Editorial [Hot Topic:Computational Medicinal Chemistry (Guest Editor: Hugo O. Villar)]

Computational chemistry has become a pervasive tool for the medicinal chemistry work because the entire process of drug discovery is becoming without question information intensive. The challenge for the chemist today is to navigate through the deluge of information, select compounds for synthesis and create the most informative series of analogs that can be made to optimize the chemical series. Even when knowledgeable scientists may be available to determine the priority of compounds for synthesis, the decision process could be laborious, particularly when large datasets have to be considered. Predominantly, expertise is scarce or unevenly distributed, particularly at the on-set of new projects. The way in which this data intensive paradigm for drug discovery presents a challenge for traditional chemoinformatics has been given considerable attention [1] and present a significant opportunity for research [2]. In very broad terms, computational chemistry techniques are used for modeling or data mining and management. There is some convergence between the two. In recent past, two areas in modeling have received the most attention. One is the problem of virtual screening, which is easy to understand, generates considerable interest. The other is the modeling of metabolic and pharmacokinetic processes. Accurate prediction of binding affinity and binding mode are crucial in drug design, as decisions as to which compounds or compound libraries to evaluate next as a lead evolves towards a drug candidate. Some reviews provide a good overview of the state of the art in these techniques, indicating their limitations and applicability [3-7]. The different algorithms and scoring functions to rank docked compounds have also been extensively reviewed. [8-10], and docking techniques are generating a list of successes that can be impressive depending on the target class [11]. However, efforts to compare the outcomes of the different techniques are sparse. [12-15]. Another area of modeling that has elicited considerable interest in the last few years is the prediction of toxicological, pharmacokinetic or other properties related to preclinical development. [16-19]. Also metabolism with its multiple implications for drug-drug interactions receives continuous attention by the computational teams. [20, 21]. The shift observed in the way in which early drug discovery is carried out has spurred significant activity in this area. Predictions are used to tailor libraries and prioritize compounds. From the calculation of octanol/water partition coefficients to define pharmacokinetic characteristics to modeling of complex biological processes predictions are becoming ubiquitous and means to prioritize directions. Data management and mining issues are likely to increase in complexity for the chemist and new generations of tools are needed to aid in what was done manually in the past. Aligning experimental and in-silico techniques has been an on-going goal [22], but systems biology and high content screening are likely to increase the amount of information handled by even the simplest of projects [23,24]. and seems the most effective way to go in some therapeutic indications [25]. The integration of chemical data with bioinformatics is likely to add yet another dimension to the complexity of the data sets to be handled [26] and the data to be analyzed [27,28]. The breadth of the developments in computational techniques applied to drug discovery is extremely wide and it would not be possible to cover them in a single issue. The areas we chose to cover in this issue are only a small fraction of the innovative computational techniques recently put forward to aid in drug design. We decided to provide a sampling of the breadth in computational applications covering some areas less frequently addressed in review form. We start with coverage of sources of information for the medicinal chemist. Ertl and Jelfs provide an overview of tools available on the internet that can be of use to medicinal chemists, while Southan, Varkonyi and Muresan contrast public and commercial sources of chemical information. Villar and Hansen review how computational techniques are applied to an alternative paradigm for drug discovery, namely fragment based drug design.....