Mapping of pharmacological space.

The analysis of pharmacological space is becoming highly relevant in light of the emerging polypharmacology paradigm, that is, the increasing evidence that many drugs elicit therapeutic effects and adverse drug reactions through interactions with multiple targets. To better understand desired and undesired polypharmacology and identify new targets for existing drugs, computational methods are of critical importance. Herein we provide an overview of computational approaches for analyzing pharmacological space and put their opportunities and limitations in perspective. Insights into computational approaches for the study of target-ligand interactions and polypharmacology are provided and put into scientific context. The interplay between computational and experimental approaches is rationalized. Computational methods have become indispensable tools for the systematic analysis of drug-target interactions. Because currently most prominent predictive methods are knowledge-based, they are affected by data bias and sparseness. Predictions of drug-target interactions are already carried out on a large scale, but experimentally validated to a much lesser extent. In order to demonstrate true utility of pharmacological space analysis for drug discovery, it will be essential to closely interface computational and experimental target profiling efforts.