  The prediction of absorption, distribution, metabolism, excretion, and
toxicity (ADMET) of small molecules from their molecular structure is a central
problem in medicinal chemistry with great practical importance in drug
discovery. Creating predictive models conventionally requires substantial
trial-and-error for the selection of molecular representations, machine
learning (ML) algorithms, and hyperparameter tuning. A generally applicable
method that performs well on all datasets without tuning would be of great
value but is currently lacking. Here, we describe Pareto-Optimal Embedded
Modeling (POEM), a similarity-based method for predicting molecular properties.
POEM is a non-parametric, supervised ML algorithm developed to generate
reliable predictive models without need for optimization. POEMs predictive
strength is obtained by combining multiple different representations of
molecular structures in a context-specific manner, while maintaining low
dimensionality. We benchmark POEM relative to industry-standard ML algorithms
and published results across 17 classifications tasks. POEM performs well in
all cases and reduces the risk of overfitting.
