Gene/Protein Disease Symptom Drug Enzyme Compound
Pivot Concepts:   Target Concepts:
Query: EC:1.5.1.3 (dihydrofolate reductase)
 5,819 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

Twenty-four out of twenty-nine quinoxalines were selected at the National Cancer Institute, Bethesda, Md, USA, for in vitro anticancer screening. Among these, 10 derivatives exhibited high values of percent tumor growth inhibition at a concentration of 10(-4) M in all cancer cell lines. Four of these compounds maintained these values at 10(-5) M, whereas a certain number exhibited significant values of percent inhibition at the most diluted concentrations (10(-8)-10(-6) M). Inhibitory activity against dihydrofolate reductase (DHFR) (bovine and rat liver) was determined for the most active compounds. This test showed that this type of quinoxaline exhibited an appreciable activity in comparison with the previously described aza analogues. In the other test (Lactobacillus casei, thymidylate synthase (TS), human HTS) no or poor activity was detected in both series of compounds.
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PMID:Quinoxaline chemistry. Part 11. 3-Phenyl-2[phenoxy- and phenoxymethyl]-6(7) or 6,8-substituted quinoxalines and N-[4-(6(7)-substituted or 6,8-disubstituted-3-phenylquinoxalin-2-yl)hydroxy or hydroxymethyl] benzoylglutamates. Synthesis and evaluation of in vitro anticancer activity and enzymatic inhibitory activity against dihydrofolate reductase and thymidylate synthase. 983 61

In many cases at the beginning of an HTS-campaign, some information about active molecules is already available. Often known active compounds (such as substrate analogues, natural products, inhibitors of a related protein or ligands published by a pharmaceutical company) are identified in low-throughput validation studies of the biochemical target. In this study we evaluate the effectiveness of a support vector machine applied for those compounds and used to classify a collection with unknown activity. This approach was aimed at reducing the number of compounds to be tested against the given target. Our method predicts the biological activity of chemical compounds based on only the atom pairs (AP) two dimensional topological descriptors. The supervised support vector machine (SVM) method herein is trained on compounds from the MDL drug data report (MDDR) known to be active for specific protein target. For detailed analysis, five different biological targets were selected including cyclooxygenase-2, dihydrofolate reductase, thrombin, HIV-reverse transcriptase and antagonists of the estrogen receptor. The accuracy of compound identification was estimated using the recall and precision values. The sensitivities for all protein targets exceeded 80% and the classification performance reached 100% for selected targets. In another application of the method, we addressed the absence of an initial set of active compounds for a selected protein target at the beginning of an HTS-campaign. In such a case, virtual high-throughput screening (vHTS) is usually applied by using a flexible docking procedure. However, the vHTS experiment typically contains a large percentage of false positives that should be verified by costly and time-consuming experimental follow-up assays. The subsequent use of our machine learning method was found to improve the speed (since the docking procedure was not required for all compounds from the database) and also the accuracy of the HTS hit lists (the enrichment factor).
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PMID:Target specific compound identification using a support vector machine. 1734 18