  The strict selectivity of the blood-brain barrier (BBB) represents one of the
most formidable challenges to successful central nervous system (CNS) drug
delivery. Computational methods to generate BBB permeable drugs in silico may
be valuable tools in the CNS drug design pipeline. However, in real-world
applications, BBB penetration alone is insufficient; rather, after transiting
the BBB, molecules must bind to a specific target or receptor in the brain and
must also be safe and non-toxic. To discover small molecules that concurrently
satisfy these constraints, we use multi-objective generative AI to synthesize
drug-like BBB-permeable small molecules. Specifically, we computationally
synthesize molecules with predicted binding affinity against dopamine receptor
D2, the primary target for many clinically effective antipsychotic drugs. After
training several graph neural network-based property predictors, we adapt
SyntheMol (Swanson et al., 2024), a recently developed Monte Carlo Tree
Search-based algorithm for antibiotic design, to perform a multi-objective
guided traversal over an easily synthesizable molecular space. We design a
library of 26,581 novel and diverse small molecules containing hits with high
predicted BBB permeability and favorable predicted safety and toxicity
profiles, and that could readily be synthesized for experimental validation in
the wet lab. We also validate top scoring molecules with molecular docking
simulation against the D2 receptor and demonstrate predicted binding affinity
on par with risperidone, a clinically prescribed D2-targeting antipsychotic. In
the future, the SyntheMol-based computational approach described here may
enable the discovery of novel neurotherapeutics for currently intractable
disorders of the CNS.
