Efforts to tackle malaria must continue for a disease
that threatens
half of the global population. Parasite resistance to current therapies
requires new chemotypes that are able to demonstrate effectiveness
and safety. Previously, we developed a machine-learning-based approach
to predict compound antimalarial activity, which was trained on the
compound collections of several organizations. The resulting prediction
platform, MAIP, was made freely available to the scientific community
and offers a solution to prioritize molecules of interest in virtual
screening and hit-to-lead optimization. Here, we experimentally validate
MAIP and demonstrate how the approach was used in combination with
a robust compound selection workflow and a recently introduced innovative
high-throughput screening (HTS) cascade to select and purchase compounds
from a public library for subsequent experimental screening. We observed
a 12-fold enrichment compared with a randomly selected set of molecules,
and the eight hits we ultimately selected exhibit good potency and
absorption, distribution, metabolism, and excretion (ADME) profiles.