Current Methods for Drug Property Prediction in the Real World
Jacob Green Cecilia Cabrera Diaz Maximilian A. H. Jakobs Andrea Dimitracopoulos Mark van der Wilk Ryan D. Greenhalgh
Predicting drug properties is key in drug discovery to enable de-risking of
assets before expensive clinical trials, and to find highly active compounds
faster. Interest from the Machine Learning community has led to the release of
a variety of benchmark datasets and proposed methods. However, it remains
unclear for practitioners which method or approach is most suitable, as
different papers benchmark on different datasets and methods, leading to
varying conclusions that are not easily compared. Our large-scale empirical
study links together numerous earlier works on different datasets and methods;
thus offering a comprehensive overview of the existing property classes,
datasets, and their interactions with different methods. We emphasise the
importance of uncertainty quantification and the time and therefore cost of
applying these methods in the drug development decision-making cycle. We
discover that the best method depends on the dataset, and that engineered
features with classical ML methods often outperform deep learning.
Specifically, QSAR datasets are typically best analysed with classical methods
such as Gaussian Processes while ADMET datasets are sometimes better described
by Trees or Deep Learning methods such as Graph Neural Networks or language
models. Our work highlights that practitioners do not yet have a
straightforward, black-box procedure to rely on, and sets the precedent for
creating practitioner-relevant benchmarks. Deep learning approaches must be
proven on these benchmarks to become the practical method of choice in drug
property prediction.