Predicting In Vivo Compound Brain Penetration Using Multi-task Graph Neural Networks

Assessing
whether compounds penetrate the brain can become critical
in drug discovery, either to prevent adverse events or to reach the
biological target. Generally, pre-clinical in vivo studies measuring
the ratio of brain and blood concentrations (Kp) are required to estimate the brain penetration potential
of a new drug entity. In this work, we developed machine learning
models to predict in vivo compound brain penetration (as LogKp) from chemical structure. Our results show
the benefit of including in vitro experimental data as auxiliary tasks
in multi-task graph neural network (MT-GNN) models. MT-GNNs outperformed
single-task (ST) models solely trained on in vivo brain penetration
data. The best-performing MT-GNN regression model achieved a coefficient
of determination of 0.42 and a mean absolute error of 0.39 (2.5-fold)
on a prospective validation set and outperformed all tested ST models.
To facilitate decision-making, compounds were classified into brain-penetrant
or non-penetrant, achieving a Matthew’s correlation coefficient
of 0.66. Taken together, our findings indicate that the inclusion
of in vitro assay data as MT-GNN auxiliary tasks improves in vivo
brain penetration predictions and prospective compound prioritization.