Recent trends in drug-likeness prediction: A comprehensive review of <i> In silico</i> methods

The low success rate of converting lead compounds into drugs owing to unfavorable pharmacokinetic parameters has evoked a renewed interest in understanding more clearly what makes a compound drug-like. This article reviews a number of computational techniques for identifying drug-like molecules, ranging from simple counting schemes to sophisticated machine learning techniques such as neural networks, along with their application and challenges.