Last year, we were happy to contribute to a new book describing the use of both supervised and unsupervised machine learning approaches to support the prediction of toxicity and is illustrated with case studies related to regulatory submissions.
The book chapter emphasized the importance of high-quality databases, containing toxicology and chemistry information. Such comprehensive databases are critical for both the performance and acceptance of the results and require the use of ontologies and controlled vocabularies to harmonize and integrate the data from multiple sources in a meaningful manner.
Alongside this, the chapter highlighted the importance of coupling effective machine learning approaches with chemical descriptors to ensure any assessment is transparent and insights are actionable, including any expert review of the results.
Another aspect underscored was the importance that any predictive model is fit-for-purpose and its use and performance documented (such as how such models adhere to OECD validation principles).
This book is currently being finalized and will be released soon.
If you would like to discuss this topic in more detail then do get in touch with myself via (Glenn Myatt; firstname.lastname@example.org).