Predicting the Hallucinogenic Potential of Molecules Using Artificial Intelligence

The development of new drugs addressing serious mental
health and
other disorders should avoid the psychedelic experience. Analogs of
psychedelic drugs can have clinical utility and are termed “psychoplastogens”.
These represent promising candidates for treating opioid use disorder
to reduce drug dependence, with rarely reported serious adverse effects.
This drug abuse cessation is linked to the induction of neuritogenesis
and increased neuroplasticity, a hallmark of psychedelic molecules,
such as lysergic acid diethylamine. Some, but not all psychoplastogens
may act through the G-protein coupled receptor (GPCR) 5HT2A whereas others may display very different polypharmacology making
prediction of hallucinogenic potential challenging. In the process
of developing tools to help design new psychoplastogens, we have used
artificial intelligence in the form of machine learning classification
models for predicting psychedelic effects using a published in vitro
data set from PsychLight (support vector classification (SVC), area
under the curve (AUC) 0.74) and in vivo human data derived from books
from Shulgin and Shulgin (SVC, AUC, 0.72) with nested five-fold cross
validation. We have also explored conformal predictors with ECFP6
and electrostatic descriptors in an effort to optimize them. These
models have been used to predict known 5HT2A agonists to
assess their potential to act as psychedelics and induce hallucinations
for PsychLight (SVC, AUC 0.97) and Shulgin and Shulgin (random forest,
AUC 0.71). We have tested these models with head twitch data from
the mouse. This predictive capability is desirable to reliably design
new psychoplastogens that lack in vivo hallucinogenic potential and
help assess existing and future molecules for this potential. These
efforts also provide useful insights into understanding the psychedelic
structure activity relationship.