Drug design: many tools for a many-faceted problem

Designing new drugs is a complex, multi-objective problem, requiring the simultaneous optimisation of target affinity, target tissue exposure, formulation, toxicity – the list goes on. Innovative drug designers are constantly identifying new methods they can use to improve their drug design capabilities to the extent that designers now have unprecedented access to data, tools and resources. The challenge faced by many is how to integrate these multiple inputs and opinions to maximise their effect and accelerate drug discovery projects into the clinic.

In this newsletter, four recent papers describe some of the state-of-the-art techniques being used to design new drugs, to solve the multi-objective problem and to coordinate the efforts of drug design teams for maximum benefit.

As technology and techniques improve it is becoming common to have access to a solved crystal structure of each protein target. In the case of anti-influenza drug design, Du et al. [1] illustrate the impact that structure-based design can have. The availability of protein crystal structures of most influenza proteins has enabled researchers to rationally locate binding sites and successfully identify potential lead molecules through virtual screening. Understanding the structure–function relationship of these proteins has highlighted new routes into inhibition of the influenza virus. Moreover, knowledge of where viral mutations frequently occur enables researchers to design drugs that bind in the conserved regions thus overcoming issues of drug resistance.

Overcoming drug resistance is also the theme of a review from Hao et al. [2]. Two main methods of predicting the effect of mutation on drug binding and protein function are described. Once more protein structure proves invaluable and together with molecular dynamics simulation, can be a powerful method for examining the effect of an amino acid mutation. More accurate prediction can be gained from statistical learning techniques, although these require a large training set of known mutations. A combination of these two techniques therefore is most promising. Having identified resistance mutations, structure-based design can be directed to avoid such problems, for example, by targeting interactions with the unchanging backbone atoms, highly conserved residues or through multi-target design.

Of course, affinity for the target protein (and commonly occurring mutations) is but one facet of a drug. Combining this with numerous other pharmaceutically important properties is the subject of a recent article. Nicolaou et al. [3] describe how multi-objective optimization methods are used with statistical modelling, docking, de novo design and library design to achieve simultaneous improvements against multiple parameters. This is especially challenging in non-continuous ‘chemistry-space’ (where small changes can have disproportionately large effects). One benefit of these methods is that they are capable of identifying multiple solution spaces, thus avoiding the ‘tunnel vision’ where drug designers get stuck in a local optimisation solution but remain ignorant of the superior global solution.

Clearly there is a wealth of techniques available to drug designers and numerous experts will contribute to drug design on a given target. One of the problems then encountered is how to integrate all these inputs in an easy and efficient manner, without overlooking a vital contribution. Robb et al. [4] describe a wiki software system for capturing expert inputs, knowledge and design ideas and sharing these in a simple and effective way, promoting discussion and collaboration. Traditional synthesis-led design is supplemented by automatic structure-based and chemoinformatics input. Analysis of how the system was used enabled scientists to remove the bottlenecks of the drug design process and improve turnaround time for the realisation of each design idea.

The field of drug design is constantly evolving, incorporating new techniques and tools. Only by taking up these new techniques and making the maximum use of all available data and knowledge will drug designers continue to find new drugs and make a meaningful difference to patient lives.

Graeme Robb is Associate Principle Scientist at AstraZeneca, with over 10 years of experience in computer-aided drug-design. He completed his doctorate in physical chemistry at the University of Edinburgh in 2002 and began work with AstraZeneca that same year. He has worked for a number of years in treatments for diabetes and obesity and more recently has transferred to the Oncology Innovative Medicines Unit. He now works as a drug designer on a diverse range of cancer targets. His role employs structural and chemoinformatic modelling of molecules and proteins to design new drug-like compounds and predict their properties.
[1]    Du, J. et al. (2012) Recent progress in structure-based anti-influenza drug design. Drug Discov. Today, 17, 1111–1120
[2]    Hao, G. et al. (2012) Structure-based methods of predicting target mutation-induced drug resistance and rational drug design to overcome the problem. Drug Discov. Today 17, 1121–1126
[3]            Nicolaou, C.A. and Brown, N. (2013) Multi-objective optimization methods in drug design. Drug Discov. Today: Technol. in press (DOI:
[4]    Robb, G.R. et al.  (2013) A chemistry wiki to facilitate and enhance compound design in drug discovery. Drug Discov. Today 18, 141–147

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