BACKGROUND

Polypharmacology, defined as the modulation of multiple proteins rather than a single target to achieve a desired therapeutic effect, has been gaining increasing attention since 1990s, when industries had to withdraw several drugs due to their adverse effects, leading to permanent injuries or death, with multi-billiondollar legal damages. Therefore, if up to then the "one drug one target" paradigm had seen many researchers interest focused on the identification of selective drugs, with the strong expectation to avoid adverse drug reactions (ADRs), very recently new research strategies resulted more appealing even as attempts to overcome the decline in productivity of the drug discovery industry.

METHODS

Polypharmacology consists of two different approaches: the former, concerning a single drug interacting with multiple targets related to only one disease pathway; the latter, foresees a single drug's action on multiple targets involved in multiple disease pathways. Both new approaches are strictly connected to the discovery of new feasible off targets for approved drugs.

RESULTS

In this review, we describe how the in silico facilities can be a crucial support in the design of polypharmacological drug. The traditional computational protocols (ligand based and structure based) can be used in the search and optimization of drugs, by using specific filters to address them against the polypharmacology (fingerprints, similarity, etc.). Moreover, we dedicated a paragraph to biological and chemical databases, due to their crucial role in polypharmacology.

CONCLUSION

Multitarget activities provide the basis for drug repurposing, a slightly different issue of high interest as well, which is mostly applied on a single target involved in more than one diseases. In this contest, computational methods have raised high interest due to the reached power of hardware and software in the manipulation of data.