CANDID-CNS: AI Unlocks Stereochemistry and Beyond Rule of 5 to Predict CNS Penetration of Small Molecules

Neuroscience is the most difficult therapeutic area for
pharmaceutical
drug discovery. The blood-brain barrier (BBB) prevents ∼100%
of large molecules and >98% of small molecules from penetrating
the
central nervous system (CNS). Most small molecule drugs designed for
the CNS are Rule of 5 (Ro5) molecules. Thus, Beyond Rule of 5 (bRo5)
molecules, which have significant potential to act on especially difficult
drug targets and indications, represent an untapped chemical class
for
novel CNS therapeutics. However, bRo5 small molecules face three key
challenges: (i) exclusion from most medicinal chemistry drug designs,
(ii) difficulty penetrating the BBB, and (iii) inability of existing
computational methods to predict the CNS penetration of bRo5 molecules.
In addition, the stereochemistry of small molecules is a potential
contributor to CNS penetration, but current computational methods
do not adequately account for it. Here, we present CANDID-CNS, an
artificial intelligence (AI) model with an attentive graph neural
network (GNN) architecture, which overcomes the barriers in bRo5 and
stereochemistry. Specifically, CANDID-CNS outperforms the industry
standard at: (1) prioritizing bRo5 molecules for CNS penetration (CANDID-CNS
87% AUPRC compared to industry standard 56%), (2) distinguishing stereoisomers
of differing CNS penetration (CANDID-CNS 68% AUROC compared to industry
standard 50%), and (3) predicting the CNS penetration of a holdout
set of our 1910 proprietary precision neuroscience molecules (CANDID-CNS
90% AUROC compared to industry standard 81%). CANDID-CNS represents
a breakthrough in AI-driven drug discovery by unlocking the potential
of bRo5 and stereochemistry to significantly expand the chemical space
of small molecules that can be developed into therapeutics for debilitating
diseases of the CNS.