a binary classifier or a multiclass classifier selected based on the query, and the predicted analysis for the third set of aptamers includes the binary classifier or the multiclass classifier. 
     
     
         20 . The system of  claim 19 , wherein: (i) the binary classifier indicates that each aptamer from the first set of aptamers functionally inhibited the one or more targets, functionally did not inhibit the one or more targets, bound to the one or more targets, or did not bound to the one or more targets, or (ii) the multiclass classifier indicates a level of functional inhibition or a gradient scale for binding affinity with respect to each aptamer from the first set of aptamers and the one or more targets.