Applications of machine learning and computational intelligence to drug discovery and development
Year: 2010
Type: article
Abstract: In silico modeling of ADMET property models with QSAR and QSPR models has proven to be an effective approach for increasing the efficiency of small molecule drug discovery and development processes. Development of new, improved models and techniques is currently an active area of research. In recent years, there has been growing interest in adapting tools and techniques from the fields of computational intelligence and machine learning for use in drug discovery and development. This report reviews some of the more popular applications. Drug Dev Res 72: 53–65, 2011. © 2010 Wiley‐Liss, Inc.
Source: Drug Development Research
DOI: 10.1002/ddr.20402
Author: David Hecht
Institution: Southwestern College - California
Language: English
Subfield: Computational Theory and Mathematics
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W2116728999