%0 Journal Article
%T An interview with Philip Biggin, Section Editor for Computational, in silico and modelling studies
%A Philip C Biggin
%J BMC Pharmacology and Toxicology
%D 2012
%I BioMed Central
%R 10.1186/2050-6511-13-2
%X I became interested in chemistry and computers as a teenager which led to my decision to read Computer-aided Chemistry for my first degree. During that time I spent a fascinating industrial year at SmithKline Beecham (now GSK) developing software to aid the computational chemistry group. Although at that time I did not know I wanted to do a PhD, I did know that I wanted to continue investigating the way in which drugs work. I then undertook a PhD in the biophysics of ion channels, which helped fill in some of the biological gaps in my knowledge. The rest is history as they say.Computational chemistry and molecular modelling have been embedded within the drug-design process for many years now and indeed most pharmaceutical companies have some kind of computational chemistry group within their Research and Development programmes. From early quantitative structure activity relationship (QSAR) studies and visualization through to cheminformatics and molecular dynamics, the scope and power of these techniques is ever increasing and they are starting to yield important results that are not only interestingly academically, but can result in substantial financial savings for drug companies.One of the major challenges for computational chemists remains the prediction of affinity for small molecules with protein (and nucleic acid) targets. There have been many approaches to this ranging from thermodynamically rigorous free energy calculations through to machine learning methods. Although there have been examples of good success, the prediction of absolute affinities remains a major challenge for the field. If the prediction of affinity was not challenging enough, the next problem will be the prediction of efficacy. Just because a compound binds with high affinity does not necessarily mean it has high efficacy. In fact the problem may be even worse than that, because it is becoming apparent that some systems exhibit 'agonist bias' whereby the efficacy of the same agonist is no
%U http://www.biomedcentral.com/2050-6511/13/2