A Machine Learning-Enhanced Ligand-Based Workflow Identifies Novel Cryptosporidium parvum Inhibitors.

Authors

Timothy B Huber, Jose E Teixeira, Omoh E Ben, Charitha Bodepudi, Elaine Lohse, Christopher D Huston, Marvin J Meyers

Published in

ACS infectious diseases. Volume 12. Issue 11. Pages 3798-3808. Nov 13, 2026.

Abstract

Cryptosporidium parasites are a leading cause of waterborne disease worldwide, causing life-threatening diarrhea and malnutrition in young children and people with HIV. Currently, nitazoxanide is the only FDA-approved treatment, but it is largely ineffective in severely immunocompromised patients, highlighting the need for new therapies. Here, we use a ligand-based virtual screening workflow that integrates Thompson Sampling, a pharmacophore model, and a machine learning classifier to screen a subset of Enamine's REAL database comprising 51 million virtual compounds. Across two screening campaigns, 20 novel micromolar hits were identified among 69 tested compounds (29%), eight of which exhibited confirmed activity at or below 21 μM. These findings expand the chemical diversity of the original series by introducing new tail and head chemotypes and demonstrate the effectiveness of active learning strategies for improving ligand-based virtual screening performance.

PMID:
43023864
Bibliographic data and abstract were imported from PubMed on 13 Nov 2026.

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