Moving towards making (quantitative) structure-activity relationships ((Q)SARs) for toxicity-related endpoints findable, accessible, interoperable and reusable (FAIR)
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Abstract
(Quantitative) structure-activity relationships ((Q)SARs) are widely used in chemical safety assessment to predict toxicological effects. Many thousands of (Q)SAR models have been developed and published, however, few are easily available to use. This investigation has applied previously developed Findability, Accessibility, Interoperability, and Reuse (FAIR) Principles for in silico models to six published, different, machine learning (ML) (Q)SARs for the same toxicity dataset (inhibition of growth to Tetrahymena pyriformis). The majority of principles were met, however, there are still gaps in making (Q)SARs FAIR. This study has enabled insights into, and recommendations for, the FAIRification of (Q)SARs including areas where more work and effort may be required. For instance, there is still a need for (Q)SARs to be associated with a unique identifier and full data / metadata for toxicological activity or endpoints, molecular properties and descriptors, as well as model description to be provided in a standardised manner. A number of solutions to the challenges were identified, such as building on the QSAR Model Reporting Format (QMRF) and the application of QSAR Assessment Framework (QAF). This study also demonstrated that resources such as the QSAR Databank (QsarDB, www.qsardb.org) are valuable in storing ML QSARs in a searchable database and also provide a Digital Object Identifier (DOI). Many activities related to FAIR are currently underway and (Q)SAR modellers should be encouraged to utilise these to move towards the easier access and use of models. Enabling FAIR computational toxicology models will support the overall progress towards animal free chemical safety assessment.
Plain language summary
This study relates to the availability of computational (termed in silico) models to predict the harmful effects of substances from a knowledge of chemical structure alone. The specific models referred to are (quantitative) structure-activity relationships ((Q)SARs, which have developed for many endpoints. Six machine learning models for toxicity were assessed against existing principles intended to make (Q)SARs findable, accessible, Interoperable and reusable (the FAIR principles). Evaluation of existing models against the FAIR principles highlighted a number of areas where progress is required to ensure the (Q)SARs are available for use. Currently there is no standard means to store (Q)SAR or provide a unique identifier, although the QSAR Databank (QsarDB) was illustrated as one possible solution. It is also crucial record the meta data associated with a model, such that it may be reproduced. A standardised ontology is required to facilitate the effective and accurate story of models and data.
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