Statistical learning approach for predicting specific pharmacodynamic, pharmacokinetic, or toxicological properties of pharmaceutical agents

Pharmaceutical agents have been developed and tested for possessing desirable pharmacodynamic, pharmacokinetic, and minimal level of toxicological properties. Computational methods have been explored for predicting these properties aimed at the discovery of promising leads and the elimination of unsuitable ones in early stages of drug development. Statistical learning methods have shown their potential for predicting these properties for structurally diverse sets of agents by using both conventional (quantitative structure-activity and structure-property relationships) and more recently explored (such as neural networks and support vector machines) statistical models. These methods have been used for predicting agents of a variety of pharmacodynamic (such as inhibitors or agonists of a therapeutic target), pharmacokinetic (such as P-glycoprotein substrates, human intestine absorption, and blood-brain barrier penetrating capabilities), and toxicological (such as genotoxicity) properties. The strategies, current progresses, and the underlying difficulties and future prospects of the application of the recently explored statistical learning methods are discussed.