Abstract
The development of quantitative structure-activity relationship (QSAR) models for computer-assisted drug design is a well-known technique in the pharmaceutical industry. QSAR models provide medicinal chemists with mechanisms for predicting the biological activity of compounds using their chemical structure or properties. This information can significantly reduce the time to discover a new drug. This work compares and contrasts particle swarms to simulated annealing and artificial ant systems techniques for the development of QSAR models based on artificial neural networks and k-nearest neighbor and kernel regression. Particle Swarm techniques are shown to compared favorably to the other techniques using three classical data sets from the QSAR literature.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
