Premium
4D‐QSPR Analysis and Virtual Screening in Materials Science
Author(s) -
Duca J. S.,
Tseng Y.F.,
Hopfinger A. J.
Publication year - 2001
Publication title -
advanced materials
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 10.707
H-Index - 527
eISSN - 1521-4095
pISSN - 0935-9648
DOI - 10.1002/1521-4095(200111)13:22<1713::aid-adma1713>3.0.co;2-c
Subject(s) - quantitative structure–activity relationship , virtual screening , construct (python library) , computer science , drug discovery , computational biology , biochemical engineering , machine learning , bioinformatics , biology , engineering , programming language
There are a large number of applications in materials science that possess the general features and properties of the drug–receptor interaction that is at the center of the pharmaceutical sciences. Computer‐assisted molecular design methods have been developed to analyze and extract information from a series of drug‐candidates that bind to a common receptor. These computational methods can be used to construct quantitative structure–activity relationships, QSARs. QSARs, in turn, can be used to probe drug–receptor mechanisms of action, and to perform virtual screening of virtual drug‐candidates, which streamlines the drug discovery process. The question arises if QSAR methods can also be applied to materials science problems to construct quantitative structure–property relationships, QSPRs. One QSAR method, called receptor‐independent 4D‐QSAR analysis, seems particularly well suited to develop QSPRs in materials science applications.