Machine Learning and Large Language Models for Modeling Complex Toxicity Pathways and Predicting Steroidogenesis

High-throughput screening and computational models have been effective in predicting chemical interactions with estrogen and androgen receptors, but similar approaches for steroidogenesis remain limited. To address this gap, we developed general steroidogenesis modulation models using data from ∼1,8...

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Bibliographic Details
Main Author:	Thomas R. Lane (6195626) (author)
Other Authors:	Patricia A. Vignaux (6911525) (author), Joshua S. Harris (19155418) (author), Scott H. Snyder (14565212) (author), Fabio Urbina (5613044) (author), Sean Ekins (162) (author)
Published:	2025
Subjects:	Biophysics Biochemistry Genetics Pharmacology Plant Biology Biological Sciences not elsewhere classified Chemical Sciences not elsewhere classified Information Systems not elsewhere classified prospective test set predicting chemical interactions potential molecular targets key steroidogenic enzymes end points simultaneously conformal prediction adjustments assessing chemical impacts 800 chemicals screened 14 predicted active 126 – 9 models may offer models enable predictions large language models steroidogenesis remain limited random forest model predicting steroidogenesis high general steroidogenesis inhibition 5 ), 5α regression models based 50 </ sub based model computational models throughput screening similar approaches scalable system regulatory decision product stewardship machine learning including cyp17a1 built classification androgen receptors 20 compounds 2 ),
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Machine Learning and Large Language Models for Modeling Complex Toxicity Pathways and Predicting Steroidogenesis

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