Artificial neural networks (ANNs) and other machine learning algorithms are extensively used in many aspects of drug design and delivery. The concepts of target validation in drug design and the concepts of drug delivery are discussed. The role of ANNs in target validation is discussed, including their capabilities and limitations. Examples of the uses of ANNs in target discovery, target validation, and hit evaluation are given. The uses of ANNs in pharmacokinetics and pharmacodynamics are discussed and examples are given for multiple aspects of absorption, distribution, metabolism, and elimination as well as toxicity. Applications of ANNs' drug delivery are discussed, including in vivo-in vitro correlation, quantitative structure-property relationship modeling, preformulation, and formulation, with the latter two topics examined in depth. It is concluded that ANNs can be of use whenever data are incomplete and/or generalization from a set of examples is required.
|Original language||English (US)|
|Title of host publication||Artificial Neural Network for Drug Design, Delivery and Disposition|
|Number of pages||13|
|State||Published - Jan 1 2016|
All Science Journal Classification (ASJC) codes
- Pharmacology, Toxicology and Pharmaceutics(all)