Virtual screening (VS) is an outstanding cornerstone in the drug discovery pipeline. A variety of computational approaches, which are generally classified as ligand-based (LB) and structure-based (SB) techniques, exploit key structural and physicochemical properties of ligands and targets to enable the screening of virtual libraries in the search of active compounds. Though LB and SB methods have found widespread application in the discovery of novel drug-like candidates, their complementary natures have stimulated continued efforts toward the development of hybrid strategies that combine LB and SB techniques, integrating them in a holistic computational framework that exploits the available information of both ligand and target to enhance the success of drug discovery projects. In this review, we analyze the main strategies and concepts that have emerged in the last years for defining hybrid LB + SB computational schemes in VS studies. Particularly, attention is focused on the combination of molecular similarity and docking, illustrating them with selected applications taken from the literature.

Molecular alignment is a standard procedure for three-dimensional (3D) similarity measurements and pharmacophore elucidation. This process is influenced by several factors, such as the physicochemical descriptors utilized to account for the molecular determinants of biological activity and the reference templates. Relying on the hypothesis that the maximal achievable binding affinity for a drug-like molecule is largely due to desolvation, we explore a novel strategy for 3D molecular overlays that exploits the partitioning of molecular hydrophobicity into atomic contributions in conjunction with information about the distribution of hydrogen-bond (HB) donor/acceptor groups. A brief description of the method, as implemented in the software package PharmScreen, including the derivation of the fractional hydrophobic contributions within the quantum mechanical version of the Miertus-Scrocco-Tomasi (MST) continuum model, and the procedure utilized for the optimal superposition between molecules, is presented. The computational procedure is calibrated by using a data set of 402 molecules pertaining to 14 distinct targets taken from the literature and validated against the AstraZeneca test, which comprises 121 experimentally derived sets of molecular overlays. The results point out the suitability of the MST-based hydrophobic parameters for generating molecular overlays, as correct predictions were obtained for 94%, 79%, and 54% of the molecules classified into easy, moderate, and hard sets, respectively. Moreover, the results point out that this accuracy is attained at a much lower degree of identity between the templates used by hydrophobic/HB fields and electrostatic/steric ones. These findings support the usefulness of the hydrophobic/HB descriptors to generate complementary overlays that may be valuable to rationalize structure-activity relationships and for virtual screening campaigns.

Since the development of structure-activity relationships about 50 years ago, 3D-QSAR methods belong to the most refined ligand-based in silico techniques for prediction of biological data using physicochemical molecular fields. In this scenario, this study reports the development and validation of quantum mechanical (QM)-based hydrophobic descriptors derived from the parametrized MST continuum solvation model to be used in 3D-QSAR studies within the framework of the Hydrophobic Pharmacophore (HyPhar) method. To this end, five sets of compounds reported in the literature (dopamine D2/D4 antagonists, antifungal 2-aryl-4-chromanones, and inhibitors of GSK-3, cruzain and thermolysin) have been revisited. The results derived from the QM/MST-based hydrophobic descriptors have been compared with previous CoMFA and CoMSIA studies, and examined in light of the available X-ray crystallographic structures of the targets. The analysis reveals that the combination of electrostatic and nonelectrostatic components of the octanol/water partition coefficient yields pharmacophoric models fully comparable with the predictive potential of standard 3D-QSAR techniques. Moreover, the graphical representation of the hydrophobic maps provides a direct linkage with the pattern of interactions found in crystallographic structures. Overall, the introduction of the QM/MST-based descriptors, which could be easily adapted to other continuum solvation formalisms, paves the way to novel computational strategies for disclosing structure-activity relationships in drug design. © 2016 Wiley Periodicals, Inc.

Clustering is a common

Industry has shifted towards multi-core designs as we have hit the memory and power walls. However, single thread performance remains of paramount importance since some applications have limited thread-level parallelism (TLP), and even a small part with limited TLP impose important constraints to the global performance, as explained by Amdahl's law.In this paper we propose a novel approach for leveraging multiple cores to improve single-thread performance in a multi-core design. The proposed technique features a set of novel hardware mechanisms that support the execution of threads generated at compile time. These threads result from a fine-grain speculative decomposition of the original application and they are executed under a modified multi-core system that includes: (1) mechanisms to support multiple versions; (2) mechanisms to detect violations among threads; (3) mechanisms to reconstruct the original sequential order; and (4) mechanisms to checkpoint the architectural state and recovery to handle misspeculations.The proposed scheme outperforms previous hardware-only schemes to implement the idea of combining cores for executing single-thread applications in a multi-core design by more than 10% on average on Spec2006 for all configurations. Moreover, single-thread performance is improved by 41% on average when the proposed scheme is used on a Tiny Core, and up to 2.6x for some selected applications.