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Predicting the Hallucinogenic Potential of Molecules Using Artificial Intelligence.

Fabio Urbina1, Thane Jones1, Joshua S Harris1

  • 1Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, North Carolina 27606, United States.

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|August 2, 2024
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Summary

Researchers developed AI models to predict psychedelic effects, aiming to design safer "psychoplastogens" for mental health treatments like opioid use disorder. These models help identify compounds with therapeutic potential but without hallucinogenic properties.

Keywords:
conformal predictorshallucinogenicmachine learningpsychedelicsupport vector classification

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Area of Science:

  • Neuroscience and Pharmacology
  • Computational Chemistry and Cheminformatics

Background:

  • Psychedelic analogs, termed "psychoplastogens," show promise for treating disorders like opioid use disorder by inducing neuroplasticity.
  • Predicting hallucinogenic potential is challenging due to varied mechanisms, including G-protein coupled receptor (GPCR) 5HT2A interactions and complex polypharmacology.

Purpose of the Study:

  • To develop artificial intelligence (AI) tools, specifically machine learning classification models, to predict the psychedelic effects of molecules.
  • To design novel psychoplastogens with therapeutic utility but lacking in vivo hallucinogenic potential.

Main Methods:

  • Utilized machine learning classification models, including support vector classification (SVC) and random forest, with nested five-fold cross-validation.
  • Trained models on in vitro (PsychLight) and in vivo human data (Shulgin's books), incorporating ECFP6 and electrostatic descriptors with conformal predictors.
  • Validated models by predicting known 5HT2A agonists and assessing their hallucinogenic potential using mouse head twitch data.

Main Results:

  • Achieved areas under the curve (AUC) of 0.74 (PsychLight in vitro) and 0.72 (Shulgin human data) for predicting psychedelic effects.
  • Models demonstrated high predictive accuracy for known 5HT2A agonists, with AUCs of 0.97 (PsychLight) and 0.71 (Shulgin data).

Conclusions:

  • AI-driven predictive models are effective in assessing the hallucinogenic potential of psychoplastogen candidates.
  • These tools are crucial for the reliable design of new therapeutic molecules that separate neuroplastic effects from psychedelic experiences.