Virtual false positive

Test a substantial number of compounds. VS methods generally offer enrichment, but most ranked hit lists contain a significant proportion of false positives. Hitlists should be scaled to 1-5% of the compounds in the virtual library screened. In many real world situations, the computational chemist is being asked to choose lists of compounds representing 0.1% or less of the compounds screened (e.g., the best 100 of 100,000 compounds). Typically, VS methods have been validated considering 1%, 5%, or 10% of the total number of compounds in the VS collection. By following up on more compounds, one increases the probability of impact from VS. 

No laboratory process is completely free from error. The GG/MS test is virtually error free, but the EMIT is far from accurate. There are some false positives you should avoid if you re getting an EMIT test. Take this... 

Laura Gibson, a medical doctor on the internet, tested positive and was not hired. She had a poppy seed bagel that morning, not knowing it was a false positive. She fought it to the point where they just decided to throw out the results and hire her anyway. But don t go taking it to court it s virtually impossible to win this case. 

Mass spectrometry (MS) is highly selective. The ability to further perform tandem mass spectrometry (MS/MS) analysis when a compound is detected to confirm the detection virtually eliminates false positive and negative alarms. But MS/MS analysis must be completely automated for the average GI to be able to perform it. A clever hand-held chemical and biological mass spectrometer has been developed that weighs only 4.3 pounds. The problem with the unit is production of the necessary vacuum, which requires 35 amps at 24 volts. Thus, battery-operated portable mass spectrometry is not yet available. 

Next, we used an in-house library design software (see details in Chapter 15) to enumerate the virtual libraries and then calculated various physical properties. Products were removed from consideration if MW is > 300, number of rotatable bonds > 3, and ClogP > 3. For solubility, two in-house model calculations were applied as filters turbidimetric >10 mg/mL and thermodynamic solubility >100 xM. The resulting cherry-picked library was then reviewed by NMR spectroscopists to remove compounds with possible artifacts, likely to be insoluble, or likely to be false positive. These included some conjugated systems and compounds with likelihood of indistinct NMR spectra. 

Finally we hope to see that more validation studies are conducted to compare any new search method with the reference exhaustive search (of course on a smaller validation virtual space of 104-106). Only through this type of rigorous validation studies, one can truly probe the rates of false positives and false negatives as well as the fold increase in search speed. This in turn allows end users to make informed decisions on which search method will be a best match for their specific tasks. 

As mentioned for the previous libraries, these varied responses (especially the increases in fluorescence) across the library help decrease the chances of false-positives for the individual analytes. Additionally, amino-functionalized TO is an excellent sensor for HStTf not only is the magnitude of the fluorescence increase quite large (72%), but it is the only anion that induces such an increase. The addition of AcO induces a quenching of 34%, while N03 and P PCV result in virtually no response. These results support those found for the previous cation systems, wherein making a library of fluorophores and binding groups results in a unique array of responses to the anions. 

The hit rate within a set of molecules selected by a virtual screen is primarily determined by two parameters the unknown proportion of p hits that exist in the set of molecules scored and the false positive error rate (a) of the classifier used for virtual screening. To a large extent, the statistics of rare events (true hits within a large compound collection) leads to some initially counterintuitive results in the magnitude of a hit rate within a set of molecules selected by a model. 

Equation (3), which is an application of Bayes theorem, is referred to as the Positive Predictive Value. The parameter p is unknown but believed to be very small (<0.01) for large virtual libraries. 1 - p is the power (or 1 - type II error, where ft is the false negative error rate) and a is the type I error, also called the size of a test in the hypothesis testing context, or the false positive error rate. The last equation defines the probability that a molecule is determined to be a hit in a biochemical assay given that the virtual screen predicts the molecule to be a hit. This probability is of great interest because it is valuable to have an estimate of the hit rate one can expect for a subset of molecules that are selected by a virtual screen. 

Phenothiazines can cause a false-positive pregnancy test, but only with the virtually obsolete Ascheim-Zondek animal test method (SED-11, 115). 

Compared with maternal serum, amniotic fluid AFP concentrations in pregnancies affected with open neural tube defects are far more separated from unaffected pregnan-cies. However, amniotic fluid AFP measurements are not by themselves diagnostic because of false-positive results. If the amniotic fluid is contaminated with even a small amount of fetal blood, as many as 2% to 3% of the results can be falsely positive. All abnormal amniotic fluid AFP results must be confirmed by measurement of amniotic fluid AChE. The combination of amniotic fluid AFP and AChE is virtually diagnostic for an open neural tube defect. 

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