(6-Methyl-1-(propan-2-yl)-9,10-didehydroergolin-8β-yl)methyl 2-ethylbutanoate

Similarities

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.

Similarity type

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.

Legal statuses

For each country, we look for references and regulations related to this molecule, of to families of compounds that this molecule belongs to.

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.

Always double check the sources provided, there can be classification errors. Our system may also have missed certain references. : This molecule was added in the legislation: This molecule was removed from the legislation

Country:

Type of legislation

Narcotics

2021-05-23

Molecule mentionned as "phénéthylamines et des alpha-méthylphénéthylamines"

Psychotropes

The system hasn't detected any mention of this drug in France as a Psychotropes but you should always double check by yourself.

Molecule properties

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.

Description: A dataset focused on predicting the inhibitory effects of molecules on the enzyme beta-secretase 1 (BACE1). BACE1 inhibition is a potential target for Alzheimer's disease treatment.

Class

TrainValTest

2.88-3.70-1.36

Description: A dataset providing insights on the ability of molecules to penetrate the blood-brain barrier. Crucial for understanding the potential of molecules as central nervous system drugs.

p_np

TrainValTest

-2.24-3.290.40

Description: This dataset deals with the FDA approval status and clinical trial toxicity of molecules. Important for understanding the safety and regulatory status of compounds.

CT_TOX

TrainValTest

-0.49-0.78-0.91

FDA_APPROVED

TrainValTest

0.53-1.27-0.50

Description: A dataset that predicts the solubility of molecules in water. Solubility is an essential property influencing bioavailability and the potential formulation of a drug.

logSolubilitylog(mol/L)

-2.07

TrainValTest

Description: This dataset is centered on predicting the free energy when a molecule is dissolved in water. The energy changes can affect molecular interactions in biological systems.

freesolvkcal/mol

-1.08

TrainValTest

Description: A dataset predicting the lipophilicity of molecules. Lipophilicity is a crucial factor affecting the distribution, metabolism, and excretion of drugs in the body.

lipoAlogP

1.54

TrainValTest

Description: This dataset gives insights into the metabolic stability of molecules. High metabolic stability often results in a longer half-life, influencing drug dosage and frequency.

low

TrainValTest

-0.17-3.50-2.88

high

TrainValTest

0.72-3.55-0.49

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