p-F-Deprenyl
p-F-Deprenyl
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4-fluoroselegiline
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SMILES:C#CCN(C)C(C)CC1=CC=C(F)C=C1
InChI:InChI=1S/C13H16FN/c1-4-9-15(3)11(2)10-12-5-7-13(14)8-6-12/h1,5-8,11H,9-10H2,2-3H3
InChI key:MUDUXRHPVDVWHU-UHFFFAOYSA-N
Check on isomerdesign
Check on drugmap
SMILES:C#CCN(C)C(C)CC1=CC=C(F)C=C1
InChI:InChI=1S/C13H16FN/c1-4-9-15(3)11(2)10-12-5-7-13(14)8-6-12/h1,5-8,11H,9-10H2,2-3H3
InChI key:MUDUXRHPVDVWHU-UHFFFAOYSA-N
Being able to identify molecules that are similar to the one we study can allow to infer some of its properties. There are several ways of measuring the similarity between molecules, by their structure, effects, pharmacological interactions etc. In the following, you can find similar molecules according to various criterions and tools that we developed. To understand the limitations of these comparisons, it is crucial to always refer to the methodology that was used to measure those similarities. Please note: This information is provided solely for informational purposes and should not be interpreted as medical advice.
To measure structural similarity, we use the Mol2vec method, which is a neural network that processes molecules and transform them into points in spaces, such that molecules with chemically related substructures are transformed into points that are close in space.
Disclaimer: The information provided on this website regarding the legal references of molecules is for informational purposes only and does not constitute legal advice. While we strive to keep the information up-to-date and accurate, laws and regulations may change over time. Therefore, we cannot guarantee the completeness, accuracy, or applicability of the information provided. It is important to always refer to the original legal texts and consult with a qualified legal professional before making any decisions based on the information provided on this website.
Using the KGPT Deep Learning model, we predict several property of the molecule. Predictions are grouped by the dataset that was used to get those prediction. Along with each prediction, we provide a plot that shows the distribution of predicted values on the train/test/val dataset. This gives an estimate of the reliability of the model.
In the following we provide more advanced analysis about the interactions of a molecule with the human metabolism, docking sites etc.
Binding affinities with a list of 61 predefined docking sites. Those affinities are used to compute the interactions similarities.
We use a Deep Learning model to predict the interactions of this molecule with metabolism. Refer to the source to understand the methodology.