Skip to main content

How to Generate Reliable and Predictive CoMFA Models

Buy Article:

$63.00 plus tax (Refund Policy)


Comparative Molecular Field Analysis (CoMFA) is a mainstream and down-to-earth 3D QSAR technique in the coverage of drug discovery and development. Even though CoMFA is remarkable for high predictive capacity, the intrinsic data-dependent characteristic still makes this methodology certainly be handicapped by noise. It's well known that the default settings in CoMFA can bring about predictive QSAR models, in the meanwhile optimized parameters was proven to provide more predictive results. Accordingly, so far numerous endeavors have been accomplished to ameliorate the CoMFA model's robustness and predictive accuracy by considering various factors, including molecular conformation and alignment, field descriptors and grid spacing. Herein, we would like to make a comprehensive survey of the conceivable descriptors and their contribution to the CoMFA model's predictive ability.

Keywords: CoMFA; Coulombic potentials; alignment; conformation; fields; grid spacing; predictive QSAR models; probe atom; receptor-ligand interactions; steric

Document Type: Research Article


Publication date: February 1, 2011

More about this publication?
  • Current Medicinal Chemistry covers all the latest and outstanding developments in medicinal chemistry and rational drug design. Each issue contains a series of timely in-depth reviews written by leaders in the field covering a range of the current topics in medicinal chemistry. Current Medicinal Chemistry is an essential journal for every medicinal chemist who wishes to be kept informed and up-to-date with the latest and most important developments.

Access Key

Free Content
Free content
New Content
New content
Open Access Content
Open access content
Partial Open Access Content
Partial Open access content
Subscribed Content
Subscribed content
Free Trial Content
Free trial content
Cookie Policy
Cookie Policy
Ingenta Connect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more