Prediction and Prioritisation of Novel Anthelmintic Candidates from Public Databases Using Deep Learning and Available Bioactivity Data Sets

Taki, Aya C.; Kapp, Louis; Hall, Ross S.; Byrne, Joseph J.; Sleebs, Brad E.; Chang, Bill C. H.; Gasser, Robin B.; Hofmann, Andreas

doi:10.3390/ijms26073134

Open AccessArticle

Prediction and Prioritisation of Novel Anthelmintic Candidates from Public Databases Using Deep Learning and Available Bioactivity Data Sets

by

Aya C. Taki

¹

,

Louis Kapp

^1,2

,

Ross S. Hall

¹

,

Joseph J. Byrne

¹,

Brad E. Sleebs

^1,3

,

Bill C. H. Chang

¹,

Robin B. Gasser

^1,*

and

Andreas Hofmann

^1,4,*

¹

Department of Veterinary Biosciences, Melbourne Veterinary School, Faculty of Science, The University of Melbourne, Parkville, VIC 3010, Australia

²

Institute of Cognitive Science, University of Osnabrück, 49090 Osnabrück, Germany

³

Walter and Eliza Hall Institute of Medical Research, Parkville, VIC 3052, Australia

⁴

Max Rubner-Institut, Federal Research Institute of Nutrition and Food, 95326 Kulmbach, Germany

^*

Authors to whom correspondence should be addressed.

Int. J. Mol. Sci. 2025, 26(7), 3134; https://doi.org/10.3390/ijms26073134

Submission received: 31 January 2025 / Revised: 23 March 2025 / Accepted: 25 March 2025 / Published: 28 March 2025

(This article belongs to the Special Issue Parasite Biology and Host-Parasite Interactions: 2nd Edition)

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Abstract

The control of socioeconomically important parasitic roundworms (nematodes) of animals has become challenging or ineffective due to problems associated with widespread resistance in these worms to most classes of chemotherapeutic drugs (anthelmintics) currently available. Thus, there is an urgent need to discover and develop novel compounds with unique mechanisms of action to underpin effective parasite control programmes. Here, we evaluated an in silico (computational) approach to accelerate the discovery of new anthelmintics against the parasitic nematode Haemonchus contortus (barber’s pole worm) as a model system. Using a supervised machine learning workflow, we trained and assessed a multi-layer perceptron classifier on a labelled dataset of 15,000 small-molecule compounds, for which extensive bioactivity data were previously obtained for H. contortus via high-throughput screening, as well as evidence-based datasets from the peer-reviewed literature. This model achieved 83% precision and 81% recall on the class of ‘active’ compounds during testing, despite a high imbalance in the training data, with only 1% of compounds carrying this label. The trained model was then used to infer nematocidal candidates by in silico screening of 14.2 million compounds from the ZINC15 database. An experimental assessment of 10 of these candidates showed significant inhibitory effects on the motility and development of H. contortus larvae and adults in vitro, with two compounds exhibiting high potency for further exploration as lead candidates. These findings indicate that the present machine learning-based approach could accelerate the in silico prediction and prioritisation of anthelmintic small molecules for subsequent in vitro and in vivo validations.

Keywords: antiparasitics; artificial neural network; drug discovery; parasite; parasitic nematode

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MDPI and ACS Style

Taki, A.C.; Kapp, L.; Hall, R.S.; Byrne, J.J.; Sleebs, B.E.; Chang, B.C.H.; Gasser, R.B.; Hofmann, A. Prediction and Prioritisation of Novel Anthelmintic Candidates from Public Databases Using Deep Learning and Available Bioactivity Data Sets. Int. J. Mol. Sci. 2025, 26, 3134. https://doi.org/10.3390/ijms26073134

AMA Style

Taki AC, Kapp L, Hall RS, Byrne JJ, Sleebs BE, Chang BCH, Gasser RB, Hofmann A. Prediction and Prioritisation of Novel Anthelmintic Candidates from Public Databases Using Deep Learning and Available Bioactivity Data Sets. International Journal of Molecular Sciences. 2025; 26(7):3134. https://doi.org/10.3390/ijms26073134

Chicago/Turabian Style

Taki, Aya C., Louis Kapp, Ross S. Hall, Joseph J. Byrne, Brad E. Sleebs, Bill C. H. Chang, Robin B. Gasser, and Andreas Hofmann. 2025. "Prediction and Prioritisation of Novel Anthelmintic Candidates from Public Databases Using Deep Learning and Available Bioactivity Data Sets" International Journal of Molecular Sciences 26, no. 7: 3134. https://doi.org/10.3390/ijms26073134

APA Style

Taki, A. C., Kapp, L., Hall, R. S., Byrne, J. J., Sleebs, B. E., Chang, B. C. H., Gasser, R. B., & Hofmann, A. (2025). Prediction and Prioritisation of Novel Anthelmintic Candidates from Public Databases Using Deep Learning and Available Bioactivity Data Sets. International Journal of Molecular Sciences, 26(7), 3134. https://doi.org/10.3390/ijms26073134

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Prediction and Prioritisation of Novel Anthelmintic Candidates from Public Databases Using Deep Learning and Available Bioactivity Data Sets

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