Guidance for good practice in the application of machine learning in development of toxicological quantitative structure-activity relationships (QSARs).

ABSTRACT: Recent years have seen a substantial growth in the adoption of machine learning approaches for the purposes of quantitative structure-activity relationship (QSAR) development. Such a trend has coincided with desire to see a shifting in the focus of methodology employed within chemical safety assessment: away from traditional reliance upon animal-intensive in vivo protocols, and towards increased application of in silico (or computational) predictive toxicology. With QSAR central amongst techniques applied in this area, the emergence of algorithms trained through machine learning with the objective of toxicity estimation has, quite naturally, arisen. On account of the pattern-recognition capabilities of the underlying methods, the statistical power of the ensuing models is potentially consi

SUBMITTER: Belfield SJ 

PROVIDER: S-EPMC10171609 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature