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We describe the opportunities posed by computer-assisted drug design in the light of two aspects of the current drug discovery scenario: the decline of innovation due to high attrition rates at clinical stage of development and the combinatorial explosion emerging from exponential growth of feasible small molecules and genome and proteome exploration. We present an overview of recent reports from our group in the field of rational drug development, by using topological descriptors (either alone, or in combination with different 3D approaches) and a diversity of modeling techniques such as Linear Discriminant Analysis and the Replacement Method. Modeling efforts aimed at the integrated prediction of several significant molecular properties in the field of drug discovery, such as pharmacological activity, aqueous solubility, human intestinal permeability and affinity to P-glycoprotein (ABCB1, MDR1) are reviewed. The suitability of conformation-independent descriptors to explore large chemical repositories is highlighted, as well as the opportunities posed by in silico guided drug repurposing.

CNS drug development is characterized by an especially high attrition rate, despite clear unmet medical needs in the field of neuro-pharmacology and significant investment in R of novel CNS drug treatments. Here, we overview the issues underlying the intrinsic difficulty of CNS drugs development, including obstacles of pharmacokinetic nature and lack of predictivity of preclinical tests. We highlight current efforts to overcome these limitations, with an emphasis on modeling opportunities towards early recognition of CNS candidates (stressing the possibilities of multi-target directed ligands or "magic shotguns") and different approaches to improve CNS bioavailability.

a P-glycoprotein (Pgp) is an ATP-dependent efflux transporter protein associated with multidrug resistance in several diseases such as cancer, epilepsy and AIDS. It is preferentially expressed in organs and tissues that function as a barrier (e.g. the gut walls or the blood-brain barrier) or promote the elimination of xenobiotics from the organism (e.g. liver and kidney). Pgp limits drug bioavailability; thus, the recognition of Pgp substrates at the early stages of the drug development cycle is essential for the development of new chemotherapeutic agents to deal with multidrug resistance issues. Here we present the development of several classifier models based on topological descriptors to identify potential Pgp substrates, aimed to be applied as secondary filter in virtual screening campaigns. Receiver Operating characteristic (ROC) curves show that combination of individual models, through data fusion, in a three-model ensemble, allows attaining higher areas under the curve and an overall better behavior in terms of sensitivity or specificity. The individual discriminant functions (dfs) presented have a performance similar to that of the previously reported models and, remarkably, our models only include low-dimensional (up to 2D) molecular descriptors, which makes them adequate for the virtual screening of increasingly large virtual chemical repositories.

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