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The idea to use the information from protein structure to guide the design of new therapeutic agents stems from the 1950s and 1960s when the first protein structures were elucidated by X-ray crystallography \[[@B1],[@B2]\]. Notably, the pioneering work of John Kendrew and Max Perutz in solving the crystal structures of myoglobin and haemoglobin explained the oxygen-carrying/storing properties of these proteins and also shed light on the molecular basis of sickle cell anaemia and at the same time on potential treatments for this disease \[[@B3]\]. Similarly, the determination of the amino acid sequence of insulin by Fred Sanger, and its three-dimensional structure by Dorothy Hodgkin, were used to engineer slowly acting synthetic insulins for the treatment of diabetes \[[@B2]\]. These ground-breaking studies exemplify how therapeutic exploitation has always been closely linked to the fundamental understanding of protein structure and function. Importantly, in addition to valuable insights into protein folds, structure-function relationships and molecular evolution, the availability of the three-dimensional structures of therapeutically relevant proteins also allowed the identification and characterisation of potential inhibitor binding sites and formed the foundation for structure-based drug design (SBDD). In the 1980s, the field rapidly evolved with biotechnology companies such as Agouron Pharmaceuticals and Molecular Discovery Ltd, respectively, pursuing structure-guided programmes aimed at inhibition of thymidylate synthase for the treatment of cancer, and inhibition of viral neuraminidase to combat influenza \[[@B1]\]. Another early and influential example of this approach was the use of the structure of HIV protease \[[@B4],[@B5]\] in the design of four FDA-approved antiviral protease inhibitors (saquinavir, nelfinavir, indinavir and ritonavir) for the treatment of HIV/AIDS \[[@B1]\]. These discoveries in turn followed earlier work on inhibitors of the aspartic protease renin, which converts angiotensinogen into angiotensin I to regulate blood pressure, using molecular modelling based on structures of fungal pepsins \[[@B2]\]. In addition to enabling drug design, the exponential growth over the years in the number of macromolecular structures deposited in the Protein Data Bank (PDB), which at the time of writing stood at 133,920 ([Figure 1](#F1){ref-type="fig"}), also stimulated the development of sophisticated software packages such as GRID \[[@B6]\], LUDI \[[@B7]\], GOLD \[[@B8]\] and GLIDE \[[@B9]\] to facilitate 'docking' of inhibitors into their predicted binding sites as well as to computationally analyse inhibitor binding and inform on further enhancement. ![Yearly growth of structures in the Protein Data Bank\ Beige bars: total number of X-ray, NMR, electron microscopy and modelled structures in the PDB. Blue bars: total number of structures deposited per year. Source: RCSB Protein Data Bank: [www.rcsb.org/pdb/home/home.do](http://www.rcsb.org/pdb/static.do?p=general_information/pdb_statistics/index.htm)](ebc-61-ebc20170052-g1){#F1} In the late 1990s, SBDD continued to evolve and, in particular, was a key enabler of fragment-based drug design (FBDD). This approach has its roots in computational and structural biology research from the 1980s and 1990s with key inputs from basic and applied studies in academia and at companies such Abbott Laboratories, Astex Pharmaceuticals and Vernalis \[[@B10]--[@B14]\]. As we write, FBDD has yielded over 30 clinical drug candidates and also three FDA-approved drugs in oncology: the BRAF inhibitor vemurafenib, the BCL-2 inhibitor venetoclax, and the CDK4