Using Physiochemical Properties of Phenylalkylamines in Silico as a Predictive Classification of Psychedelic Effect
Abstract
Rationale: Substituted phenethylamines have been widely used and distributed for religious, therapeutic, and recreational purposes. While some correlations have been reported on small subsets of these molecules and their action, little is known about the fundamental basis of their action at an atomic level. As such, collecting a large collection of computationally predicted physiochemical properties of these compounds in a comprehensive database can serve to aid in predicting their mechanisms of action. Knowing this will ultimately reduce harm among recreational users and increase efficacy in personalized therapies. As such this study aims at establishing a relationship between the calculated atomic properties of substituted phenethylamines and their psychedelic effect.
Materials & Methods: The drug docking program Autodock 4.0 was used to predict the strength and conformation of binding of a list of known phenethylamines to protein targets. The quantum chemistry package Gaussian 09 was used to calculate physiochemical properties such as molecular orbital energies, polarization, and of the same compounds. All calculations were benchmarked against previous experimental data. Classification and feature selection for correlates to psychedelic potency were performed in MATLAB’s classification learners.
Results: Extensive data on 102 properties was collected for 202 substituted phenylalkylamines. The variability of psychedelic potency was largely explained by five of these properties: isotropic polarizability (α), aqueous solubility (AS), polar surface area (PSA), molar refractivity (MR), and molecular weight (MW). Among a specific subset of mescaline analogs at high dosage groups, the highest occupied molecular orbital (HOMO) was also highly correlated with psychedelic potency.
Conclusions: Psychedelic potency may be predicted in part from molecular properties after steric requirements are met. The model presented here compliments categorization based on binding affinity and inhibition constants of neural receptors, especially for anomalous compounds. More data is necessary to conclusively classify these compounds on a dosage basis.
Faculty Sponsors
Dr. Travis J.A. Craddock
Project Type
Event
Location
Alvin Sherman Library
Start Date
4-3-2024 12:30 PM
End Date
4-4-2024 1:30 PM
Using Physiochemical Properties of Phenylalkylamines in Silico as a Predictive Classification of Psychedelic Effect
Alvin Sherman Library
Rationale: Substituted phenethylamines have been widely used and distributed for religious, therapeutic, and recreational purposes. While some correlations have been reported on small subsets of these molecules and their action, little is known about the fundamental basis of their action at an atomic level. As such, collecting a large collection of computationally predicted physiochemical properties of these compounds in a comprehensive database can serve to aid in predicting their mechanisms of action. Knowing this will ultimately reduce harm among recreational users and increase efficacy in personalized therapies. As such this study aims at establishing a relationship between the calculated atomic properties of substituted phenethylamines and their psychedelic effect.
Materials & Methods: The drug docking program Autodock 4.0 was used to predict the strength and conformation of binding of a list of known phenethylamines to protein targets. The quantum chemistry package Gaussian 09 was used to calculate physiochemical properties such as molecular orbital energies, polarization, and of the same compounds. All calculations were benchmarked against previous experimental data. Classification and feature selection for correlates to psychedelic potency were performed in MATLAB’s classification learners.
Results: Extensive data on 102 properties was collected for 202 substituted phenylalkylamines. The variability of psychedelic potency was largely explained by five of these properties: isotropic polarizability (α), aqueous solubility (AS), polar surface area (PSA), molar refractivity (MR), and molecular weight (MW). Among a specific subset of mescaline analogs at high dosage groups, the highest occupied molecular orbital (HOMO) was also highly correlated with psychedelic potency.
Conclusions: Psychedelic potency may be predicted in part from molecular properties after steric requirements are met. The model presented here compliments categorization based on binding affinity and inhibition constants of neural receptors, especially for anomalous compounds. More data is necessary to conclusively classify these compounds on a dosage basis.
