The dominant paradigm in drug discovery is the concept of designing maximally selective ligands to act on individual drug targets. However, many effective drugs act via modulation of multiple proteins rather than single targets. Advances in systems biology are revealing a phenotypic robustness and a network structure that strongly suggests that exquisitely selective compounds, compared with multitarget drugs, may exhibit lower than desired clinical efficacy. This new appreciation of the role of polypharmacology has significant implications for tackling the two major sources of attrition in drug development--efficacy and toxicity. Integrating network biology and polypharmacology holds the promise of expanding the current opportunity space for druggable targets. However, the rational design of polypharmacology faces considerable challenges in the need for new methods to validate target combinations and optimize multiple structure-activity relationships while maintaining drug-like properties. Advances in these areas are creating the foundation of the next paradigm in drug discovery: network pharmacology.

For the past decade, the number of molecular targets for approved drugs has been debated. Here, we reconcile apparently contradictory previous reports into a comprehensive survey, and propose a consensus number of current drug targets for all classes of approved therapeutic drugs. One striking feature is the relatively constant historical rate of target innovation (the rate at which drugs against new targets are launched); however, the rate of developing drugs against new families is significantly lower. The recent approval of drugs that target protein kinases highlights two additional trends: an emerging realization of the importance of polypharmacology, and also the power of a gene-family-led approach in generating novel and important therapies.

An assessment of the number of molecular targets that represent an opportunity for therapeutic intervention is crucial to the development of post-genomic research strategies within the pharmaceutical industry. Now that we know the size of the human genome, it is interesting to consider just how many molecular targets this opportunity represents. We start from the position that we understand the properties that are required for a good drug, and therefore must be able to understand what makes a good drug target.

No abstract

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...

Summary Ligand efficiency measures quantify the molecular properties, particularly size and lipophilicity, of small molecules that are required to gain binding affinity to a drug target. For example, ligand efficiency, is the binding free energy per heavy atom count (LE = G/HA) and lipophilic ligand efficiency (LLE = pIC 50 or KicLogP/D). There are additional efficiency measures for groups in a molecule, and for combinations of size and lipophilicity. The application of ligand efficiency metrics has been widely reported in the selection and optimisation of fragments, hits, and leads. In particular, optimisation of lipophilic ligand efficiency shows that it is possible to increase affinity and reduce lipophilicity at the same time, even with challenging 'lipophile-preferring' targets. Mean ligand efficiency measures of molecules acting at a specific target, when combined with their drug-like physical properties, is a practical means of estimating target 'druggability.' This is exemplified with 480 targetassay pairs from the primary literature. Across these targets correlations between biological activity in vitro and physical properties are generally weak, showing that increasing activity by increasing physical properties is not always necessary. Charles H. ReynoldsCharles Reynolds is currently President of Gfree Bio, a modeling and structure-based design company in AbstractThe judicious application of ligand or binding efficiencies, which quantify the molecular properties required to gain binding affinity for a drug target, is gaining traction in the selection and optimisation of fragments, hits, and leads. Retrospective analysis of recently marketed oral drugs shows that they frequently have highly optimised ligand efficiency values for their target. Optimising ligand efficiencies based on both molecular size and lipophilicity, when set in the context of the specific target, has the potential to ameliorate the molecular inflation that pervades current practice in medicinal chemistry, and to increase the developability of drug candidates.

The clinical efficacy and safety of a drug is determined by its activity profile across multiple proteins in the proteome. However, designing drugs with a specific multi-target profile is both complex and difficult. Therefore methods to rationally design drugs a priori against profiles of multiple proteins would have immense value in drug discovery. We describe a new approach for the automated design of ligands against profiles of multiple drug targets. The method is demonstrated by the evolution of an approved acetylcholinesterase inhibitor drug into brain penetrable ligands with either specific polypharmacology or exquisite selectivity profiles for G-protein coupled receptors. Overall, 800 ligand-target predictions of prospectively designed ligands were tested experimentally, of which 75% were confirmed correct. We also demonstrate target engagement in vivo. The approach can be a useful source of drug leads where multi-target profiles are required to achieve either selectivity over other drug targets or a desired polypharmacology.

No abstract