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
Drug-likeness is a frequently invoked, although not always precisely defined, concept in drug discovery. Opinions on drug-likeness are to a large extent shaped by the relationships that are observed between surrogate measures of drug-likeness (e.g. aqueous solubility; permeability; pharmacological promiscuity) and fundamental physicochemical properties (e.g. lipophilicity; molecular size). This article draws on examples from the literature to highlight approaches to data analysis that exaggerate trends in data and the term correlation inflation is introduced in the context of drug discovery. Averaging groups of data points prior to analysis is a common cause of correlation inflation and results from analysis of binned continuous data should always be treated with caution.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1007/s10822-012-9631-5 | DOI Listing |
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!