Extended reduced graphs provide summary representations of chemical structures using pharmacophore-type node descriptions to encode the relevant molecular properties. Commonly used similarity measures using reduced graphs convert these graphs into 2D vectors like fingerprints, before chemical comparisons are made. This study investigates the effectiveness of a graph-only driven molecular comparison by using extended reduced graphs along with graph edit distance methods for molecular similarity calculation as a tool for ligand-based virtual screening applications, which estimate the bioactivity of a chemical on the basis of the bioactivity of similar compounds. The results proved to be very stable and the graph editing distance method performed better than other methods previously used on reduced graphs. This is exemplified with six publicly available data sets: DUD-E, MUV, GLL&GDD, CAPST, NRLiSt BDB, and ULS-UDS. The screening and statistical tools available on the ligand-based virtual screening benchmarking platform and the RDKit were also used. In the experiments, our method performed better than other molecular similarity methods which use array representations in most cases. Overall, it is shown that extended reduced graphs along with graph edit distance is a combination of methods that has numerous applications and can identify bioactivity similarities in a structurally diverse group of molecules.
Background: Graph edit distance is a methodology used to solve error-tolerant graph matching. This methodology estimates a distance between two graphs by determining the minimum number of modifications required to transform one graph into the other. These modifications, known as edit operations, have an edit cost associated that has to be determined depending on the problem. Objective: This study focuses on the use of optimization techniques in order to learn the edit costs used when comparing graphs by means of the graph edit distance. Method: Graphs represent reduced structural representations of molecules using pharmacophore-type node descriptions to encode the relevant molecular properties. This reduction technique is known as extended reduced graphs. The screening and statistical tools available on the ligand-based virtual screening benchmarking platform and the RDKit were used. Results: In the experiments, the graph edit distance using learned costs performed better or equally good than using predefined costs. This is exemplified with six publicly available datasets: DUD-E, MUV, GLL&GDD, CAPST, NRLiSt BDB, and ULS-UDS. Conclusion: This study shows that the graph edit distance along with learned edit costs is useful to identify bioactivity similarities in a structurally diverse group of molecules. Furthermore, the target-specific edit costs might provide useful structure-activity information for future drug-design efforts.
This document is in the required format. This work shows a benchmark of e-Learning tools including an approach for comparing them based on histogram specification concepts. The analysis is based on the definition of a set of criteria which are useful and desirable characteristics of learning management systems. The final results show the evaluation from different views including the approach based on their histograms. The evaluation of each e-Learning tool is based on the use of a three-dimensional model which organizes the criteria in three different axes according to their functionality inside the model, namely: Management, Technological and Instructional. With the application of the evaluation methodology we can assess the tools from different points of view. One of the main objectives of this work is to help users and developers of e-Learning tools to make good decisions about which tool have the best features for developing training and learning systems and for development and management of resources, courses and learning objects.
This work presents a performance evaluation of two of the most popular end-to-end third generation technologies. The technologies analyzed were EVDO Rev. A and HSDPA. Although these technologies are present around the world, most of the work based on these technologies is only simulation, being most of the real-case scenario investigations, internal carrier studies. We used a real-case scenario using TCP and UDP. We measured throughput, jitter and packet loss/discard as QoS performance parameters. We performed tests using both technologies concurrently with two identical computers at downlink and uplink at 0 km/h, 60 km/h and 90 km/h. These computers send/receive data to a static server host located in California. In general, we noticed that HSDPA and EVDO lose performance when speed increases. The most affected performance parameter was the throughput being most of the average values under 50 percent of the nominal throughput value. At downlink, HSDPA was the most speed increase sensitive technology and at uplink EVDO was the most speed sensitive. HSDPA at downlink goes from 32.9 percent of the nominal throughput value to 8.57 percent of nominal throughput value at 90 km/h. EVDO in the uplink goes from 17.11 percent of nominal throughput value to 6.79 percent of the nominal throughput value at 90 km/h.
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