Severity: Warning
Message: file_get_contents(https://...@pubfacts.com&api_key=b8daa3ad693db53b1410957c26c9a51b4908&a=1): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests
Filename: helpers/my_audit_helper.php
Line Number: 176
Backtrace:
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 176
Function: file_get_contents
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 250
Function: simplexml_load_file_from_url
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3122
Function: getPubMedXML
File: /var/www/html/application/controllers/Detail.php
Line: 575
Function: pubMedSearch_Global
File: /var/www/html/application/controllers/Detail.php
Line: 489
Function: pubMedGetRelatedKeyword
File: /var/www/html/index.php
Line: 316
Function: require_once
Drug resistance severely erodes the efficacy of therapeutic treatments for many diseases. Assessing the potency of a drug lead to combat resistance is no doubt critical for designing new drugs or new therapeutic combinations. Virtual screening is often the first step in drug discovery and a challenging problem is to accurately predict the resistant profile of an inhibitor based on the docking structures. Using a well studied system HIV-1 protease, we have illustrated the success of a computational method called MIEC-SVM on tackling this problem. We computed molecular interaction energy components (MIECs) between the ligand and the protease residues to characterize the docking poses, which were input to support vector machine (SVM) to distinguish resistant from nonresistant mutants. More importantly, the method is able to predict resistant profiles for new drugs based on the docking structures as indicated by its satisfactory performance in leave-one-drug-out and leave-drug/mutants-out tests. Therefore, the MIEC-SVM method can also facilitate designing effective therapeutic combinations by combining drugs with complementary resistant profiles.
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Source |
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http://dx.doi.org/10.1021/ci400011c | DOI Listing |
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