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Motivation: Lead identification is a fundamental step to prioritize candidate compounds for downstream drug discovery process. Machine learning (ML) and deep learning (DL) approaches are widely used to identify lead compounds using both chemical property and experimental information. However, ML or DL methods rarely consider compound similarity information directly since ML and DL models use abstract representation of molecules for model construction. Alternatively, data mining approaches are also used to explore chemical space with drug candidates by screening undesirable compounds. A major challenge for data mining approaches is to develop efficient data mining methods that search large chemical space for desirable lead compounds with low false positive rate. Results: In this work, we developed a network propagation (NP) based data mining method for lead identification that performs search on an ensemble of chemical similarity networks. We compiled 14 fingerprint-based similarity networks. Given a target protein of interest, we use a deep learning-based drug target interaction model to narrow down compound candidates and then we use network propagation to prioritize drug candidates that are highly correlated with drug activity score such as IC50. In an extensive experiment with BindingDB, we showed that our approach successfully discovered intentionally unlabeled compounds for given targets. To further demonstrate the prediction power of our approach, we identified 24 candidate leads for CLK1. Two out of five synthesizable candidates were experimentally validated in binding assays. In conclusion, our framework can be very useful for lead identification from very large compound databases such as ZINC.

This article presents data on social capital at the United States’ county-level. Following Rupasingha et al. (2006), the social capital index captures the common factor among density measures of 10 different types of associations, voter turnout rates, U.S. decennial census participation rates, and the number of non-profit organizations. Based on Knack (2003), we create associational densities measures as a proxy for both bridging and bonding social capital. Including data on income inequality, racial diversity, minority group size, average household income, educational attainment, the ratio of a family household, the size of migration population, and female labor market participation rates, the data covers 3,104 U.S. counties for both 2009 and 2014. This paper includes descriptive statistics and figures. This data article is associated with the article “Race, Inequality, and Social Capital in the U.S. Counties.”

The influential role of international treaty secretariats in coordinating bureaucracies across jurisdictional boundaries has been highlighted in recent years. While we now better understand how their influence occurs, the field still faces a substantial difficulty in answering the basic quantitative question of “how influential?” By employing network analysis, we devised and tested a survey to quantify secretariat influence within an international environmental regime. We applied the survey tool to two transboundary fisheries governance networks in North America and here focus on the Great Lakes Fishery Commission (GLFC) as our primary case study. The results demonstrate a high ability of treaty secretariat to influence the management decisions of federal and state/provincial agencies. Primary interview data collected with the GLFC secretariat staff helps explain this finding. This study advances the reconceptualization of secretariat influence via relational metrics, and offers a way to estimate secretariat influence despite their typically veiled modes of operation.

Chromium and chromium nitride were selected as potential barriers to prevent fuel-clad chemical interaction (FCCI) between the cladding and the fuel material. In this study, ferritic/martensitic HT-9 steel and misch metal were used to simulate the reaction between the cladding and fuel fission product, respectively. Radio frequency magnetron sputtering was used to deposit Cr and CrN films onto the cladding, and the gas flow rates of argon and nitrogen were fixed at certain values for each sample to control the deposition rate and the crystal structure of the films. The samples were heated for 24 h at 933 K through the diffusion couple test, and considerable amount of interdiffusion (max. thickness: 550 μm) occurred at the interface between HT-9 and misch metal when the argon and nitrogen were used individually. The elemental contents of misch metal were detected at the HT-9 through energy dispersive X-ray spectroscopy due to the interdiffusion. However, the specimens that were sputtered by mixed gases (Ar and N2) exhibited excellent resistance to FCCI. The thickness of these CrN films were only 4 μm, but these films effectively prevented the FCCI due to their high adhesion strength (frictional force ≥1,200 μm) and dense columnar microstructures. Keywords: CrN, Fuel-Clad Chemical Interaction Barrier, Interdiffusion, Scratch Test