In the big data era, voluminous datasets are routinely acquired, stored and analyzed with the aim to inform biomedical discoveries and validate hypotheses. No doubt, data volume and diversity have dramatically increased by the advent of new technologies and open data initiatives. Big data are used across the whole drug discovery pipeline from target identification and mechanism of action to identification of novel leads and drug candidates. Such methods are depicted and discussed, with the aim to provide a general view of computational tools and databases available. We feel that big data leveraging needs to be cost-effective and focus on personalized medicine. For this, we propose the interplay of information technologies and (chemo)informatic tools on the basis of their synergy.

There is a need for novel drugs for the treatment of infectious diseases, autoimmunity and cancer. Cyclic peptides constitute a class of compounds that have made crucial contributions to the treatment of certain diseases. Penicillin, Vancomycin, Cyclosporin, the Echinocandins and Bleomycin are well-known cyclic peptides. Cyclic peptides, compared to linear peptides, have been considered to have greater potential as therapeutic agents due to their increased chemical and enzymatic stability, receptor selectively, and improved pharmacodynamic properties. They have been used as synthetic immunogens, transmembrane ion channels, antigens for Herpes Simplex Virus, potential immunotherapeutic vaccines for diabetes and Experimental Autoimmune Encephalomyelitis - an animal model of Multiple Sclerosis, as inhibitors against α-amylase and as protein stabilizers. Herein, we review important cyclic peptides as therapeutic agents in disease.