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: 1034
Function: getPubMedXML
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3152
Function: GetPubMedArticleOutput_2016
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
Chemical genomics approaches are evolving to overcome key problems limiting the efficiency of drug discovery in the postgenomic era. Many of these stem from the low success rates in finding drugs for novel genomics targets whose biochemical properties and therapeutic relevance is poorly understood. The fundamental objective of chemical genomics is to find and optimize chemical compounds that can be used to directly test the therapeutic relevance of new targets revealed through genome sequencing. An integrated approach to chemical genomics encompasses a diverse set of tools including quantitative affinity-based screens, computer-directed combinatorial chemistry, and structure-based drug design. The approach is most effectively applied across targets classes whose members are structurally related, and where some members are known to have bona fide therapeutic relevance.
Download full-text PDF |
Source |
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http://dx.doi.org/10.1517/phgs.4.3.257.22692 | DOI Listing |
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