Shared Consensus Machine Learning Models for Predicting Blood Stage Malaria Inhibition

The development of
new antimalarial therapies is essential, and
lowering the barrier of entry for the screening and discovery of new
lead compound classes can spur drug development at organizations that
may not have large compound screening libraries or resources to conduct
high-throughput screens. Machine learning models have been long established
to be more robust and have a larger domain of applicability with larger
training sets. Screens over multiple data sets to find compounds with
potential malaria blood stage inhibitory activity have been used to
generate multiple Bayesian models. Here we describe a method by which
Bayesian quantitative structure–activity relationship models,
which contain information on thousands to millions of proprietary
compounds, can be shared between collaborators at both for-profit
and not-for-profit institutions. This model-sharing paradigm allows
for the development of consensus models that have increased predictive
power over any single model and yet does not reveal the identity of
any compounds in the training sets.