Druglikeness is a key consideration when selecting compounds during the early stages of drug discovery. However, evaluation of druglikeness in absolute terms does not adequately reflect the whole spectrum of compound quality. More worryingly, widely used rules may inadvertently foster undesirable molecular property inflation as they permit the encroachment of rule-compliant compounds toward their boundaries. We propose a measure of druglikeness based on the concept of desirability called Quantitative Estimate of Druglikeness (QED). The empirical rationale of QED reflects the underlying distribution of molecular properties. QED is intuitive, transparent, straightforward to implement in many practical settings and allows compounds to be ranked by their relative merit. We extend the utility of QED by applying it to the problem of molecular target druggability assessment by prioritizing a large set of published bioactive compounds. The measure may also capture the abstract notion of aesthetics in medicinal chemistry.The concept of druglikeness provides useful guidelines for early stage drug discovery 1, 2 . Analysis of the observed distribution of some key physicochemical properties of approved drugs, including molecular weight, hydrophobicity and polarity, reveals they preferentially occupy a relatively narrow range of possible values 3 . Compounds that fall within this range are described as "druglike." Note that this definition holds in the absence of any obvious structural similarity to an approved drug. It has been shown that preferential selection of druglike compounds increases the likelihood of surviving the well-documented high rates of attrition in drug discovery 4 .Druglikeness can be rationalized by consideration of how simple physicochemical properties impact molecular behavior in vivo, with particular respect to solubility, permeability, metabolic stability and transporter effects. Indeed druglikeness is often used as a proxy for Correspondence should be addressed to A.L.H. (a.hopkins@dundee.ac.uk).. Additional InformationSupplementary information is available online at XXXX. We have implemented QED as simple functions in Python, SQL (Structure Query Language), Accelrys Pipeline Pilot and Microsoft Excel, the codes for which are available in the Supplementary Information. The Microsoft Excel example also includes data on the 771 oral drugs used to derive the desirability functions. Pre-calculated QED values and desirability functions for 657,736 compounds from ChEMBL (release ChEMBL09) are also available. Author contributions Europe PMC Funders Author ManuscriptsEurope PMC Funders Author Manuscripts oral bioavailability. However, druglikeness provides a broad composite descriptor that implicitly captures several criteria, with bioavailability amongst the most prominent.In practical terms, assessment of druglikeness is most commonly manifested as rules, the original and most well known of which is Lipinski's Rule of Five (Ro5) 5 . The rule states that a compound is more likely to exhibit poor a...

Natural products (NPs) are a rich source of novel compound classes and new drugs. In the present study we have used the chemical space navigation tool ChemGPS-NP to evaluate the chemical space occupancy by NPs and bioactive medicinal chemistry compounds from the database WOMBAT. The two sets differ notable in coverage of chemical space, and tangible lead-like NPs were found to cover regions of chemical space that lack representation in WOMBAT. Property based similarity calculations were performed to identify NP neighbours of approved drugs. Several of the NPs revealed by this method, were confirmed to exhibit the same activity as their drug neighbours. The identification of leads from a NP starting point may prove a useful strategy for drug discovery, in the search for novel leads with unique properties.

The 'quality' of small-molecule drug candidates, encompassing aspects including their potency, selectivity and ADMET (absorption, distribution, metabolism, excretion and toxicity) characteristics, is a key factor influencing the chances of success in clinical trials. Importantly, such characteristics are under the control of chemists during the identification and optimization of lead compounds. Here, we discuss the application of computational methods, particularly quantitative structure-activity relationships (QSARs), in guiding the selection of higher-quality drug candidates, as well as cultural factors that may have affected their use and impact.

Natural compounds are evolutionary selected and prevalidated by Nature, displaying a unique chemical diversity and a corresponding diversity of biological activities. These features make them highly interesting for studies of chemical biology, and in the pharmaceutical industry for development of new leads. Of utmost importance, for the discovery of new biologically active compounds, is the identification and charting of the corresponding biologically relevant chemical space. The primary key to this is the coverage of the natural products' chemical space. Here we introduce ChemGPS-NP, a new tool tuned for handling the chemical diversity encountered in natural products research, in contrast to previous tools focused on the much more restricted drug-like chemical space. The aim is to provide a framework for making compound classification and comparison more efficient and stringent, to identify volumes of chemical space related to particular biological activities, and to track changes in chemical properties due to, for example, evolutionary traits and modifications in biosynthesis. Physical-chemical properties not directly discernible from structural data can be discovered, making selection more efficient and increasing the probability of hit generation when screening natural compounds and analogues.

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Internet has become a central source for information, tools, and services facilitating the work for medicinal chemists and drug discoverers worldwide. In this paper we introduce a web-based public tool, ChemGPS-NP(Web) (http://chemgps.bmc.uu.se), for comprehensive chemical space navigation and exploration in terms of global mapping onto a consistent, eight dimensional map over structure derived physico-chemical characteristics. ChemGPS-NP(Web) can assist in compound selection and prioritization; property description and interpretation; cluster analysis and neighbourhood mapping; as well as comparison and characterization of large compound datasets. By using ChemGPS-NP(Web), researchers can analyze and compare chemical libraries in a consistent manner. In this study it is demonstrated how ChemGPS-NP(Web) can assist in interpreting results from two large datasets tested for activity in biological assays for pyruvate kinase and Bcl-2 family related protein interactions, respectively. Furthermore, a more than 30-year-old suggestion of "chemical similarity" between the natural pigments betalains and muscaflavins is tested.

Addressing drug-like/lead-like properties of biologically active small molecules early in a lead generation program is the current paradigm within the drug discovery community. Lipinski's "rule of five" has become the most commonly used tool to assess the relationship between structures and drug-like properties. Sixty percent of the 126 140 unique compounds in The Dictionary of Natural Products had no violations of Lipinski's "rule of five". We have isolated 814 natural products based on their expected drug-like/lead-like properties to generate a natural product library (NPL) in which 85% of the isolated compounds had no Lipinski violations. The library demonstrates the feasibility of obtaining natural products known for rich chemical diversity with the required physicochemical properties for drug discovery. The knowledge generated in creation of the library of structurally characterized pure natural products may provide opportunities to front-load lead-like property space in natural product drug discovery programs.

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