Computation, experiment and molecular design

Abstract

Perhaps the best way to start a Perspective like this is to say what we mean by ‘Molecular Design’ and one definition is control of properties and behaviour of compounds and materials through manipulation of molecular properties [1]. Molecular interactions [2] occupy a special place in the framework of Molecular Design because the functional behaviour of a compound is determined by the strengths of the interactions of its molecules with the different environments (e.g. solution; crystal lattice; bound to protein) in which they exist [1]. Molecular Design can be hypothesisdriven or prediction-driven and the objectives of Computer-Aided Molecular Design (CAMD) are to provide physicochemical insight for framing design hypotheses and to build predictive models with which to evaluate compounds. Energy, geometry and connectivity form the basis of the tools used in CAMD. For example, a method for predicting crystal structure might use geometry to pack molecules into prospective unit cells and energy to rank the resulting crystal lattices. The molecular connection table is the defining cheminformatic data structure and we can use connectivity to infer physicochemical characteristics such as interaction potential (e.g. strong hydrogen bond acceptor) and likelihood of being ionised in solution. From the CAMD perspective, Drug Discovery is a process in two steps. Lead Identification is a search for active compounds that can be optimised in the second step to compounds with properties and behaviour consistent with dosing in humans as therapeutic agents. Although the second step is frequently described as a process of multiobjective optimisation, it can be argued that lead optimisation actually has minimisation of therapeutic dose as its principal, if not sole, objective. The last 25 years have seen adoption of a number of technologies by pharmaceutical and biotechnology industries and much CAMD activity is a reaction to these developments. Typically, each new technology is introduced with the promise that it will revolutionise Drug Discovery and much of the hyperbole spills over into the CAMD arena. Some of the difficulty that CAMD scientists face in gaining acceptance for their approaches can be traced to extravagant claims made earlier by other CAMD scientists. Assessing the value added by a new technology is not always easy. When a pharmaceutical company spends a large amount of money to acquire new capability, it is in the interests of both vendor and customer that purchases and collaborations are seen in the most favourable light. Over-selling of technologies leads to panacea-centric thinking, which is especially dangerous in CAMD because success frequently depends on bringing together diverse computational tools to both define and solve problems. One important lesson from the last 25 years of Drug Discovery is that technology is a good servant but a poor master. The software tools of CAMD are in many cases interconnected and the CAMD scientist must recognise these connections in order to exploit them. I will attempt to illustrate this point with reference to computational methods for identifying compounds with interesting biological activity, which has become known as virtual screening. Pharmacophore searching, molecular shape-matching and docking are essentially geometry-based approaches to virtual screening. The first two of these are ligand-based and complement each other in virtual screening campaigns. Despite algorithmic differences, pharmacophore searching and shape-matching are actually more similar than first appearances might suggest in that each method aligns hit molecules with the respective query. We can rank P. W. Kenny (&) Macclesfield, UK e-mail: pwk.pub.2008@gmail.com