How to design multi-target drugs

TLDR

Despite advances in rational drug design and high‑throughput screening, the number of novel single‑target drugs has fallen short, whereas multi‑target drugs expand the druggable proteome by exploiting low‑affinity, network‑dependent effects that increase target numbers and reduce side effects. The authors aim to review recent progress, compare network attack strategies, and propose methods for selecting target sets for multi‑target drugs. They review the literature, evaluate network attack strategies, and propose computational approaches to identify target sets for multi‑target drugs.

Abstract

Despite improved rational drug design and a remarkable progress in genomic, proteomic and high-throughput screening methods, the number of novel, single-target drugs has fallen far behind expectations during the past decade. Multi-target drugs multiply the number of pharmacologically relevant target molecules by introducing a set of indirect, network-dependent effects. Parallel with this, the low-affinity binding of multi-target drugs eases the constraints of druggability and significantly increases the size of the druggable proteome. These effects tremendously expand the number of potential drug targets and introduce novel classes of multi-target drugs with smaller side effects and toxicity. Here, the authors review the recent progress in this field, compare possible network attack strategies and propose several methods to find target-sets for multi-target drugs.