In the big data era, voluminous datasets are routinely acquired, stored and analyzed with the aim to inform biomedical discoveries and validate hypotheses. No doubt, data volume and diversity have dramatically increased by the advent of new technologies and open data initiatives. Big data are used across the whole drug discovery pipeline from target identification and mechanism of action to identification of novel leads and drug candidates. Such methods are depicted and discussed, with the aim to provide a general view of computational tools and databases available. We feel that big data leveraging needs to be cost-effective and focus on personalized medicine. For this, we propose the interplay of information technologies and (chemo)informatic tools on the basis of their synergy.

Computational approaches in target identification and drug discovery

Combination of ligand- and structure-based methods in virtual screening

The discovery of bioactive molecules is an expensive and time-consuming process and new strategies are continuously searched for in order to optimize this process. Virtual Screening (VS) is one of the recent strategies that has been explored for the identification of candidate bioactive molecules. The number of new techniques and software that can be applied in this strategy has grown considerably in recent years, so, before their use, it is necessary to understand the basics an also the limitations behind each one to get the most out of them. It is also necessary to assess the real contributions of this strategy so that more significant progress can be made in the future. In this context, this review aims to discuss some important points related to VS, including the use of virtual ligand and biotarget libraries, structurebased and ligand-based VS techniques, as well as to present recent cases where this strategy was successfully applied.

Virtual Screening Techniques in Drug Discovery: Review and Recent Applications.

Methods for generating and applying pharmacophore models as virtual screening filters and for bioactivity profiling.

A pharmacophore describes the arrangement of molecular features a ligand must contain to efficaciously bind a receptor. Pharmacophore models are developed to improve molecular understanding of ligand–protein interactions, and can be used as a tool to identify novel compounds that fulfil the pharmacophore requirements and have a high probability of being biologically active. Protein structure-based pharmacophores (SBPs) derive these molecular features by conversion of protein properties to reciprocal ligand space. Unlike ligand-based pharmacophore models, which require templates of ligands in their bioactive conformation, SBPs do not depend on ligand information. The current review describes the different steps in the construction of SBPs: (i) protein structure preparation, (ii) binding site detection, (iii) pharmacophore feature definition, and (iv) pharmacophore feature selection. We show that the SBP modeling workflow poses different challenges than ligand-based pharmacophore modeling, including the definition of protein pharmacophore features essential for ligand binding. A comprehensive overview of different SBP modeling and screening methods and applications is provided to illustrate that SBPs can be efficiently used for virtual screening, ligand binding mode prediction, and binding site similarity detection. Our review demonstrates that SBPs are valuable tools for hit and lead optimization, compound library design and target hopping, especially in cases where ligand information is scarce.

From the protein's perspective: the benefits and challenges of protein structure-based pharmacophore modeling

Multiple pharmacophore models were constructed based on the 18 crystal structures of phosphodiesterase 4 (PDE4) in complex with different inhibitors for discovering new potential PDE4 inhibitors. After validation of their efficiency in screening, 10 of the pharmacophore models were confirmed effective. Remarkably, the hits retrieved by these effective pharmacophore models were different, demonstrating that different pharmacophore models may have different performances in database screening. Therefore, all these models were employed to screen the compound database SPECS for finding potent leads with much structural diversity. Combining all the screened hits based on the 10 pharmacophore models, followed by molecular docking and bioassay, 4 of 53 tested compounds were found as active as rolipram (a well studied PDE4 inhibitor). More impressively, the four potent inhibitors with different chemical scaffolds were discovered by three different pharmacophore models separately, suggesting that a single pharmacop...

Multiple Pharmacophore Models Combined with Molecular Docking: A Reliable Way for Efficiently Identifying Novel PDE4 Inhibitors with High Structural Diversity

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Multiple Pharmacophore Models Combined with Molecular Docking: A Reliable Way for Efficiently Identifying Novel PDE4 Inhibitors with High Structural Diversity