'Drug-likeness', a qualitative property of chemicals assigned by experts committee vote, is widely integrated into the early stages of lead and drug discovery. Its conceptual evolution paralleled work related to Pfizer's 'rule of five' and lead-likeness, and is placed within this framework. The discrimination between 'drugs' (represented by a collection of pharmaceutically relevant small molecules, some of which are marketed drugs) and 'nondrugs' (typically, chemical reagents) is possible using a wide variety of statistical tools and chemical descriptor systems. Here we summarize 18 papers focused on drug-likeness, and provide a comprehensive overview of progress in the field. Tools that estimate drug-likeness are valuable in the early stages of lead discovery, and can be used to filter out compounds with undesirable properties from screening libraries and to prioritize hits from primary screens. As the goal is, most often, to develop orally available drugs, it is also useful to optimize drug-like pharmacokinetic properties. We examine tools that evaluate drug-likeness and some of their shortcomings, challenges facing these tools, and address the following issues: What is the definition of drug-likeness and how can it be utilized to reduce attrition rate in drug discovery? How difficult is it to distinguish drugs from nondrugs? Are nondrug datasets reliable? Can we estimate oral drug-likeness? We discuss a drug-like filter and recent advances in the prediction of oral drug-likeness. The heuristic aspect of drug-likeness is also addressed.
|Original language||American English|
|Number of pages||22|
|Journal||Wiley Interdisciplinary Reviews: Computational Molecular Science|
|State||Published - Sep 2011|